1. A method of determining placement of one or more pressure sensors in a fluid distribution network, the method comprising: receiving, by a sensor placement determination system, one or more parameters associated with a fluid distribution network with plurality of nodes; recording, by the sensor placement determination system, pressure data associated with each of the plurality of nodes for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters, wherein the pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node; generating, by the sensor placement determination system, sensitivity matrix of the fluid distribution network for each of the plurality of demand values, based on the recorded pressure data, and the predefined amount of leak; grouping, by the sensor placement determination system, the plurality of nodes to form one or more clusters based on the sensitivity matrix, wherein each of the one or more clusters comprises nodes associated with similar sensitivity; and determining, by the sensor placement determination system, placement of one or more pressure sensors in the fluid distribution network, based on the grouping.

2. The method as claimed in claim 1, wherein each of the one or more pressure sensors is associated with a cluster from the one or more clusters.

3. The method as claimed in claim 2, wherein each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters.

4. The method as claimed in claim 3, wherein the node is at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster.

5. The method as claimed in claim 1, further comprising identifying one or more critical nodes from the plurality of nodes, for placement of pressure sensors at the one or more critical nodes, wherein the one or more critical nodes are associated with at least one of highest elevation and farthest distance from source, in the fluid distribution network.

6. A sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution network, the sensor placement determination system comprises: a processor; and a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to: receive one or more parameters associated with a fluid distribution network with plurality of nodes; record pressure data associated with each of the plurality of nodes for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters,, wherein the pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node; generate sensitivity matrix of the fluid distribution network for each of the plurality of demand values, based on the recorded pressure data and the predefined amount of leak; group the plurality of nodes to form one or more clusters based on the sensitivity matrix, wherein each of the one or more clusters comprises nodes associated with similar sensitivity; and determine placement of one or more pressure sensors in the fluid distribution network, based on the grouping.

7. The sensor placement determination system as claimed in claim 6, wherein each of the one or more pressure sensors is associated with a cluster from the one or more clusters.

8. The sensor placement determination system as claimed in claim 7, wherein each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters.

9. The sensor placement determination system as claimed in claim 8, wherein the node is at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster.

10. The sensor placement determination system as claimed in claim 6, further comprises the processor configured to identify one or more critical nodes from the plurality of nodes, for placement of pressure sensors at the one or more critical nodes, wherein the one or more critical nodes are associated with at least one of highest elevation and farthest distance from source, in the fluid distribution network. Dated this 6th Day of August, 2019 Swetha G N IN/PA-2847 of K & S Partners Agent for the Applicant , Description: TECHNICAL FIELD The present subject matter is related in general to fluid distribution networks, more particularly, but not exclusively to a method and a system for determining placement of pressure sensors in a fluid distribution network. BACKGROUND A water utility industry is made up of domestic entities responsible for safe and timely distribution of water and other related services, such as wastewater treatment. Most of water utilities around the world are under tremendous stress to prevent loss of treated water in the water network. Leakage leads to loss of water, as well as revenue for water utilities. Due to reducing water resources around the world, gap between demand and supply of water is widening. Also, leakage in-turn adversely affect said gap, by reducing the amount of supply available for consumer use. Leakage may affect water quality integrity of respective water distribution network by leading to contaminant intrusion into the water distribution network during low pressure conditions. Aging infrastructure, frequent pressure fluctuations, low structural strength at joints of the water distribution network, theft and so on, may leads to water loss from the water distribution network as leakage. Such problems may not be faced in water distribution networks but may also be faced in any fluid distribution network. In the present age of advanced sensing technologies, most of the water utilities are becoming open to the use of sensors for water network management. High cost incurred in extensive instrumentation of water networks may led to advancement in research in field of optimal sensor location determination. Leaks in water networks lead to change in flow and pressure in the distribution network. Hence, leaks in the distribution network may be identified by studying such signatures. Flow and pressures in a water network may be measured in real-time using flow meters and pressure sensors. Since cost of the flow meters are higher compared to pressure sensors, the flow meters are usually installed at inlet or outlets of the source of the distribution network or at district metered areas of the distribution network. Even though pressure sensors are cheaper compared to flow meters, installation of pressure sensors at all the nodes of the water network is not a feasible or an economical solution. The pressure sensors in the network are to be placed in such a way that, with a smaller number of sensors, maximum coverage of the network is achieved. Also, the pressures sensors are to be capable of measuring change in pressure due to leaks anywhere in the network. Some of the existing systems proposed techniques to determine optimal placement of sensors in the network. One of such existing systems may teach to use a calibrated hydraulic model to generate pressure sensitivity matrix of the network. Once the pressure sensitivity matrix is generated, k-means clustering may be used to cluster junctions in the network with similar leak pressure signals. The pressure sensors are placed at nodes which are closest to center of the cluster. However, such existing system does not consider uncertainty in consumer demands for estimating the placement of sensors in the network. With variation in the demand at nodes of the network, the sensitivity of the nodes may also vary. Without the consideration of uncertainty in demands, a placement of sensors in the network may not be accurate. The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art. SUMMARY In an embodiment, the present disclosure relates to a method of determining placement of one or more pressure sensors in a fluid distribution system. Initially, one or more parameters associated with the fluid distribution network with plurality of nodes are received. Pressure data associated with each of the plurality of nodes is recorded for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters. The pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node. A sensitivity matrix of the fluid distribution network is generated for each of the plurality of demand values, based on the recorded pressure data and the predefined amount of leak. Further, the plurality of nodes is grouped to form one or more clusters based on the sensitivity matrix. Each of the one or more clusters comprises nodes associated with similar sensitivity. The placement of the one or more pressure sensors in the fluid distribution network is determined based on the grouping of the plurality of nodes. In an embodiment, the present disclosure relates to a sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution system. The sensor placement determination system comprises a processor and a memory communicatively coupled to the processor. The memory stores processor-executable instructions, which, on execution, cause the processor to determine the placement of plurality of pressure sensors. Initially, one or more parameters associated with the fluid distribution network with plurality of nodes are received. Pressure data associated with each of the plurality of nodes is recorded for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters. The pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node. A sensitivity matrix of the fluid distribution network is generated for each of the plurality of demand values, based on the recorded pressure data and the predefined amount of leak. Further, the plurality of nodes is grouped to form one or more clusters based on the sensitivity matrix. Each of the one or more clusters comprises nodes associated with similar sensitivity. The placement of the one or more pressure sensors in the fluid distribution network is determined based on the grouping of the plurality of nodes. The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description. BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which: Figure 1 shows exemplary environment of a sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of the present disclosure; Figure 2 shows a detailed block diagram of sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of the present disclosure; Figure 3a-3e shows exemplary embodiments for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of the present disclosure; Figure 4 shows a flow diagram illustrating method for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of present disclosure; and Figure 5 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure. It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown. DETAILED DESCRIPTION In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments. While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure. The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method. The terms “includes”, “including”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “includes… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method. In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense. Present disclosure proposes system and method for determining placement of pressure sensors in a fluid distribution network. Number and location of the pressure sensors are determined to prevent leaks and for better pressure management in the fluid distribution network. The present disclosure teaches to consider possible values of demand at nodes of the fluid distribution network to understand sensitivity of the nodes. Based on the sensitivity, the nodes are grouped to form clusters and pressure sensor are placed based on the grouping. The present disclosure also teaches to identify critical node in the fluid distribution network, for the placement of the pressure sensors. Proposed method and system provision resilient and accurate placement of the pressure sensor even with demand uncertainty in the fluid distribution network. Also, the placement determined by the proposed disclosure provisions maximum coverage of the fluid distribution network with minimum number of the pressure sensors. Figure 1 shows an exemplary environment 100 of a sensor placement determination system 101. The sensor placement determination system 101 may be configured to determine placement of one or more pressure sensors in a fluid distribution network 102. The fluid distribution network 102 may be configured to provision distribution of fluid in a physical environment. The fluid may be a liquid or gas. In an embodiment, the fluid distribution network 102 may be a water distribution network. The fluid distribution network 102 also be configured to monitor distribution of the fluid in the physical environment. Source of the fluid distribution network 102 may be connected to consumers via plurality of interconnected branches and joints. Each of the consumers may be denoted as a node in the fluid distribution network 102. In an embodiment, the fluid distribution network 102 may include plurality of nodes with are interconnected with each other and to the source of the fluid distribution network 102. It may be necessary to monitor pressure and flow of the fluid at each of the plurality of nodes to monitor the distribution of the fluid. One or more sensors for measuring the pressure and the flow may be placed in the fluid distribution network 102 for monitoring. Also, it is necessary to determine an optimal placement of the one or more sensors, such that complete fluid distribution network 102 is covered for monitoring with minimal number of the one or more sensors. The sensor placement determination system 101 is configured to determine optimal placement of one or more pressure sensors in the fluid distribution network 102. The sensor placement determination system 101 may include a processor 104, I/O interface 105 and a memory 106. In some embodiments, the memory 106 may be communicatively coupled to the processor 104. The memory 106 stores instructions, executable by the processor 104, which on execution, may cause the sensor placement determination system 101 to determine the placement of the pressure sensors. In an embodiment, the memory 106 may include one or more modules 107 and data 108. The one or more modules 107 may be configured to perform the steps of the present disclosure using the data 108, to determine the placement of the pressure sensors as disclosed in the present disclosure. In an embodiment, each of the one or more modules 107 may be a hardware unit which may be outside the memory 1067 and coupled with the sensor placement determination system 101. The sensor placement determination system 101 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, e-book readers, a server, a network server, and the like. For determining the placement of the one or more pressure sensors, the sensor placement determination system 101 may be configured to receive one or more parameters associated with the fluid distribution network 102. In an embodiment, the one or more parameter may include information related to the fluid distribution network 102. For example, the one or more parameters may include model of the fluid distribution network 102, total number of the plurality of nodes in the fluid distribution network 102, interconnection/connectivity between the plurality of nodes, distance between each of the plurality of nodes, distance between the source and each of the plurality of nodes, fluid consumption data, network configuration data and so on. One or more other information relating to the fluid distribution network 102, which may be used by the sensor placement determination system 101 for determining the placement of the one or more pressure sensors, may be received as the one or more parameters. In an embodiment, the one or more parameters may be received and stored in the sensor placement determination system 101, during deployment of the sensor placement determination system 101. The stored one or more parameters may be used in real-time, when determining the placement of the one or more pressure sensors. In an embodiment, the sensor placement determination system 101 may be configured to dynamically receive the one or more parameters from the fluid distribution network 102, at the time of determining the placement of the one or more pressure sensors. Further, the sensor placement determination system 101 may be configured to record pressure data associated with each of the plurality of nodes. The pressure data may include value of pressure at a node. In an embodiment, the pressure data may include variation in value of the pressure at a node. One or more other information related to the pressure at a node may be recorded as the pressure data at that node. The pressure data may be recorded for predefined amount of leak at each of the plurality of nodes. In an embodiment, the predefined amount of leak may be simulated in a node from the plurality of nodes. For the predefined amount of leak at the node, the pressure data may be recorded at all the plurality of nodes, by the sensor placement determination system 101. The predefined amount of leak may be simulated at every node from the plurality of nodes and corresponding pressure data at all the plurality of nodes may be recorded. In an embodiment, the predefined amount of leak may be simulated using a simulation module. In an embodiment, the one or more parameters of the fluid distribution network 102, may be utilized in the simulation module of the fluid distribution network 102, to generate pressures values and flows values, with and without leak. Further, such pressure data is recorded in relation to each of plurality of demand values associated with the node. Each of the plurality of nodes in the fluid distribution system 102 may be associated with demands. In case the fluid distribution network 102 is huge, multiple consumers of the fluid distribution network 102 may be aggregated and indicated as a node, for the network skeletonization. The demands of the multiple consumers may also be aggregated, and cumulative demand may be included to be one of plurality of demand values of the node. The demands may not be certain. Such demands may affect pressure, flow and sensitivity of the plurality of nodes in the fluid distribution system 102. In an embodiment, maximum value and minimum value of the demand in the fluid distribution network 102 may be estimated or inferred using one or more techniques known to a person skilled in the art. In an embodiment, the maximum value and the minimum value of the demand at a node may be inferred using past consumption values associated with consumer at the node. Values of demand ranging from the maximum value and the minimum value may be considered to be the plurality of demand values. For each of the plurality of demand values, the pressure data may be recorded by the sensor placement determination system 101. Using said pressure data, the sensor placement determination system 101 may be configured to generate a sensitivity matrix of the fluid distribution network 102 for each of the plurality of demand values. The sensitivity matrix may be generated based on the recorded pressure data, and the predefined amount of leak. In an embodiment, the sensitivity matrix may be referred to as fault sensitivity matrix. The sensitivity matrix may indicate sensitivity of each of the plurality of nodes with the predefined amount of leak for varying demand values at each of the plurality of nodes. Further, the sensor placement determination system 101 may be configured to group the plurality of nodes to form one or more clusters. The plurality of nodes may be grouped or clustered based on the sensitivity matrix. Each of the one or more clusters comprises nodes associated with similar sensitivity. The sensor placement determination system 101 may determine the placement of one or more pressure sensors in the fluid distribution network 102 based on the grouping or the clustering. In an embodiment, each of the one or more pressure sensors is associated with a cluster from the one or more clusters. In an embodiment, each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters. In an embodiment, the node may be at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster. In an embodiment, the sensor placement determination system 101 may be configured to identify one or more critical nodes from the plurality of nodes, for placement of pressure sensors at the one or more critical nodes. In an embodiment, the one or more critical nodes may be associated with at least one of highest elevation and farthest distance from the source in the fluid distribution network 102. The sensor placement determination system 101 may be configured to determine placement of one or more pressure sensors at each of the one or more critical nodes. In an embodiment, the sensor placement determination system 101 may be a dedicated server or a cloud-based server in communication with the fluid distribution network 102. The sensor placement determination system 101 may communicate with the fluid distribution network 102 via a communication network 103. The communication network 103 may include, but is not limited to, a direct interconnection, , a Peer to Peer (P2P) network, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol), Controller Area Network (CAN), the Internet, Wi-Fi, and such. In an embodiment, the sensor placement determination system 101 may be associated with plurality of fluid distribution networks, to determine optimal placement of pressure sensors in each of the fluid distribution networks. In an embodiment, the sensor placement determination system 101 may communicate with each of the fluid distribution networks, via dedicated communication network. In an embodiment, the sensor placement determination system 101 may be integral part of the fluid distribution network 102. The I/O interface 105 of the sensor placement determination system 101 may assist in transmitting and receiving data. Received data may include, but is not limited to, the one or more parameters from the fluid distribution network 102. Transmitted data may include, but is not limited to, the determined placement of the one or more pressure sensors. One or more other data, which is associated with determination of the placement of the pressure sensors performed by the sensor placement determination system 101, may be received and transmitted via the I/O interface 105. Figure 2 shows a detailed block diagram of the sensor placement determination system 101 for determining placement of the one or more pressure sensors in the fluid distribution network 102, in accordance with some embodiments of the present disclosure. The data 108 and the one or more modules 107 in the memory 106 of the sensor placement determination system 101 may be described herein in detail. In one implementation, the one or more modules 107 may include, but are not limited to, a parameter reception module 201, a pressure data recordation module 202, a sensitivity matrix generation module 203, a nodes group module 204, placement determination module 205, critical node identification module 206 and one or more other modules 207, associated with the sensor placement determination system 101. In an embodiment, the data 108 in the memory 106 may include parameters 208 (also referred to as one or more parameters 208), pressure data 209, leak data 210 (also referred to as predefined amount of leak 210), demand data 211 (also referred to as plurality of demand values 211), sensitivity matrix data 212 (also referred to as sensitivity matrix 212), cluster data 213 (also referred to as one or more clusters 213), placement data 214, critical node data 215 (also referred to as one or more critical nodes 215) and other data 216 associated with the sensor placement determination system 101. In an embodiment, the data 108 in the memory 106 may be processed by the one or more modules 107 of the sensor placement determination system 101. In an embodiment, the one or more modules 107 may be implemented as dedicated units and when implemented in such a manner, said modules may be configured with the functionality defined in the present disclosure to result in a novel hardware. As used herein, the term module may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality. The sensor placement determination system 101 may be associated with the fluid distribution network 102 of any network configuration. Consider a fluid distribution network 300 illustrated in Figure 3a. The fluid distribution network 300 may include source 301 of fluid and plurality of nodes including first node 302.1, second node 302.2, third node 302.3, fourth node 302.4, fifth node 302.5, sixth node 302.6, seventh node 302.7 and eight node 302.8 (together referred to as the plurality of nodes 302.1….302.8). Figure 3a illustrates an exemplary representation of the fluid distribution network 102. The fluid distribution network 102 may include any number of plurality of nodes in any network configuration and connectivity. Consider the placement of the one or more pressure sensors in the fluid distribution network 300 is to be determined by the sensor placement determination system 101. The parameter reception module 201 of the sensor placement determination system 101 may be configured to receive the one or more parameters 208 associated with the fluid distribution network 300. In an embodiment, the one or more parameter 208 may be at least one of network configuration, network details, consumption data, connection details and so on, associated with the fluid distribution network 300. Further, the pressure data recordation module 202 of the sensor placement determination system 101 may be configured to record the pressure data 209 associated with each of the plurality of nodes 302.1….302.8. For recording the pressure data 209, the fluid distribution network 300 is run and the pressure data 209 at all the plurality of nodes 302.1….302.8 may be recorded. The one or more parameters of the fluid distribution network 102 may be used for recording the pressure data. Let Pn be the pressure at demand node n in a fluid distribution network 102, when there is no leak in the fluid distribution network 102. For the fluid distribution network 300 given in Figure 3a, normal pressures may be represented as equation (1), given below: P = [P1, P2 , P3 ··· P8] ………. (1) where, P is pressure recorded in the fluid distribution network 300 without a leak; and P1 is pressure recorded at the first node 302.1, P2 is pressure recorded at the second node 302.2, P3 is pressure recorded at the third node 302.3 and so on. Further, the pressure data recordation module 202 is configured to generate the predefined amount of leak 210 at any node from the plurality of nodes 302.1….302.8 and run the fluid distribution network 300. For example, as shown in Figure 3b, consider the predefined amount of leak 210 is simulated at the first node 302.1 in the fluid distribution network 300. Consider the predefined amount of leak 210 is L mg/liter. The pressure data 209 recoded at all the plurality of nodes 302.1….302.8 due to the predefined amount of leak 210 at the first node 302.1 may be representation as equation (2) given below: P^1:[P_1^1,P_2^1,P_3^1··· P_8^1 ] ………. (2) where, P^1 is recorded pressure in the fluid distribution network 300 for predefined amount of leak 210 in the first node 302.1; and P_1^1 is pressure at the first node 302.1 due to leak in the first node 302.1, P_2^1 is pressure at the second node 302.2 due to leak in the first node 302.1, P_3^1 is pressure at the third node 302.3 due to leak in the first node 302.1 and so on. In an embodiment, based on the pressure data 209 determined using equation (2), the sensitivity matrix generation module 203 of the sensor placement determination system 101 may be configured to generate a fault signature matrix for the fluid distribution network 300, in relation with the predefined amount of leak 210 of L mg/liter. Said fault signature matrix may be representation using equation (3), given below: F=[¦((P_1^1-P1)/L&(P_2^1-P2)/L&¦(?&(P_8^1-P8)/L)@(P_1^2-P1)/L&(P_2^2-P2)/L&¦(?&(P_8^2-P8)/L)@¦(?@(P_1^8-P1)/L)&¦(?@(P_1^8-P2)/L)&¦(?&¦(?@(P_8^8-P8)/L)))] ………. (3) It may be known to a person skilled in the art that in any fluid distribution network 102, the demands for fluid of the consumers is an uncertain parameter. The demand may be one of most sensitive parameters, since the change in demand of the fluid across the plurality of nodes leads to change in pressures across the fluid distribution network 102. The pressure data recordation module 202 may be configured to record the pressure data 209 in the plurality of nodes 302.1….302.8 based on uncertainty in demand. Consider that the demands at a node in the fluid distribution system 300 is uncertain. Probable range within which actual value of that demand fall may be considered for the simulation. Let M1 and M2 be upper limit and lower limit of the demand as shown equation (4) below: M1 < d < M2 ………. (4) For incorporating effects of the demand uncertainty in the fluid distribution network 300, the plurality of demand values 211 ranging between M1 and M2 may be considered. Let D be the ensemble of the plurality of demand values 211. D may be represented as equation (5) given below: D = [ d1 , d2 , ···. dn] ………. (5) such that, M1 < di ? i < M2 The plurality of demand values 211 may be considered while generating the fault signature matrix. For given plurality of demand values 211, the fault signature matrix may be represented as equation (6), given below: F_(d_i )=[¦((P_1^1-P1)/L&(P_2^1-P2)/L&¦(?&(P_8^1-P8)/L)@(P_1^2-P1)/L&(P_2^2-P2)/L&¦(?&(P_8^2-P8)/L)@¦(?@(P_1^8-P1)/L)&¦(?@(P_1^8-P2)/L)&¦(?&¦(?@(P_8^8-P8)/L)))]_(d_i ) ………. (6) Each element of the fault signature matrix may represent sensitivity of a node for the predefined amount of leak 210 and demand value of d_i at a node. For example, (P_1^1-P1)/L may represent sensitivity of the first node 302.1 due to the predefined amount of leak 210 in the first node 302.1 with demand of d_i. Similarly, (P_1^8-P2)/L may represent sensitivity of the eighth node 302.8 due to the predefined amount of leak 210 in the first node 302.1 with demand of d_i, (P_8^8-P8)/L may represent sensitivity of the eighth node 302.8 due to the predefined amount of leak 210 in the eighth node 302.8 with demand of d_i and so on. In an embodiment, the fault signature matrix as shown in equation (6) may be referred to the sensitivity matrix 212 and may also be represented as equation (7) given below: F_(d_i )=[¦(f_11&f_12&¦(?&f_18 )@f_21&f_22&¦(?&f_28 )@¦(?@f_81 )&¦(?@f_82 )&¦(?&¦(?@f_88 )))]_(d_i ) ………. (6) where, f11 indicates sensitivity at the first node 302.1 due to predefined amount of leak 210 at the first node 302.1, f12 indicates sensitivity at the first node 302.1 due to predefined amount of leak 210 at the second node 302.2 and so on. Using the sensitivity matrix 212, sensitivity associated with each of the plurality of nodes 302.1….302.8 due to leakage at each of other plurality of nodes may be identified. Figure 3c shows a plot of sensitivity of associated with each of the plurality of nodes 302.1….302.8 due to leakage at each of other plurality of nodes. X-axis of the plot indicates the plurality of nodes 302.1….302.8. Y-axis of the plot indicates sensitivity values. Sensitivity of each of the plurality of nodes 302.1….302.8 is plotted for predefined amount of leak 210 in each of the plurality of nodes 302.1….302.8. For example, at node 302.1 in the X-axis, the predefined amount of leak 210 is simulated at the node 302.1 and corresponding sensitivity of each of the plurality of nodes 302.1….302.8 is plotted. Similarly, sensitivity of the plurality of nodes 302.1….302.8 for the predefined amount of leak 210 at other nodes 302.2……302.8 is plotted. From the plot, it may be noticed that some of the plurality of nodes 302.1….302.8 exhibit similar sensitivity with the predefined amount of leak 210 in any node from the plurality of nodes 302.1….302.8. For example, when the predefined amount of leak 210 is simulated at the first node 302.1, the sensitivity of the first node 302.1, the second node 302.2 and the fourth node 302.4 are of closer value, the sensitivity of the firth node 302.5, the seventh node 302.7 and the eight node 302.8 are of closer value and the sensitivity of the sixth node 302.6 is closer to that of the third node 302.3. The same observation may be noticed for the predefined amount of leak 210 at other nodes as well. Based on the sensitivity, the nodes group module 204 may be configured to group the plurality of nodes 302.1….302.8 into the one or more clusters 213. In an embodiment, the nodes group module 204 may be configured to generate a plot similar to the plot in Figure 3c using the sensitivity matrix 212, to form the one or more clusters 213. For the given plot, the nodes group module 204 may group the plurality of nodes 302.1….302.8, as shown in Figure 3d, to form first cluster 303.1, second cluster 303.2 and third cluster 303.3. The first cluster 303.1 may include the first node 302.1, the second node 302.2 and the fourth node 302.4. The second cluster 303.2 may include the fifth node 302.5, the seventh node 302.7 and the eighth node 302.8. The third cluster 303.3 may include the sixth node 302.6 and the third node 302.3. Upon forming the one or more clusters 213, the placement determination module 205 may be configured to place the one or more pressures sensors, based on the one or more clusters 213. In an embodiment, one pressure sensor may be placed to cover one cluster from the one or more clusters 213. In an embodiment, the pressure sensor may be placed at a node which is at centre of the cluster or at nearest distance to the centre of the cluster. For the one or more clusters 303.1, 303.2 and 303.3, the one or more pressure sensors may be placed at each of the one or more clusters 303.1, 303.2 and 303.3. Consider, the first node 302.1 is the center of the first cluster 303.1, the eighth node 302.8 is at nearest distance to the centre of the second cluster 303.2 and the third node 302.3 is at nearest distance to the centre of the third cluster 303.3. Hence, the one or more pressure sensors may be determined to be placed at the first node 302.1, the eight node 302.8 and the third node 302.3 as shown in Figure 3e. In the fluid distribution network 300, a first pressure sensor 304.1 may be placed at the first node 302.1, a second pressure sensor 304.2 may be placed at the eight node 302.8 and a third pressure sensor 304.3 may be placed at the third node 302.3. In an embodiment, the placement of the one or more pressure sensors determined by the placement determination module 205, may be stored in the memory 106 as the placement data 214. For any fluid distribution network 102, pressures at the plurality of nodes should be always above a certain value in order to maintain adequate supply of the fluid. However, one or more critical nodes 215 may be identified in the fluid distribution network 102. Such one or more critical nodes 215 may be associated with minimum value of pressure. For efficient management of a fluid distribution network 102, the pressure at the one or more critical nodes 215 need to be monitored. The critical node identification module 206 of the sensor placement determination system 101 is configured to identify the one or more critical nodes 215 in the fluid distribution system 102. In an embodiment, the one or more critical nodes 215 may be identified to be at highest elevation in the fluid distribution network 102. In an embodiment, the one or more critical nodes 215 may be identified to be at farthest distance from the source in the fluid distribution network 102. Upon identifying the one or more critical nodes 215, the critical node identification module 206 may be configured to verify if the determined placement of the one or more pressure sensors includes placement pressure sensors at the one or more critical nodes 215. In case, the pressure sensors are not placed at the one or more critical nodes 215, the critical node identification module 206 may be configured to update the determined placement of the one or more pressure sensors to include the pressure sensors at the one or more critical nodes 215. Such pressure sensors at the one or more critical nodes 215 may be referred to additional candidate pressure sensors. Upon determining the placement of the one or more pressure sensors, output may be displayed to a user associated with the fluid distribution network 102. In an embodiment, the other modules 207 of the sensor placement determination system 101 may include a display unit to display the placement of the one or more pressure sensors in the model of the fluid distribution network 102. One or more other means, known to a person skilled in the art, may be used to display the placement of the one or more pressure sensors. In an embodiment, the displayed placement of the one or more pressure sensors may include location and number of the one or more pressure sensors in the fluid distribution network 102. The other data 216 may store data, including temporary data and temporary files, generated by modules for performing the various functions of the sensor placement determination system 101. The one or more modules 107 may also include other modules 207 to perform various miscellaneous functionalities of the sensor placement determination system 101. It will be appreciated that such modules may be represented as a single module or a combination of different modules. In an embodiment, the present disclosure may be implemented for leak detection in a fluid distribution network 102. Such application facilitates pressure measurements across the fluid distribution network 102. In presence of a leak at any node in the fluid distribution network 102, the pressure sensor network will detect the anomaly . In this way, utilities will be able to identify occurrence of leak and seek for filed surveys to locate and rectify the leak. In an embodiment, the present disclosure may be implemented for leak localization. In presence of a leak at a node in a fluid distribution network 102, pressure sensors near to the leak will record more deviation in pressures than the other pressure sensors. This way, fluid utilities might be able to identify the most affected area due to the leak, and thus reduce the spatial extend of the field leak survey to be conducted. Figure 4 shows a flow diagram illustrating method for determining placement of the one or more pressure sensors in the fluid distribution network 102, in accordance with some embodiments of present disclosure. At block 401, the parameter reception module 201 of the sensor placement determination system 101 may be configured to receive one or more parameters associated with the fluid distribution network 102. The one or more parameter may include information relating to the fluid distribution network 102. At block 402, the pressure data recordation module 202 of the sensor placement determination system 101 may be configured to record the pressure data 209 associated with each of the plurality of nodes for the predefined amount of leak 210 in each of the plurality of nodes. The pressure data 209 may be recorded using the one or more parameters associated with the fluid distribution network 102. The pressure data 209 of a node from the plurality of nodes is recorded in relation to each of the plurality of demand values associated with the node. In an embodiment, the plurality of demand values 211 may be range of demand values varying from the maximum value to the minimum value of the demand in the fluid distribution network 102. At block 403, the sensitivity matrix generation module 203 of the sensor placement determination system 101 may be configured to generate the sensitivity matrix 212 of the fluid distribution network 102 for each of the plurality of demand values 211. The sensitivity matrix 212 may be generated based on the recorded pressure data 209 and the predefined amount of leak 210. In an embodiment, the sensitivity matrix 212 may include values of sensitivities of each of the plurality of nodes in relation to the predefined amount of leak 210 and corresponding demand value. At block 404, the nodes group module 204 of the sensor placement determination system 101 may be configured to group the plurality of nodes to form the one or more clusters 213 based on the sensitivity matrix 212. Each of the one or more clusters 213 comprises nodes associated with similar sensitivity At block 405, the placement determination module 205 of the sensor placement determination system 101 may be configured to determine the placement of the one or more pressure sensors in the fluid distribution network 102, based on the grouping. In an embodiment, each of the one or more pressure sensors may be associated with a cluster from the one or more clusters 213. In an embodiment, each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters 213. In an embodiment, the node may be at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster. Methods illustrated in Figure 4 may include one or more blocks for executing processes in the sensor placement determination system 101. The methods illustrated in Figure 4 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types. The order in which the methods illustrated in Figure 4 are described may not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof. Computing System Figure 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 is used to implement the sensor placement determination system 101. The computer system 500 may include a central processing unit (“CPU” or “processor”) 502. The processor 502 may include at least one data processor for executing processes in Virtual Storage Area Network. The processor 502 may include specialized processing units such as, integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc. The processor 502 may be disposed in communication with one or more input/output (I/O) devices 509 and 510 via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc. Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices 509 and 510. For example, the input devices 509 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device/source, etc. The output devices 510 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma Display Panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc. In some embodiments, the computer system 500 may consist of the sensor placement determination system 101. The processor 502 may be disposed in communication with the communication network 511 via a network interface 503. The network interface 503 may communicate with the communication network 511. The network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 511 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface 503 and the communication network 511, the computer system 500 may communicate with the fluid distribution network 512, for determining placement of pressure sensors in the fluid distribution network 512. The network interface 503 may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 511 includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc. In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM, ROM, etc. not shown in Figure 5) via a storage interface 504. The storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as, serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc. The memory 505 may store a collection of program or database components, including, without limitation, user interface 506, an operating system 507, web browser 508 etc. In some embodiments, computer system 500 may store user/application data 506, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle ® or Sybase®. The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLE® IOSTM, GOOGLE® ANDROIDTM, BLACKBERRY® OS, or the like. In some embodiments, the computer system 500 may implement a web browser 508 stored program component. The web browser 508 may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers 508 may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 500 may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ActiveX, ANSI C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), Microsoft Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 500 may implement a mail client stored program component. The mail client may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, Mozilla Thunderbird, etc. Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media. Advantages An embodiment of the present disclosure considers uncertainty in consumption of the fluid. By which a more accurate configuration of pressure sensor during generation of pressure sensitivity network may be generated. Hence the present disclosure provisions resilient and accurate placement of the pressure sensor even with demand uncertainty in the fluid distribution network. An embodiment of the present disclosure provisions placement of pressure sensors with maximum coverage of the fluid distribution network with minimum number of the pressure sensors. The described operations may be implemented as a method, system or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof. The described operations may be implemented as code maintained in a “non-transitory computer readable medium”, where a processor may read and execute the code from the computer readable medium. The processor is at least one of a microprocessor and a processor capable of processing and executing the queries. A non-transitory computer readable medium may include media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, etc.), optical storage (CD-ROMs, DVDs, optical disks, etc.), volatile and non-volatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, etc.), etc. Further, non-transitory computer-readable media may include all computer-readable media except for a transitory. The code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), etc.). An “article of manufacture” includes non-transitory computer readable medium, and /or hardware logic, in which code may be implemented. A device in which the code implementing the described embodiments of operations is encoded may include a computer readable medium or hardware logic. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the invention, and that the article of manufacture may include suitable information bearing medium known in the art. The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise. The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise. The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise. The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise. A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention. When a single device or article is described herein, it will be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself. The illustrated operations of Figure 4 shows certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units. Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims. While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims. Referral numerals: Reference Number Description 101 Sensor placement determination system 102 Fluid distribution network 103 Communication network 104 Processor 105 I/O interface 106 Memory 107 Modules 108 Data 201 Parameter reception module 202 Pressure data recordation module 203 Sensitivity matrix generation module 204 Nodes group module 205 Placement determination module 206 Critical node identification module 207 Other modules 208 Parameters 209 Pressure data 210 Leak data 211 Demand data 212 Sensitivity matrix data 213 Cluster data 214 Placement data 215 Critical node data 216 Other data 300 Fluid distribution network 301 Source 302.1 First node 302.2 Second node 302.3 Third node 302.4 Fourth node 302.5 Fifth node 302.6 Sixth node 302.7 Seventh node 302.8 Eighth node 303.1 First cluster 303.2 Second cluster 303.3 Third cluster 304.1 First pressure sensor 304.2 Second pressure sensor 304.3 Third pressure sensor 500 Computer System 501 I/O Interface 502 Processor 503 Network Interface 504 Storage Interface 505 Memory 506 User Interface 507 Operating System 508 Web Browser 509 Input Devices 510 Output Devices 511 Communication Network 512 Fluid distribution network

Claims:We claim:
1. A method of determining placement of one or more pressure sensors in a fluid distribution network, the method comprising:
receiving, by a sensor placement determination system, one or more parameters associated with a fluid distribution network with plurality of nodes;
recording, by the sensor placement determination system, pressure data associated with each of the plurality of nodes for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters, wherein the pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node;
generating, by the sensor placement determination system, sensitivity matrix of the fluid distribution network for each of the plurality of demand values, based on the recorded pressure data, and the predefined amount of leak;
grouping, by the sensor placement determination system, the plurality of nodes to form one or more clusters based on the sensitivity matrix, wherein each of the one or more clusters comprises nodes associated with similar sensitivity; and
determining, by the sensor placement determination system, placement of one or more pressure sensors in the fluid distribution network, based on the grouping.

2. The method as claimed in claim 1, wherein each of the one or more pressure sensors is associated with a cluster from the one or more clusters.

3. The method as claimed in claim 2, wherein each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters.

4. The method as claimed in claim 3, wherein the node is at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster.

5. The method as claimed in claim 1, further comprising identifying one or more critical nodes from the plurality of nodes, for placement of pressure sensors at the one or more critical nodes, wherein the one or more critical nodes are associated with at least one of highest elevation and farthest distance from source, in the fluid distribution network.

6. A sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution network, the sensor placement determination system comprises:
a processor; and
a memory communicatively coupled to the processor, wherein the memory stores processor-executable instructions, which, on execution, cause the processor to:
receive one or more parameters associated with a fluid distribution network with plurality of nodes;
record pressure data associated with each of the plurality of nodes for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters,, wherein the pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node;
generate sensitivity matrix of the fluid distribution network for each of the plurality of demand values, based on the recorded pressure data and the predefined amount of leak;
group the plurality of nodes to form one or more clusters based on the sensitivity matrix, wherein each of the one or more clusters comprises nodes associated with similar sensitivity; and
determine placement of one or more pressure sensors in the fluid distribution network, based on the grouping.

7. The sensor placement determination system as claimed in claim 6, wherein each of the one or more pressure sensors is associated with a cluster from the one or more clusters.

8. The sensor placement determination system as claimed in claim 7, wherein each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters.

9. The sensor placement determination system as claimed in claim 8, wherein the node is at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster.

10. The sensor placement determination system as claimed in claim 6, further comprises the processor configured to identify one or more critical nodes from the plurality of nodes, for placement of pressure sensors at the one or more critical nodes, wherein the one or more critical nodes are associated with at least one of highest elevation and farthest distance from source, in the fluid distribution network.
Dated this 6th Day of August, 2019

Swetha G N
IN/PA-2847
of K & S Partners
Agent for the Applicant

, Description:
TECHNICAL FIELD
The present subject matter is related in general to fluid distribution networks, more particularly, but not exclusively to a method and a system for determining placement of pressure sensors in a fluid distribution network.

BACKGROUND
A water utility industry is made up of domestic entities responsible for safe and timely distribution of water and other related services, such as wastewater treatment. Most of water utilities around the world are under tremendous stress to prevent loss of treated water in the water network. Leakage leads to loss of water, as well as revenue for water utilities. Due to reducing water resources around the world, gap between demand and supply of water is widening. Also, leakage in-turn adversely affect said gap, by reducing the amount of supply available for consumer use. Leakage may affect water quality integrity of respective water distribution network by leading to contaminant intrusion into the water distribution network during low pressure conditions. Aging infrastructure, frequent pressure fluctuations, low structural strength at joints of the water distribution network, theft and so on, may leads to water loss from the water distribution network as leakage. Such problems may not be faced in water distribution networks but may also be faced in any fluid distribution network.

In the present age of advanced sensing technologies, most of the water utilities are becoming open to the use of sensors for water network management. High cost incurred in extensive instrumentation of water networks may led to advancement in research in field of optimal sensor location determination. Leaks in water networks lead to change in flow and pressure in the distribution network. Hence, leaks in the distribution network may be identified by studying such signatures. Flow and pressures in a water network may be measured in real-time using flow meters and pressure sensors. Since cost of the flow meters are higher compared to pressure sensors, the flow meters are usually installed at inlet or outlets of the source of the distribution network or at district metered areas of the distribution network. Even though pressure sensors are cheaper compared to flow meters, installation of pressure sensors at all the nodes of the water network is not a feasible or an economical solution. The pressure sensors in the network are to be placed in such a way that, with a smaller number of sensors, maximum coverage of the network is achieved. Also, the pressures sensors are to be capable of measuring change in pressure due to leaks anywhere in the network.
Some of the existing systems proposed techniques to determine optimal placement of sensors in the network. One of such existing systems may teach to use a calibrated hydraulic model to generate pressure sensitivity matrix of the network. Once the pressure sensitivity matrix is generated, k-means clustering may be used to cluster junctions in the network with similar leak pressure signals. The pressure sensors are placed at nodes which are closest to center of the cluster. However, such existing system does not consider uncertainty in consumer demands for estimating the placement of sensors in the network. With variation in the demand at nodes of the network, the sensitivity of the nodes may also vary. Without the consideration of uncertainty in demands, a placement of sensors in the network may not be accurate.

The information disclosed in this background of the disclosure section is only for enhancement of understanding of the general background of the invention and should not be taken as an acknowledgement or any form of suggestion that this information forms the prior art already known to a person skilled in the art.

SUMMARY
In an embodiment, the present disclosure relates to a method of determining placement of one or more pressure sensors in a fluid distribution system. Initially, one or more parameters associated with the fluid distribution network with plurality of nodes are received. Pressure data associated with each of the plurality of nodes is recorded for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters. The pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node. A sensitivity matrix of the fluid distribution network is generated for each of the plurality of demand values, based on the recorded pressure data and the predefined amount of leak. Further, the plurality of nodes is grouped to form one or more clusters based on the sensitivity matrix. Each of the one or more clusters comprises nodes associated with similar sensitivity. The placement of the one or more pressure sensors in the fluid distribution network is determined based on the grouping of the plurality of nodes.

In an embodiment, the present disclosure relates to a sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution system. The sensor placement determination system comprises a processor and a memory communicatively coupled to the processor. The memory stores processor-executable instructions, which, on execution, cause the processor to determine the placement of plurality of pressure sensors. Initially, one or more parameters associated with the fluid distribution network with plurality of nodes are received. Pressure data associated with each of the plurality of nodes is recorded for predefined amount of leak in each of the plurality of nodes, based on the one or more parameters. The pressure data of a node from the plurality of nodes is recorded in relation to each of plurality of demand values associated with the node. A sensitivity matrix of the fluid distribution network is generated for each of the plurality of demand values, based on the recorded pressure data and the predefined amount of leak. Further, the plurality of nodes is grouped to form one or more clusters based on the sensitivity matrix. Each of the one or more clusters comprises nodes associated with similar sensitivity. The placement of the one or more pressure sensors in the fluid distribution network is determined based on the grouping of the plurality of nodes.

The foregoing summary is illustrative only and is not intended to be in any way limiting. In addition to the illustrative aspects, embodiments, and features described above, further aspects, embodiments, and features will become apparent by reference to the drawings and the following detailed description.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the figures to reference like features and components. Some embodiments of system and/or methods in accordance with embodiments of the present subject matter are now described, by way of example only, and regarding the accompanying figures, in which:

Figure 1 shows exemplary environment of a sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of the present disclosure;

Figure 2 shows a detailed block diagram of sensor placement determination system for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of the present disclosure;

Figure 3a-3e shows exemplary embodiments for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of the present disclosure;

Figure 4 shows a flow diagram illustrating method for determining placement of one or more pressure sensors in a fluid distribution network, in accordance with some embodiments of present disclosure; and

Figure 5 illustrates a block diagram of an exemplary computer system for implementing embodiments consistent with the present disclosure.

It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative systems embodying the principles of the present subject matter. Similarly, it will be appreciated that any flow charts, flow diagrams, state transition diagrams, pseudo code, and the like represent various processes which may be substantially represented in computer readable medium and executed by a computer or processor, whether such computer or processor is explicitly shown.

DETAILED DESCRIPTION
In the present document, the word "exemplary" is used herein to mean "serving as an example, instance, or illustration." Any embodiment or implementation of the present subject matter described herein as "exemplary" is not necessarily to be construed as preferred or advantageous over other embodiments.

While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however that it is not intended to limit the disclosure to the forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternative falling within the spirit and the scope of the disclosure.

The terms “comprises”, “comprising”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

The terms “includes”, “including”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, device or method that includes a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or device or method. In other words, one or more elements in a system or apparatus proceeded by “includes… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or method.

In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings that form a part hereof, and in which are shown by way of illustration specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

Present disclosure proposes system and method for determining placement of pressure sensors in a fluid distribution network. Number and location of the pressure sensors are determined to prevent leaks and for better pressure management in the fluid distribution network. The present disclosure teaches to consider possible values of demand at nodes of the fluid distribution network to understand sensitivity of the nodes. Based on the sensitivity, the nodes are grouped to form clusters and pressure sensor are placed based on the grouping. The present disclosure also teaches to identify critical node in the fluid distribution network, for the placement of the pressure sensors. Proposed method and system provision resilient and accurate placement of the pressure sensor even with demand uncertainty in the fluid distribution network. Also, the placement determined by the proposed disclosure provisions maximum coverage of the fluid distribution network with minimum number of the pressure sensors.

Figure 1 shows an exemplary environment 100 of a sensor placement determination system 101. The sensor placement determination system 101 may be configured to determine placement of one or more pressure sensors in a fluid distribution network 102. The fluid distribution network 102 may be configured to provision distribution of fluid in a physical environment. The fluid may be a liquid or gas. In an embodiment, the fluid distribution network 102 may be a water distribution network. The fluid distribution network 102 also be configured to monitor distribution of the fluid in the physical environment. Source of the fluid distribution network 102 may be connected to consumers via plurality of interconnected branches and joints. Each of the consumers may be denoted as a node in the fluid distribution network 102. In an embodiment, the fluid distribution network 102 may include plurality of nodes with are interconnected with each other and to the source of the fluid distribution network 102. It may be necessary to monitor pressure and flow of the fluid at each of the plurality of nodes to monitor the distribution of the fluid. One or more sensors for measuring the pressure and the flow may be placed in the fluid distribution network 102 for monitoring. Also, it is necessary to determine an optimal placement of the one or more sensors, such that complete fluid distribution network 102 is covered for monitoring with minimal number of the one or more sensors. The sensor placement determination system 101 is configured to determine optimal placement of one or more pressure sensors in the fluid distribution network 102.

The sensor placement determination system 101 may include a processor 104, I/O interface 105 and a memory 106. In some embodiments, the memory 106 may be communicatively coupled to the processor 104. The memory 106 stores instructions, executable by the processor 104, which on execution, may cause the sensor placement determination system 101 to determine the placement of the pressure sensors. In an embodiment, the memory 106 may include one or more modules 107 and data 108. The one or more modules 107 may be configured to perform the steps of the present disclosure using the data 108, to determine the placement of the pressure sensors as disclosed in the present disclosure. In an embodiment, each of the one or more modules 107 may be a hardware unit which may be outside the memory 1067 and coupled with the sensor placement determination system 101. The sensor placement determination system 101 may be implemented in a variety of computing systems, such as a laptop computer, a desktop computer, a Personal Computer (PC), a notebook, a smartphone, a tablet, e-book readers, a server, a network server, and the like.

For determining the placement of the one or more pressure sensors, the sensor placement determination system 101 may be configured to receive one or more parameters associated with the fluid distribution network 102. In an embodiment, the one or more parameter may include information related to the fluid distribution network 102. For example, the one or more parameters may include model of the fluid distribution network 102, total number of the plurality of nodes in the fluid distribution network 102, interconnection/connectivity between the plurality of nodes, distance between each of the plurality of nodes, distance between the source and each of the plurality of nodes, fluid consumption data, network configuration data and so on. One or more other information relating to the fluid distribution network 102, which may be used by the sensor placement determination system 101 for determining the placement of the one or more pressure sensors, may be received as the one or more parameters. In an embodiment, the one or more parameters may be received and stored in the sensor placement determination system 101, during deployment of the sensor placement determination system 101. The stored one or more parameters may be used in real-time, when determining the placement of the one or more pressure sensors. In an embodiment, the sensor placement determination system 101 may be configured to dynamically receive the one or more parameters from the fluid distribution network 102, at the time of determining the placement of the one or more pressure sensors.

Further, the sensor placement determination system 101 may be configured to record pressure data associated with each of the plurality of nodes. The pressure data may include value of pressure at a node. In an embodiment, the pressure data may include variation in value of the pressure at a node. One or more other information related to the pressure at a node may be recorded as the pressure data at that node. The pressure data may be recorded for predefined amount of leak at each of the plurality of nodes. In an embodiment, the predefined amount of leak may be simulated in a node from the plurality of nodes. For the predefined amount of leak at the node, the pressure data may be recorded at all the plurality of nodes, by the sensor placement determination system 101. The predefined amount of leak may be simulated at every node from the plurality of nodes and corresponding pressure data at all the plurality of nodes may be recorded. In an embodiment, the predefined amount of leak may be simulated using a simulation module. In an embodiment, the one or more parameters of the fluid distribution network 102, may be utilized in the simulation module of the fluid distribution network 102, to generate pressures values and flows values, with and without leak. Further, such pressure data is recorded in relation to each of plurality of demand values associated with the node. Each of the plurality of nodes in the fluid distribution system 102 may be associated with demands. In case the fluid distribution network 102 is huge, multiple consumers of the fluid distribution network 102 may be aggregated and indicated as a node, for the network skeletonization. The demands of the multiple consumers may also be aggregated, and cumulative demand may be included to be one of plurality of demand values of the node.
The demands may not be certain. Such demands may affect pressure, flow and sensitivity of the plurality of nodes in the fluid distribution system 102. In an embodiment, maximum value and minimum value of the demand in the fluid distribution network 102 may be estimated or inferred using one or more techniques known to a person skilled in the art. In an embodiment, the maximum value and the minimum value of the demand at a node may be inferred using past consumption values associated with consumer at the node. Values of demand ranging from the maximum value and the minimum value may be considered to be the plurality of demand values. For each of the plurality of demand values, the pressure data may be recorded by the sensor placement determination system 101.

Using said pressure data, the sensor placement determination system 101 may be configured to generate a sensitivity matrix of the fluid distribution network 102 for each of the plurality of demand values. The sensitivity matrix may be generated based on the recorded pressure data, and the predefined amount of leak. In an embodiment, the sensitivity matrix may be referred to as fault sensitivity matrix. The sensitivity matrix may indicate sensitivity of each of the plurality of nodes with the predefined amount of leak for varying demand values at each of the plurality of nodes.

Further, the sensor placement determination system 101 may be configured to group the plurality of nodes to form one or more clusters. The plurality of nodes may be grouped or clustered based on the sensitivity matrix. Each of the one or more clusters comprises nodes associated with similar sensitivity. The sensor placement determination system 101 may determine the placement of one or more pressure sensors in the fluid distribution network 102 based on the grouping or the clustering. In an embodiment, each of the one or more pressure sensors is associated with a cluster from the one or more clusters. In an embodiment, each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters. In an embodiment, the node may be at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster.

In an embodiment, the sensor placement determination system 101 may be configured to identify one or more critical nodes from the plurality of nodes, for placement of pressure sensors at the one or more critical nodes. In an embodiment, the one or more critical nodes may be associated with at least one of highest elevation and farthest distance from the source in the fluid distribution network 102. The sensor placement determination system 101 may be configured to determine placement of one or more pressure sensors at each of the one or more critical nodes.

In an embodiment, the sensor placement determination system 101 may be a dedicated server or a cloud-based server in communication with the fluid distribution network 102. The sensor placement determination system 101 may communicate with the fluid distribution network 102 via a communication network 103. The communication network 103 may include, but is not limited to, a direct interconnection, , a Peer to Peer (P2P) network, Local Area Network (LAN), Wide Area Network (WAN), wireless network (e.g., using Wireless Application Protocol), Controller Area Network (CAN), the Internet, Wi-Fi, and such. In an embodiment, the sensor placement determination system 101 may be associated with plurality of fluid distribution networks, to determine optimal placement of pressure sensors in each of the fluid distribution networks. In an embodiment, the sensor placement determination system 101 may communicate with each of the fluid distribution networks, via dedicated communication network. In an embodiment, the sensor placement determination system 101 may be integral part of the fluid distribution network 102. The I/O interface 105 of the sensor placement determination system 101 may assist in transmitting and receiving data. Received data may include, but is not limited to, the one or more parameters from the fluid distribution network 102. Transmitted data may include, but is not limited to, the determined placement of the one or more pressure sensors. One or more other data, which is associated with determination of the placement of the pressure sensors performed by the sensor placement determination system 101, may be received and transmitted via the I/O interface 105.

Figure 2 shows a detailed block diagram of the sensor placement determination system 101 for determining placement of the one or more pressure sensors in the fluid distribution network 102, in accordance with some embodiments of the present disclosure.

The data 108 and the one or more modules 107 in the memory 106 of the sensor placement determination system 101 may be described herein in detail.

In one implementation, the one or more modules 107 may include, but are not limited to, a parameter reception module 201, a pressure data recordation module 202, a sensitivity matrix generation module 203, a nodes group module 204, placement determination module 205, critical node identification module 206 and one or more other modules 207, associated with the sensor placement determination system 101.
In an embodiment, the data 108 in the memory 106 may include parameters 208 (also referred to as one or more parameters 208), pressure data 209, leak data 210 (also referred to as predefined amount of leak 210), demand data 211 (also referred to as plurality of demand values 211), sensitivity matrix data 212 (also referred to as sensitivity matrix 212), cluster data 213 (also referred to as one or more clusters 213), placement data 214, critical node data 215 (also referred to as one or more critical nodes 215) and other data 216 associated with the sensor placement determination system 101.

In an embodiment, the data 108 in the memory 106 may be processed by the one or more modules 107 of the sensor placement determination system 101. In an embodiment, the one or more modules 107 may be implemented as dedicated units and when implemented in such a manner, said modules may be configured with the functionality defined in the present disclosure to result in a novel hardware. As used herein, the term module may refer to an Application Specific Integrated Circuit (ASIC), an electronic circuit, a Field-Programmable Gate Arrays (FPGA), Programmable System-on-Chip (PSoC), a combinational logic circuit, and/or other suitable components that provide the described functionality.

The sensor placement determination system 101 may be associated with the fluid distribution network 102 of any network configuration. Consider a fluid distribution network 300 illustrated in Figure 3a. The fluid distribution network 300 may include source 301 of fluid and plurality of nodes including first node 302.1, second node 302.2, third node 302.3, fourth node 302.4, fifth node 302.5, sixth node 302.6, seventh node 302.7 and eight node 302.8 (together referred to as the plurality of nodes 302.1….302.8). Figure 3a illustrates an exemplary representation of the fluid distribution network 102. The fluid distribution network 102 may include any number of plurality of nodes in any network configuration and connectivity. Consider the placement of the one or more pressure sensors in the fluid distribution network 300 is to be determined by the sensor placement determination system 101. The parameter reception module 201 of the sensor placement determination system 101 may be configured to receive the one or more parameters 208 associated with the fluid distribution network 300. In an embodiment, the one or more parameter 208 may be at least one of network configuration, network details, consumption data, connection details and so on, associated with the fluid distribution network 300.

Further, the pressure data recordation module 202 of the sensor placement determination system 101 may be configured to record the pressure data 209 associated with each of the plurality of nodes 302.1….302.8. For recording the pressure data 209, the fluid distribution network 300 is run and the pressure data 209 at all the plurality of nodes 302.1….302.8 may be recorded. The one or more parameters of the fluid distribution network 102 may be used for recording the pressure data. Let Pn be the pressure at demand node n in a fluid distribution network 102, when there is no leak in the fluid distribution network 102. For the fluid distribution network 300 given in Figure 3a, normal pressures may be represented as equation (1), given below:

P = [P1, P2 , P3 ··· P8] ………. (1)

where, P is pressure recorded in the fluid distribution network 300 without a leak; and
P1 is pressure recorded at the first node 302.1, P2 is pressure recorded at the second node 302.2, P3 is pressure recorded at the third node 302.3 and so on.

Further, the pressure data recordation module 202 is configured to generate the predefined amount of leak 210 at any node from the plurality of nodes 302.1….302.8 and run the fluid distribution network 300. For example, as shown in Figure 3b, consider the predefined amount of leak 210 is simulated at the first node 302.1 in the fluid distribution network 300. Consider the predefined amount of leak 210 is L mg/liter. The pressure data 209 recoded at all the plurality of nodes 302.1….302.8 due to the predefined amount of leak 210 at the first node 302.1 may be representation as equation (2) given below:

P^1:[P_1^1,P_2^1,P_3^1··· P_8^1 ] ………. (2)
where, P^1 is recorded pressure in the fluid distribution network 300 for predefined amount of leak 210 in the first node 302.1; and
P_1^1 is pressure at the first node 302.1 due to leak in the first node 302.1, P_2^1 is pressure at the second node 302.2 due to leak in the first node 302.1, P_3^1 is pressure at the third node 302.3 due to leak in the first node 302.1 and so on.

In an embodiment, based on the pressure data 209 determined using equation (2), the sensitivity matrix generation module 203 of the sensor placement determination system 101 may be configured to generate a fault signature matrix for the fluid distribution network 300, in relation with the predefined amount of leak 210 of L mg/liter. Said fault signature matrix may be representation using equation (3), given below:

F=[¦((P_1^1-P1)/L&(P_2^1-P2)/L&¦(?&(P_8^1-P8)/L)@(P_1^2-P1)/L&(P_2^2-P2)/L&¦(?&(P_8^2-P8)/L)@¦(?@(P_1^8-P1)/L)&¦(?@(P_1^8-P2)/L)&¦(?&¦(?@(P_8^8-P8)/L)))] ………. (3)

It may be known to a person skilled in the art that in any fluid distribution network 102, the demands for fluid of the consumers is an uncertain parameter. The demand may be one of most sensitive parameters, since the change in demand of the fluid across the plurality of nodes leads to change in pressures across the fluid distribution network 102. The pressure data recordation module 202 may be configured to record the pressure data 209 in the plurality of nodes 302.1….302.8 based on uncertainty in demand. Consider that the demands at a node in the fluid distribution system 300 is uncertain. Probable range within which actual value of that demand fall may be considered for the simulation. Let M1 and M2 be upper limit and lower limit of the demand as shown equation (4) below:
M1 < d < M2 ………. (4)

For incorporating effects of the demand uncertainty in the fluid distribution network 300, the plurality of demand values 211 ranging between M1 and M2 may be considered. Let D be the ensemble of the plurality of demand values 211. D may be represented as equation (5) given below:
D = [ d1 , d2 , ···. dn] ………. (5)

such that, M1 < di ? i < M2

The plurality of demand values 211 may be considered while generating the fault signature matrix. For given plurality of demand values 211, the fault signature matrix may be represented as equation (6), given below:

F_(d_i )=[¦((P_1^1-P1)/L&(P_2^1-P2)/L&¦(?&(P_8^1-P8)/L)@(P_1^2-P1)/L&(P_2^2-P2)/L&¦(?&(P_8^2-P8)/L)@¦(?@(P_1^8-P1)/L)&¦(?@(P_1^8-P2)/L)&¦(?&¦(?@(P_8^8-P8)/L)))]_(d_i ) ………. (6)
Each element of the fault signature matrix may represent sensitivity of a node for the predefined amount of leak 210 and demand value of d_i at a node. For example, (P_1^1-P1)/L may represent sensitivity of the first node 302.1 due to the predefined amount of leak 210 in the first node 302.1 with demand of d_i. Similarly, (P_1^8-P2)/L may represent sensitivity of the eighth node 302.8 due to the predefined amount of leak 210 in the first node 302.1 with demand of d_i, (P_8^8-P8)/L may represent sensitivity of the eighth node 302.8 due to the predefined amount of leak 210 in the eighth node 302.8 with demand of d_i and so on.

In an embodiment, the fault signature matrix as shown in equation (6) may be referred to the sensitivity matrix 212 and may also be represented as equation (7) given below:
F_(d_i )=[¦(f_11&f_12&¦(?&f_18 )@f_21&f_22&¦(?&f_28 )@¦(?@f_81 )&¦(?@f_82 )&¦(?&¦(?@f_88 )))]_(d_i ) ………. (6)

where, f11 indicates sensitivity at the first node 302.1 due to predefined amount of leak 210 at the first node 302.1, f12 indicates sensitivity at the first node 302.1 due to predefined amount of leak 210 at the second node 302.2 and so on.

Using the sensitivity matrix 212, sensitivity associated with each of the plurality of nodes 302.1….302.8 due to leakage at each of other plurality of nodes may be identified. Figure 3c shows a plot of sensitivity of associated with each of the plurality of nodes 302.1….302.8 due to leakage at each of other plurality of nodes. X-axis of the plot indicates the plurality of nodes 302.1….302.8. Y-axis of the plot indicates sensitivity values. Sensitivity of each of the plurality of nodes 302.1….302.8 is plotted for predefined amount of leak 210 in each of the plurality of nodes 302.1….302.8. For example, at node 302.1 in the X-axis, the predefined amount of leak 210 is simulated at the node 302.1 and corresponding sensitivity of each of the plurality of nodes 302.1….302.8 is plotted. Similarly, sensitivity of the plurality of nodes 302.1….302.8 for the predefined amount of leak 210 at other nodes 302.2……302.8 is plotted. From the plot, it may be noticed that some of the plurality of nodes 302.1….302.8 exhibit similar sensitivity with the predefined amount of leak 210 in any node from the plurality of nodes 302.1….302.8. For example, when the predefined amount of leak 210 is simulated at the first node 302.1, the sensitivity of the first node 302.1, the second node 302.2 and the fourth node 302.4 are of closer value, the sensitivity of the firth node 302.5, the seventh node 302.7 and the eight node 302.8 are of closer value and the sensitivity of the sixth node 302.6 is closer to that of the third node 302.3. The same observation may be noticed for the predefined amount of leak 210 at other nodes as well. Based on the sensitivity, the nodes group module 204 may be configured to group the plurality of nodes 302.1….302.8 into the one or more clusters 213. In an embodiment, the nodes group module 204 may be configured to generate a plot similar to the plot in Figure 3c using the sensitivity matrix 212, to form the one or more clusters 213. For the given plot, the nodes group module 204 may group the plurality of nodes 302.1….302.8, as shown in Figure 3d, to form first cluster 303.1, second cluster 303.2 and third cluster 303.3. The first cluster 303.1 may include the first node 302.1, the second node 302.2 and the fourth node 302.4. The second cluster 303.2 may include the fifth node 302.5, the seventh node 302.7 and the eighth node 302.8. The third cluster 303.3 may include the sixth node 302.6 and the third node 302.3.

Upon forming the one or more clusters 213, the placement determination module 205 may be configured to place the one or more pressures sensors, based on the one or more clusters 213. In an embodiment, one pressure sensor may be placed to cover one cluster from the one or more clusters 213. In an embodiment, the pressure sensor may be placed at a node which is at centre of the cluster or at nearest distance to the centre of the cluster. For the one or more clusters 303.1, 303.2 and 303.3, the one or more pressure sensors may be placed at each of the one or more clusters 303.1, 303.2 and 303.3. Consider, the first node 302.1 is the center of the first cluster 303.1, the eighth node 302.8 is at nearest distance to the centre of the second cluster 303.2 and the third node 302.3 is at nearest distance to the centre of the third cluster 303.3. Hence, the one or more pressure sensors may be determined to be placed at the first node 302.1, the eight node 302.8 and the third node 302.3 as shown in Figure 3e. In the fluid distribution network 300, a first pressure sensor 304.1 may be placed at the first node 302.1, a second pressure sensor 304.2 may be placed at the eight node 302.8 and a third pressure sensor 304.3 may be placed at the third node 302.3. In an embodiment, the placement of the one or more pressure sensors determined by the placement determination module 205, may be stored in the memory 106 as the placement data 214.

For any fluid distribution network 102, pressures at the plurality of nodes should be always above a certain value in order to maintain adequate supply of the fluid. However, one or more critical nodes 215 may be identified in the fluid distribution network 102. Such one or more critical nodes 215 may be associated with minimum value of pressure. For efficient management of a fluid distribution network 102, the pressure at the one or more critical nodes 215 need to be monitored. The critical node identification module 206 of the sensor placement determination system 101 is configured to identify the one or more critical nodes 215 in the fluid distribution system 102. In an embodiment, the one or more critical nodes 215 may be identified to be at highest elevation in the fluid distribution network 102. In an embodiment, the one or more critical nodes 215 may be identified to be at farthest distance from the source in the fluid distribution network 102. Upon identifying the one or more critical nodes 215, the critical node identification module 206 may be configured to verify if the determined placement of the one or more pressure sensors includes placement pressure sensors at the one or more critical nodes 215. In case, the pressure sensors are not placed at the one or more critical nodes 215, the critical node identification module 206 may be configured to update the determined placement of the one or more pressure sensors to include the pressure sensors at the one or more critical nodes 215. Such pressure sensors at the one or more critical nodes 215 may be referred to additional candidate pressure sensors.

Upon determining the placement of the one or more pressure sensors, output may be displayed to a user associated with the fluid distribution network 102. In an embodiment, the other modules 207 of the sensor placement determination system 101 may include a display unit to display the placement of the one or more pressure sensors in the model of the fluid distribution network 102. One or more other means, known to a person skilled in the art, may be used to display the placement of the one or more pressure sensors. In an embodiment, the displayed placement of the one or more pressure sensors may include location and number of the one or more pressure sensors in the fluid distribution network 102.

The other data 216 may store data, including temporary data and temporary files, generated by modules for performing the various functions of the sensor placement determination system 101. The one or more modules 107 may also include other modules 207 to perform various miscellaneous functionalities of the sensor placement determination system 101. It will be appreciated that such modules may be represented as a single module or a combination of different modules.

In an embodiment, the present disclosure may be implemented for leak detection in a fluid distribution network 102. Such application facilitates pressure measurements across the fluid distribution network 102. In presence of a leak at any node in the fluid distribution network 102, the pressure sensor network will detect the anomaly . In this way, utilities will be able to identify occurrence of leak and seek for filed surveys to locate and rectify the leak.

In an embodiment, the present disclosure may be implemented for leak localization. In presence of a leak at a node in a fluid distribution network 102, pressure sensors near to the leak will record more deviation in pressures than the other pressure sensors. This way, fluid utilities might be able to identify the most affected area due to the leak, and thus reduce the spatial extend of the field leak survey to be conducted.

Figure 4 shows a flow diagram illustrating method for determining placement of the one or more pressure sensors in the fluid distribution network 102, in accordance with some embodiments of present disclosure.

At block 401, the parameter reception module 201 of the sensor placement determination system 101 may be configured to receive one or more parameters associated with the fluid distribution network 102. The one or more parameter may include information relating to the fluid distribution network 102.

At block 402, the pressure data recordation module 202 of the sensor placement determination system 101 may be configured to record the pressure data 209 associated with each of the plurality of nodes for the predefined amount of leak 210 in each of the plurality of nodes. The pressure data 209 may be recorded using the one or more parameters associated with the fluid distribution network 102. The pressure data 209 of a node from the plurality of nodes is recorded in relation to each of the plurality of demand values associated with the node. In an embodiment, the plurality of demand values 211 may be range of demand values varying from the maximum value to the minimum value of the demand in the fluid distribution network 102.

At block 403, the sensitivity matrix generation module 203 of the sensor placement determination system 101 may be configured to generate the sensitivity matrix 212 of the fluid distribution network 102 for each of the plurality of demand values 211. The sensitivity matrix 212 may be generated based on the recorded pressure data 209 and the predefined amount of leak 210. In an embodiment, the sensitivity matrix 212 may include values of sensitivities of each of the plurality of nodes in relation to the predefined amount of leak 210 and corresponding demand value.

At block 404, the nodes group module 204 of the sensor placement determination system 101 may be configured to group the plurality of nodes to form the one or more clusters 213 based on the sensitivity matrix 212. Each of the one or more clusters 213 comprises nodes associated with similar sensitivity

At block 405, the placement determination module 205 of the sensor placement determination system 101 may be configured to determine the placement of the one or more pressure sensors in the fluid distribution network 102, based on the grouping. In an embodiment, each of the one or more pressure sensors may be associated with a cluster from the one or more clusters 213. In an embodiment, each of the one or more pressure sensors is placed at a node associated with corresponding cluster from the one or more clusters 213. In an embodiment, the node may be at one of center of the corresponding cluster and nearest distance from the center of the corresponding cluster.

Methods illustrated in Figure 4 may include one or more blocks for executing processes in the sensor placement determination system 101. The methods illustrated in Figure 4 may be described in the general context of computer executable instructions. Generally, computer executable instructions can include routines, programs, objects, components, data structures, procedures, modules, and functions, which perform particular functions or implement particular abstract data types.

The order in which the methods illustrated in Figure 4 are described may not intended to be construed as a limitation, and any number of the described method blocks can be combined in any order to implement the method. Additionally, individual blocks may be deleted from the methods without departing from the scope of the subject matter described herein. Furthermore, the method can be implemented in any suitable hardware, software, firmware, or combination thereof.

Computing System
Figure 5 illustrates a block diagram of an exemplary computer system 500 for implementing embodiments consistent with the present disclosure. In an embodiment, the computer system 500 is used to implement the sensor placement determination system 101. The computer system 500 may include a central processing unit (“CPU” or “processor”) 502. The processor 502 may include at least one data processor for executing processes in Virtual Storage Area Network. The processor 502 may include specialized processing units such as, integrated system (bus) controllers, memory management control units, floating point units, graphics processing units, digital signal processing units, etc.

The processor 502 may be disposed in communication with one or more input/output (I/O) devices 509 and 510 via I/O interface 501. The I/O interface 501 may employ communication protocols/methods such as, without limitation, audio, analog, digital, monaural, RCA, stereo, IEEE-1394, serial bus, universal serial bus (USB), infrared, PS/2, BNC, coaxial, component, composite, digital visual interface (DVI), high-definition multimedia interface (HDMI), RF antennas, S-Video, VGA, IEEE 802.n /b/g/n/x, Bluetooth, cellular (e.g., code-division multiple access (CDMA), high-speed packet access (HSPA+), global system for mobile communications (GSM), long-term evolution (LTE), WiMax, or the like), etc.

Using the I/O interface 501, the computer system 500 may communicate with one or more I/O devices 509 and 510. For example, the input devices 509 may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, stylus, scanner, storage device, transceiver, video device/source, etc. The output devices 510 may be a printer, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light-emitting diode (LED), plasma, Plasma Display Panel (PDP), Organic light-emitting diode display (OLED) or the like), audio speaker, etc.

In some embodiments, the computer system 500 may consist of the sensor placement determination system 101. The processor 502 may be disposed in communication with the communication network 511 via a network interface 503. The network interface 503 may communicate with the communication network 511. The network interface 503 may employ connection protocols including, without limitation, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc. The communication network 511 may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, etc. Using the network interface 503 and the communication network 511, the computer system 500 may communicate with the fluid distribution network 512, for determining placement of pressure sensors in the fluid distribution network 512. The network interface 503 may employ connection protocols include, but not limited to, direct connect, Ethernet (e.g., twisted pair 10/100/1000 Base T), transmission control protocol/internet protocol (TCP/IP), token ring, IEEE 802.11a/b/g/n/x, etc.

The communication network 511 includes, but is not limited to, a direct interconnection, an e-commerce network, a peer to peer (P2P) network, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), the Internet, Wi-Fi, and such. The first network and the second network may either be a dedicated network or a shared network, which represents an association of the different types of networks that use a variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), etc., to communicate with each other. Further, the first network and the second network may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, etc.

In some embodiments, the processor 502 may be disposed in communication with a memory 505 (e.g., RAM, ROM, etc. not shown in Figure 5) via a storage interface 504. The storage interface 504 may connect to memory 505 including, without limitation, memory drives, removable disc drives, etc., employing connection protocols such as, serial advanced technology attachment (SATA), Integrated Drive Electronics (IDE), IEEE-1394, Universal Serial Bus (USB), fibre channel, Small Computer Systems Interface (SCSI), etc. The memory drives may further include a drum, magnetic disc drive, magneto-optical drive, optical drive, Redundant Array of Independent Discs (RAID), solid-state memory devices, solid-state drives, etc.

The memory 505 may store a collection of program or database components, including, without limitation, user interface 506, an operating system 507, web browser 508 etc. In some embodiments, computer system 500 may store user/application data 506, such as, the data, variables, records, etc., as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle ® or Sybase®.

The operating system 507 may facilitate resource management and operation of the computer system 500. Examples of operating systems include, without limitation, APPLE MACINTOSH® OS X, UNIX®, UNIX-like system distributions (E.G., BERKELEY SOFTWARE DISTRIBUTIONTM (BSD), FREEBSDTM, NETBSDTM, OPENBSDTM, etc.), LINUX DISTRIBUTIONSTM (E.G., RED HATTM, UBUNTUTM, KUBUNTUTM, etc.), IBMTM OS/2, MICROSOFTTM WINDOWSTM (XPTM, VISTATM/7/8, 10 etc.), APPLE® IOSTM, GOOGLE® ANDROIDTM, BLACKBERRY® OS, or the like.

In some embodiments, the computer system 500 may implement a web browser 508 stored program component. The web browser 508 may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, etc. Secure web browsing may be provided using Secure Hypertext Transport Protocol (HTTPS), Secure Sockets Layer (SSL), Transport Layer Security (TLS), etc. Web browsers 508 may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, Application Programming Interfaces (APIs), etc. In some embodiments, the computer system 500 may implement a mail server stored program component. The mail server may be an Internet mail server such as Microsoft Exchange, or the like. The mail server may utilize facilities such as ASP, ActiveX, ANSI C++/C#, Microsoft .NET, CGI scripts, Java, JavaScript, PERL, PHP, Python, WebObjects, etc. The mail server may utilize communication protocols such as Internet Message Access Protocol (IMAP), Messaging Application Programming Interface (MAPI), Microsoft Exchange, Post Office Protocol (POP), Simple Mail Transfer Protocol (SMTP), or the like. In some embodiments, the computer system 500 may implement a mail client stored program component. The mail client may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, Mozilla Thunderbird, etc.

Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present disclosure. A computer-readable storage medium refers to any type of physical memory on which information or data readable by a processor may be stored. Thus, a computer-readable storage medium may store instructions for execution by one or more processors, including instructions for causing the processor(s) to perform steps or stages consistent with the embodiments described herein. The term “computer-readable medium” should be understood to include tangible items and exclude carrier waves and transient signals, i.e., be non-transitory. Examples include Random Access Memory (RAM), Read-Only Memory (ROM), volatile memory, non-volatile memory, hard drives, CD ROMs, DVDs, flash drives, disks, and any other known physical storage media.

Advantages
An embodiment of the present disclosure considers uncertainty in consumption of the fluid. By which a more accurate configuration of pressure sensor during generation of pressure sensitivity network may be generated. Hence the present disclosure provisions resilient and accurate placement of the pressure sensor even with demand uncertainty in the fluid distribution network.

An embodiment of the present disclosure provisions placement of pressure sensors with maximum coverage of the fluid distribution network with minimum number of the pressure sensors.

The described operations may be implemented as a method, system or article of manufacture using standard programming and/or engineering techniques to produce software, firmware, hardware, or any combination thereof. The described operations may be implemented as code maintained in a “non-transitory computer readable medium”, where a processor may read and execute the code from the computer readable medium. The processor is at least one of a microprocessor and a processor capable of processing and executing the queries. A non-transitory computer readable medium may include media such as magnetic storage medium (e.g., hard disk drives, floppy disks, tape, etc.), optical storage (CD-ROMs, DVDs, optical disks, etc.), volatile and non-volatile memory devices (e.g., EEPROMs, ROMs, PROMs, RAMs, DRAMs, SRAMs, Flash Memory, firmware, programmable logic, etc.), etc. Further, non-transitory computer-readable media may include all computer-readable media except for a transitory. The code implementing the described operations may further be implemented in hardware logic (e.g., an integrated circuit chip, Programmable Gate Array (PGA), Application Specific Integrated Circuit (ASIC), etc.).

An “article of manufacture” includes non-transitory computer readable medium, and /or hardware logic, in which code may be implemented. A device in which the code implementing the described embodiments of operations is encoded may include a computer readable medium or hardware logic. Of course, those skilled in the art will recognize that many modifications may be made to this configuration without departing from the scope of the invention, and that the article of manufacture may include suitable information bearing medium known in the art.
The terms “an embodiment”, “embodiment”, “embodiments”, “the embodiment”, “the embodiments”, “one or more embodiments”, “some embodiments”, and “one embodiment” mean “one or more (but not all) embodiments of the invention(s)” unless expressly specified otherwise.

The terms “including”, “comprising”, “having” and variations thereof mean “including but not limited to”, unless expressly specified otherwise.

The enumerated listing of items does not imply that any or all of the items are mutually exclusive, unless expressly specified otherwise.

The terms “a”, “an” and “the” mean “one or more”, unless expressly specified otherwise.

A description of an embodiment with several components in communication with each other does not imply that all such components are required. On the contrary a variety of optional components are described to illustrate the wide variety of possible embodiments of the invention.

When a single device or article is described herein, it will be readily apparent that more than one device/article (whether or not they cooperate) may be used in place of a single device/article. Similarly, where more than one device or article is described herein (whether or not they cooperate), it will be readily apparent that a single device/article may be used in place of the more than one device or article or a different number of devices/articles may be used instead of the shown number of devices or programs. The functionality and/or the features of a device may be alternatively embodied by one or more other devices which are not explicitly described as having such functionality/features. Thus, other embodiments of the invention need not include the device itself.

The illustrated operations of Figure 4 shows certain events occurring in a certain order. In alternative embodiments, certain operations may be performed in a different order, modified, or removed. Moreover, steps may be added to the above described logic and still conform to the described embodiments. Further, operations described herein may occur sequentially or certain operations may be processed in parallel. Yet further, operations may be performed by a single processing unit or by distributed processing units.

Finally, the language used in the specification has been principally selected for readability and instructional purposes, and it may not have been selected to delineate or circumscribe the inventive subject matter. It is therefore intended that the scope of the invention be limited not by this detailed description, but rather by any claims that issue on an application based here on. Accordingly, the disclosure of the embodiments of the invention is intended to be illustrative, but not limiting, of the scope of the invention, which is set forth in the following claims.

While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for purposes of illustration and are not intended to be limiting, with the true scope and spirit being indicated by the following claims.

Referral numerals:
Reference Number Description
101 Sensor placement determination system
102 Fluid distribution network
103 Communication network
104 Processor
105 I/O interface
106 Memory
107 Modules
108 Data
201 Parameter reception module
202 Pressure data recordation module
203 Sensitivity matrix generation module
204 Nodes group module
205 Placement determination module
206 Critical node identification module
207 Other modules
208 Parameters
209 Pressure data
210 Leak data
211 Demand data
212 Sensitivity matrix data
213 Cluster data
214 Placement data
215 Critical node data
216 Other data
300 Fluid distribution network
301 Source
302.1 First node
302.2 Second node
302.3 Third node
302.4 Fourth node
302.5 Fifth node
302.6 Sixth node
302.7 Seventh node
302.8 Eighth node
303.1 First cluster
303.2 Second cluster
303.3 Third cluster
304.1 First pressure sensor
304.2 Second pressure sensor
304.3 Third pressure sensor
500 Computer System
501 I/O Interface
502 Processor
503 Network Interface
504 Storage Interface
505 Memory
506 User Interface
507 Operating System
508 Web Browser
509 Input Devices
510 Output Devices
511 Communication Network
512 Fluid distribution network