DESC:FORM 2
THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENT RULES, 2003

COMPLETE SPECIFICATION
(See Section 10 and Rule 13)

Title of Invention:
A METHOD AND SYSTEM FOR NETWORK PLANNING

APPLICANT:
PROBUS SMART THINGS PRIVATE LIMITED
An Indian entity having the address as:
63, IIIrd Floor, DSIDC Complex, Phase-I, Okhla Industrial Area, New Delhi - 110020

The following specification particularly describes the invention and the manner in which it is to be performed.

CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY
[0001] The present application claims priority from the Indian provisional patent application, having application number 202411036810, filed on 09th May 2024, incorporated herein by reference.
TECHNICAL FIELD
[0002] The presently disclosed embodiments are related to the field of smart meter operations. More particularly, the present invention relates to a system for planning optimal placement of one or more telemetry units within a smart meter network, introducing innovations to optimize the deployment and communication technology for ensuring optimal performance and efficiency in the realm of utility metering and management.
BACKGROUND
[0003] This section is intended to introduce the reader to various aspects of art (the relevant technical field or area of knowledge to which the invention pertains), which may be related to various aspects of the present disclosure that are described or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements in this background section are to be read in this light, and not as admissions of prior art. Similarly, a problem mentioned in the background section.
[0004] In the landscape of modern energy infrastructure, the integration of smart meters indicates a new era of data-driven management and efficiency. However, the optimal functioning of these smart meter networks relies heavily on the strategic placement of one or more telemetry units. Further, these one or more telemetry units serve as central hubs for aggregating data from individual meters, enabling utilities to monitor and manage energy consumption effectively. Yet, the challenge lies in determining the most efficient locations for deploying one or more telemetry units within the smart meter network. Further, the problem of optimal telemetry units placement stems from the complex interplay of various factors, including geographic topology, population density, and communication range. In densely populated urban areas, for instance, the concentration of meters may necessitate a higher density of one or more telemetry units to ensure adequate coverage and data collection. Conversely, in rural or remote regions, sparse meter distribution poses challenges in achieving comprehensive network connectivity, requiring careful consideration of placement strategies to minimize gaps in coverage.
[0005] Moreover, the dynamic nature of energy demand and consumption patterns further complicates the task of placing one or more telemetry units. Peak usage periods, fluctuating demand profiles, and evolving consumer behaviours all influence the optimal positioning of one or more telemetry units to maximize data collection efficiency and responsiveness. Failure to address these dynamic factors can result in suboptimal placement decisions, leading to inefficiencies in resource allocation, inaccurate billing, and compromised grid reliability. Ultimately, the challenge of planning optimal telemetry units placement within smart meter networks underscores the need for sophisticated modelling and optimization techniques. By leveraging advanced analytics, geographic information systems (GIS), and predictive algorithms, utilities can develop data-driven strategies to identify the most advantageous locations for the one or more telemetry units deployment. Such approaches not only enhance the performance and reliability of smart meter networks but also pave the way for more intelligent and sustainable energy management practices.
[0006] The emergence of smart meters promised transformative shifts towards efficiency and sustainability. However, a critical challenge emerges when smart meters fall outside the range of one or more telemetry units, thus failing to form a cohesive cluster for data aggregation. This problem stems from the inherent limitations of communication range, hindering the seamless integration of these meters into the larger grid infrastructure. The failure to include smart meters beyond the reach of one or more telemetry units poses significant repercussions for energy monitoring and management. Without a comprehensive network, crucial data regarding consumption patterns, peak usage hours, and potential inefficiencies remain uncollected and underutilized. This not only impedes the ability of utility providers to optimize resources and anticipate demand but also deprives consumers of valuable insights to make informed decisions about their energy usage.
[0007] Moreover, the exclusion of smart meters from the clustering process exacerbates disparities in energy distribution and pricing, as areas with insufficient coverage may be subject to inaccurate billing and inefficient resource allocation. From an operational standpoint, this fragmentation undermines the overarching goal of a seamlessly interconnected grid, thwarting efforts to transition towards a smarter, more sustainable energy ecosystem.
[0008] The issue of data latency also poses a significant problem for smart meter networks lacking optimal telemetry units placement. As energy consumption patterns fluctuate, the delay in data transmission can result in outdated or inaccurate readings that fail to reflect real-time usage. This can hinder the ability of utilities to respond to demand spikes or implement demand-response strategies effectively. Moreover, prolonged latency can diminish consumer trust in the accuracy of their bills, as discrepancies may arise between actual usage and reported figures, leading to potential dissatisfaction and disengagement from energy-saving initiatives.
[0009] Additionally, the geographic diversity across regions introduces another layer of complexity to the telemetry units placement dilemma. Urban environments often feature high-density meter populations but may also face interference from urban infrastructure, while rural areas may have sparse meter distributions requiring long-distance communication. The variability in terrain can further affect signal propagation and the effectiveness of communication between meters and one or more telemetry units. These geographic and infrastructural differences must be carefully evaluated to prevent coverage gaps that could lead to unreliable data collection and inefficiencies in energy management.
[0010] In view of the above, addressing these challenges requires innovative solutions to extend the reach of one or more telemetry units or alternative methods of data collection, ensuring that no smart meter is left unaccounted for in the tracking of efficient energy management.
[0011] Further limitations and disadvantages of conventional and traditional approaches will become apparent to one of skill in the art, through comparison of the described systems with some aspects of the present disclosure, as set forth in the remainder of the present application and with reference to the drawings.
SUMMARY
[0012] This summary is provided to introduce concepts related to a method and system for network planning, and the concepts are further described below in the detailed description. This summary is not intended to identify essential features of the claimed subject matter, nor is it intended for use in determining or limiting the scope of the claimed subject matter.
[0013] According to embodiments illustrated herein, a method for network planning is disclosed. Further, the method may comprise identifying one or more coordinate positions of one or more devices. Further, the method may comprise generating one or more clusters based on the one or more coordinate positions and a configurable node distance. Further, the method may comprise estimating a number of one or more telemetry units required for each of the one or more clusters. Further, the method may comprise identifying a position of each of the one or more telemetry units using a clustering technique. An initial position of each of the number of one or more telemetry units is initially assigned within each of the one or more clusters. Further, the method may comprise providing the position of the each of the one or more telemetry units to a network planning tool.
[0014] According to embodiments illustrated herein, a system for network planning is disclosed. Further, the system may comprise a memory and a processor. Further, the processor may be configured to execute programmed instructions stored in the memory. Further, the system may comprise one or more devices, one or more telemetry units and a network planning tool. Further, the system may comprise a network planning tool by utilising the memory, and the processor is configured to identify the one or more coordinate positions of the one or more devices. Further, the network planning tool may generate the one or more clusters based on the one or more coordinate positions and the configurable node distance. Further, the network planning tool may estimate the number of the one or more telemetry units required for each of the one or more clusters. Further, the network planning tool may identify the position of each of the one or more telemetry units using the clustering technique. The initial position of each of the number of the one or more telemetry units is initially assigned within each of the one or more clusters. Further, the network planning tool may provide the position of the each of the one or more telemetry units to the network planning tool.
[0015] According to embodiments illustrated herein, a non-transitory computer-readable storage medium for network planning is disclosed. The non-transitory computer-readable storage medium having stored thereon a set of computer-executable instructions causing a computer comprising a processor to perform steps. The step may comprise identifying the one or more coordinate positions of the one or more devices. Further, the step may comprise generating the one or more clusters based on the one or more coordinate positions and the configurable node distance. Further, the step may comprise estimating the number of one or more telemetry units required for each of the one or more clusters. Further, the step may comprise identifying the position of each of the one or more telemetry units using the clustering technique. The initial position of each of the number of one or more telemetry units is initially assigned within each of the one or more clusters. Further, the step may comprise providing the position of the each of the one or more telemetry units to the network planning tool.
[0016] 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 DRAWINGS
[0017] The accompanying drawings illustrate the various embodiments of systems, methods, and other aspects of the disclosure. Any person with ordinary skills in art will appreciate that the illustrated element boundaries (e.g., boxes, groups of boxes, or other shapes) in the figures represent one example of the boundaries. In some examples, one element may be designed as multiple elements, or multiple elements may be designed as one element. In some examples, an element shown as an internal component of one element may be implemented as an external component in another, and vice versa. Further, the elements may not be drawn to scale.
[0018] Various embodiments will hereinafter be described in accordance with the appended drawings, which are provided to illustrate and not to limit the scope in any manner, wherein similar designations denote similar elements, and in which:
[0019] FIG. 1 is a block diagram that illustrates a system (100) for network planning in accordance with an embodiment of the present subject matter.
[0020] FIG. 2 is a block diagram (200) that illustrates various components of an application server (104) configured for the network planning, in accordance with an embodiment of the present subject matter.
[0021] FIGS. 3, 3.A, 3.B, 3.C is a block diagram (300) of network communication for one or more devices (303) connected to one or more telemetry units (301) coupled through Radio Frequency (RF) mesh connectivity, in accordance with an embodiment of the present subject matter.
[0022] FIG. 4 is a flowchart that illustrates a method (400) for the network planning, in accordance with an embodiment of the present subject matter.
[0023] FIG. 5 illustrates a block diagram (500) of an exemplary computer system for implementing embodiments consistent with the present subject matter.
DETAILED DESCRIPTION
[0024] The present disclosure may be best understood with reference to the detailed figures and description set forth herein. Various embodiments are discussed below with reference to the figures. However, those skilled in the art will readily appreciate that the detailed descriptions given herein with respect to the figures are simply for explanatory purposes as the methods and systems may extend beyond the described embodiments. For example, the teachings presented, and the needs of a particular application may yield multiple alternative and suitable approaches to implement the functionality of any detail described herein. Therefore, any approach may extend beyond the particular implementation choices in the following embodiments described and shown.
[0025] References to “one embodiment,” “at least one embodiment,” “an embodiment,” “one example,” “an example,” “for example,” and so on indicate that the embodiment(s) or example(s) may include a particular feature, structure, characteristic, property, element, or limitation but that not every embodiment or example necessarily includes that particular feature, structure, characteristic, property, element, or limitation. Further, repeated use of the phrase “in an embodiment” does not necessarily refer to the same embodiment. The terms “comprise”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, system 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 system or method. In other words, one or more elements in a system or apparatus preceded by “comprises… a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.
[0026] The terminology “one or more devices”, “smart meter system”, “smart meters”, “utility meters”, “one or more smart meters”, “plurality of smart meters” “, intelligent meters” has the same meaning and are used alternatively throughout the specification.
[0027] The terminology “cluster”, “clusters”, “one or more clusters” has the same meaning and are used alternatively throughout the specification.
[0028] The terminology “mesh connectivity”, “mesh network” has the same meaning and are used alternatively throughout the specification.
[0029] The terminology “RF schema”, “RF connectivity”, “Radio Frequency schema” “radio frequency connectivity” has the same meaning and are used alternatively throughout the specification.
[0030] The objective of the present disclosure is to create a method that improves the aggregation of data from one or more devices by strategically placing telemetry units based on geographic factors.
[0031] Another objective of the present disclosure is to implement density-based clustering that adapts to the changing patterns of energy consumption, ensuring telemetry units are positioned to respond to peak usage times effectively.
[0032] Another objective of the present disclosure is to calculate the precise number of telemetry units needed for each cluster, facilitating efficient use of resources in both urban and rural settings.
[0033] Another objective of the present disclosure is to minimize gaps in communication range by identifying optimal telemetry units locations that ensure all smart meters are included within the network.
[0034] Yet another objective of the present disclosure is to design a user-friendly interface that allows utility providers to visualize and modify telemetry units placement based on real-time data and analytics.
[0035] Yet another objective of the present disclosure is to enhance the accuracy of energy billing by ensuring that all smart meters are within the communication range of telemetry units.
[0036] Yet another objective of the present disclosure is to leverage predictive algorithms that forecast energy consumption trends, assisting in proactive telemetry units placement.
[0037] Yet another objective of the present invention is to provide utilities with data-driven strategies that promote sustainable energy management and reduce environmental impacts.
[0038] Yet another objective of the present invention is to ensure the method is scalable, allowing for adjustments in telemetry units placement as one or more devices grow and evolve over time.
[0039] Yet another objective of the present invention is to identify and relocate non-optimally positioned telemetry units to the optimal position.
[0040] FIG. 1 is a block diagram that illustrates a system (100) for network planning, in accordance with an embodiment of the present subject matter. The system (100) typically includes a database server (102), an application server (104), a communication network (106), and one or more portable devices (108). The database server (102), the application server (104), and the one or more portable devices (108) are typically communicatively coupled with each other via the communication network (106). In an embodiment, the application server (104) may communicate with the database server (102), and the one or more portable devices (108) using one or more protocols such as, but not limited to, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), RF mesh, Bluetooth Low Energy (BLE), and the like, to communicate with one another.
[0041] In one embodiment, the database server (102) may refer to a computing device that may be configured to store one or more devices (303) data, one or more telemetry units (301) data, one or more node variables, final centroid locations for the one or more telemetry units (301), process for generating one or more clusters, process of calculating number of one or more telemetry units (301) and other intermediate processing data.
[0042] In an embodiment, the database server (102) may include a special purpose operating system specifically configured to perform one or more database operations on the stored content. Examples of database operations may include, but are not limited to, Select, Insert, Update, and Delete. In an embodiment, the database server (102) may include hardware that may be configured to perform one or more predetermined operations. In an embodiment, the database server (102) may be realized through various technologies such as, but not limited to, Microsoft® SQL Server, Oracle®, IBM DB2®, Microsoft Access®, PostgreSQL®, MySQL®, SQLite®, distributed database technology and the like. In an embodiment, the database server (102) may be configured to utilize the application server (104) for implementing the method for network planning.
[0043] A person with ordinary skills in art will understand that the scope of the disclosure is not limited to the database server (102) as a separate entity. In an embodiment, the functionalities of the database server (102) can be integrated into the application server (104) or into the one or more portable devices (108).
[0044] In an embodiment, the application server (104) may refer to a computing device or a software framework hosting an application or a software service. In an embodiment, the application server (104) may be implemented to execute procedures such as, but not limited to, programs, routines, or scripts stored in one or more memories for supporting the hosted application or the software service. In an embodiment, the hosted application or the software service may be configured to perform one or more predetermined operations. The application server (104) may be realized through various types of application servers, such as, but are not limited to, a Java application server, a .NET framework application server, a Base4 application server, a PHP framework application server, or any other application server framework.
[0045] In an embodiment, the application server (104) may be configured to utilize the database server (102) and the one or more portable devices (108), in conjunction, for implementing the method for network planning. In an implementation, the application server (104) corresponds to an infrastructure for implementing the method for network planning.
[0046] In an embodiment, the communication network (106) may correspond to a communication medium through which the application server (104), the database server (102), and the one or more portable devices (108) may communicate with each other. Such a communication may be performed in accordance with various wired and wireless communication protocols. Examples of such wired and wireless communication protocols include, but are not limited to, Transmission Control Protocol and Internet Protocol (TCP/IP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), Wireless Application Protocol (WAP), File Transfer Protocol (FTP), ZigBee, EDGE, infrared IR), IEEE 802.11, 802.16, 2G, 3G, 4G, 5G, 6G, 7G cellular communication protocols, and/or Bluetooth (BT) communication protocols. The communication network (106) may either be a dedicated network or a shared network. Further, the communication network (106) may include a variety of network devices, including routers, bridges, servers, computing devices, storage devices, and the like. The communication network (106) may include, but is not limited to, the Internet, intranet, a cloud network, a Wireless Fidelity (Wi-Fi) network, a Wireless Local Area Network (WLAN), a Local Area Network (LAN), a cable network, the wireless network, a telephone network (e.g., Analog, Digital, POTS, PSTN, ISDN, xDSL), a telephone line (POTS), a Metropolitan Area Network (MAN), an electronic positioning network, an X.25 network, an optical network (e.g., PON), a satellite network (e.g., VSAT), a packet-switched network, a circuit-switched network, a public network, a private network, and/or other wired or wireless communications network configured to carry data.
[0047] In an embodiment, the one or more portable devices (108) may refer to a computing device used by a user. The one or more portable devices (108) may comprise one or more processors and one or more memory. The one or more memories may include computer readable code that may be executable by one or more processors to perform predetermined operations. In an embodiment, the one or more portable devices (108) may present a web user interface for network planning using the application server (104). Example web user interfaces are presented on the one or more portable devices (108) to display the placement of one or more telemetry units (301) within one or more devices. Examples of the one or more portable devices (108) may include, but are not limited to, a personal computer, a laptop, a computer desktop, a personal digital assistant (PDA), a mobile device, a tablet, or any other computing device.
[0048] The system (100) can be implemented using hardware, software, or a combination of both, which includes using, where suitable, one or more computer programs, mobile applications, or “apps” by deploying either on-premises over the corresponding computing terminals or virtually over cloud infrastructure. The system (100) may include various micro-services or groups of independent computer programs which can act independently in collaboration with other micro-services. The system (100) may also interact with a third-party or external computer system. A critical attribute of the system (100) is that it can concurrently and instantly perform network planning.
[0049] FIG. 2 illustrates a block (200) diagram illustrating various components of the application server (104) configured for performing stepwise planning placement of one or more telemetry units (301) within one or more devices, in accordance with an embodiment of the present subject matter. Further, FIG. 2 is explained in conjunction with elements from FIG. 1. Here, the application server (104) preferably includes a processor (202), a memory (204), a transceiver (206), an input/output (I/O) unit (208), a user interface unit (210), a network planning unit (212) and a clustering unit (214). The processor (202) is further preferably communicatively coupled to the memory (204), the transceiver (206), the input/output (I/O) unit (208), the user interface unit (210), the network planning unit (212) and the clustering unit (214), while the transceiver (206) is preferably communicatively coupled to the communication network (106).
[0050] The processor (202) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to execute a set of instructions stored in the memory (204), and may be implemented based on several processor technologies known in the art. The processor (202) works in coordination with the transceiver (206), the input/output (I/O) unit (208), the user interface unit (210), the network planning unit (212), and the clustering unit (214) for network planning. Examples of the processor (202) include, but not limited to, standard microprocessor, microcontroller, central processing unit (CPU), an X86-based processor, a Reduced Instruction Set Computing (RISC) processor, an Application- Specific Integrated Circuit (ASIC) processor, and a Complex Instruction Set Computing (CISC) processor, distributed or cloud processing unit, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions and/or other processing logic that accommodates the requirements of the present invention.
[0051] The memory (204) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to store the set of instructions, which are executed by the processor (202). Preferably, the memory (204) is configured to store one or more programs, routines, or scripts that are executed in coordination with the processor (202). Additionally, the memory (204) may include any computer-readable medium or computer program product known in the art including, for example, volatile memory, such as static random-access memory (SRAM) and dynamic random-access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, a Hard Disk Drive (HDD), flash memories, Secure Digital (SD) card, Solid State Disks (SSD), optical disks, magnetic tapes, memory cards, virtual memory and distributed cloud storage. The memory (204) may be removable, non-removable, or a combination thereof. Further, the memory (204) may include routines, programs, objects, components, data structures, etc., which perform particular tasks or implement particular abstract data types. The memory (204) may include programs or coded instructions that supplement applications and functions of the system (100). In one embodiment, the memory (204), amongst other things, serves as a repository for storing data processed, received, and generated by one or more of the programs or the coded instructions. In yet another embodiment, the memory (204) may be managed under a federated structure that enables adaptability and responsiveness of the application server (104).
[0052] The transceiver (206) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to receive, process or transmit information, data or signals, which are stored by the memory (204) and executed by the processor (202). The transceiver (206) is preferably configured to receive, process or transmit one or more programs, routines, or scripts that are executed in coordination with the processor (202). The transceiver (206) is preferably communicatively coupled to the communication network (106) of the system (100) for communicating all the information, data, signal, programs, routines or scripts through the network.
[0053] The transceiver (206) may implement one or more known technologies to support wired or wireless communication with the communication network (106). In an embodiment, the transceiver (206) may include, but is not limited to, an antenna, a radio frequency (RF) transceiver, one or more amplifiers, a tuner, one or more oscillators, a digital signal processor, a Universal Serial Bus (USB) device, a coder-decoder (CODEC) chipset, a subscriber identity module (SIM) card, and/or a local buffer. Also, the transceiver (206) may communicate via wireless communication with networks, such as the Internet, an Intranet and/or a wireless network, such as a cellular telephone network, a wireless local area network (LAN) and/or a metropolitan area network (MAN). Accordingly, the wireless communication may use any of a plurality of communication standards, protocols and technologies, such as: Global System for Mobile Communications (GSM), Enhanced Data GSM Environment (EDGE), wideband code division multiple access (W-CDMA), code division multiple access (CDMA), time division multiple access (TDMA), Bluetooth, Wireless Fidelity (Wi-Fi) (e.g., IEEE 802.11a, IEEE 802.11b, IEEE 802.11g and/or IEEE 802.11n), voice over Internet Protocol (VoIP), Wi-MAX, a protocol for email, instant messaging, and/or Short Message Service (SMS).
[0054] The input/output (I/O) unit (208) comprises suitable logic, circuitry, interfaces, and/or code that may be configured to receive or present information. The input/output (I/O) unit (208) comprises various input and output devices that are configured to communicate with the processor (202). Examples of the input devices include, but are not limited to, a keyboard, a mouse, a joystick, a touch screen, a microphone, a camera, and/or a docking station. Examples of the output devices include, but are not limited to, a display screen and/or a speaker. The input/output (I/O) unit (208) may include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like. The input/output (I/O) unit (208) may allow the system (100) to interact with the user directly or through the portable devices (108). Further, the input/output (I/O) unit (208) may enable the system (100) to communicate with other computing devices, such as web servers and external data servers (not shown). The input/output (I/O) unit (208) can facilitate multiple communications within a wide variety of networks and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. The user input/output (I/O) unit (208) may include one or more ports for connecting a number of devices to one another or to another server. In one embodiment, the input/output (I/O) unit (208) allows the application server (104) to be logically coupled to other portable devices unit (108), some of which may be built in. Illustrative components include tablets, mobile phones, desktop computers, wireless devices, a cell phone, personal digital assistant (PDA), a stationary personal computer, IPTV remote control, a laptop computer, a pocket PC, a television set capable of receiving IP based video services, a mobile IP device, etc.
[0055] In another embodiment, the user interface unit (210) of the application server (104) is disclosed. In an embodiment, the user interface unit (210) may be configured to display a network interface to the one or more users. Further, the network interface may correspond to one or more devices (303) locations, locations of one or more telemetry units (301) and optimal placement of one or more telemetry units (301) within the one or more devices. Further, the user interface unit (210) may be configured to perform various operations, such as but not limited to data upload, parameter setting, map visualization, and reporting.
[0056] In another embodiment, the one or more telemetry units may comprise at least one of Data Concentrator Units (DCUs), Gateways, Radiofrequency (RF) Field Devices, or a combination thereof. The selection of the type of telemetry unit may be based on network requirements, communication protocol compatibility, device density within each cluster, or deployment constraints. The one or more telemetry units may be configured to collect, aggregate, and transmit data from the one or more devices located within the corresponding one or more clusters, enabling efficient communication between edge devices and backend systems in the network infrastructure.
[0057] In another embodiment, the one or more telemetry units may be connected to the one or more devices using one or more wireless mesh protocols. The wireless mesh protocols may include Wireless Application Protocol (WAP), radio frequency (RF) mesh, Bluetooth Low Energy (BLE), Wi-Fi, Zigbee, LoRa, or a combination thereof. The selection of the wireless mesh protocol may be based on factors such as range, data rate, power consumption, network topology, and device density. The use of wireless mesh connectivity may enable robust, self-healing, and scalable communication among the one or more devices and the one or more telemetry units within each of the one or more clusters.
[0058] In an embodiment, the user interface unit (210) of the application server (104) may include, but not limited to, an app interface, a web interface, a graphical user interface and a touch user interface. Further, the user interface unit (210) may be configured to display the optimal placement of one or more telemetry units (301) within one or more devices.
[0059] In another embodiment, the network planning unit (212) of the application server (104) is disclosed. The network planning unit (212) may be configured for identifying non-optimally positioned one or more telemetry units (301) and facilitating their relocation to enhance network efficiency and performance. An exemplary implementation of this capability may involve utilizing geographic information systems (GIS) to analyze user distribution and network conditions, thereby enabling dynamic adjustments in one or more one or more telemetry units (301) placement based on real-time signal strength and congestion levels.
[0060] In another embodiment, the network planning unit (212) may correspond to a network planning tool. In one embodiment, the network planning tool may utilize the memory and the processor to support the identification and placement of the one or more telemetry units across a network of the one or more devices. The network planning tool may identify the one or more coordinate positions of the one or more devices. Based on the one or more coordinate positions and a configurable node distance, the network planning tool may generate the one or more clusters by grouping the one or more devices in spatial proximity.
[0061] In one embodiment, generating a report may describe the position of each of the one or more telemetry units corresponding to each of the one or more clusters of the one or more devices. The report may include detailed information on the final positions of the telemetry units, the associated clusters, and the clustering technique used to determine those positions. The report may also provide metadata such as the number of devices in each cluster, communication parameters, and the optimal configuration for deployment. This report may be used for network planning, deployment validation, or ongoing monitoring and optimization of the network infrastructure.
[0062] In another embodiment, identifying of the position of each of the one or more telemetry units may be based on a distance between an initial position and the one or more devices within each of the one or more clusters. The clustering technique used by the network planning tool may operate by randomly selecting a set of initial central points, referred to as centroids. Each of the one or more devices may then be assigned to the closest centroid based on a selected distance metric, resulting in the formation of an initial cluster configuration. In another embodiment, after assignment, each centroid may be updated by computing the average of the coordinate positions of the one or more devices in the corresponding cluster. This process of assignment and centroid update may be repeated iteratively until the centroid positions stabilize and stop changing. In another embodiment, the objective of this clustering technique may be to organise the one or more devices into the one or more clusters such that devices with similar spatial characteristics are grouped together. The final centroid positions may then correspond to optimal positions for placement of the one or more telemetry units.
[0063] In another embodiment, the identification of the position of each of the one or more telemetry units may be performed through repeated execution of the clustering technique. The clustering technique may begin with an initial assignment of centroid positions, followed by allocation of the one or more devices to the nearest centroid based on a selected distance metric. After each assignment step, the position of each centroid may be updated based on the average coordinate positions of the one or more devices assigned to that centroid. This process of assignment and centroid adjustment may be executed iteratively. The repeated execution may continue until the position of the one or more telemetry units stabilizes and no further significant changes occur in the centroid positions. At convergence, the resulting one or more clusters may represent a stable and optimized configuration for placement of the one or more telemetry units.
[0064] In another embodiment, the network planning tool may estimate a number of the one or more telemetry units required for each of the one or more clusters. The estimation may be based on a desirable number of the one or more devices per telemetry unit or based on one or more communication or deployment constraints. The network planning tool may assign an initial position for each of the number of the one or more telemetry units within each of the one or more clusters using a clustering technique.
[0065] In another embodiment, the clustering technique may include at least one of K-Means Clustering, K-Medoids, Fuzzy C-Means (FCM), Gaussian Mixture Models (GMM), Agglomerative Clustering, or a combination thereof. The final centroid locations derived from the clustering technique may represent optimal positions for placement of the one or more telemetry units. The network planning tool may display the final centroid locations through the user interface.
[0066] In another embodiment, the planning unit associated with the network planning tool may include one or more of automated telemetry unit relocation capabilities, real-time signal analysis, predictive modelling, or a combination thereof, to identify and relocate non-optimally positioned one or more telemetry units. The planning unit may also be associated with at least one of the user interface (UI), a geographic information system (GIS), the clustering unit, or a web technology, or a combination thereof. The final positions of the one or more telemetry units may be provided back to the network planning tool for further processing or execution.
[0067] In one non-limiting embodiment, the network planning unit (212) of the application server (104) may comprise a planning algorithm for analyzing the smart meter network and further planning the optimal placement of the one or more telemetry units (301) within the smart meter network through RF mesh connectivity.
[0068] In an embodiment, the network planning unit (212) may be associated with the user interface (UI), the geographic information system (GIS), a clustering unit (214) and a web technology or a combination thereof. The geographic information systems (GIS) associated with the network planning unit (212) may be configured for capturing, storing, checking, and analyzing geographic patterns of the one or more telemetry units (301) placement locations, through map visualization, to perform feasibility analysis.
[0069] In another embodiment, the clustering unit (214) of the application server (104) is disclosed. The clustering unit (214) may be configured to perform one or more steps for implementing the method of network planning. The clustering unit (214) may be configured for creating one or more node variables for the one or more devices (303). Further, the one or more nodes variable may hold at least one of Geographic coordinates, Spatial coordinates, Latitude and longitude, Positioning coordinates, Location coordinates, Geodetic coordinates and Cartographic coordinates or a combination thereof of the one or more devices (303). The clustering unit (214) may be configured to employ one or more density-based clustering algorithms to generate one or more clusters. Further, the one or more density-based clustering may ensure to select only the one or more devices (303) which are in range of each other for clustering and prevent non-ranging one or more devices (303) to be part of the clusters to be used in planning for optimal placements of one or more telemetry units (301). The one or more density-based clustering methods may utilise a Haversine metric and a configurable inter-node distance for generating one or more clusters. Further, the clustering unit (214) may be configured for calculating a number of one or more telemetry units (301) corresponding to the one or more clusters. Further, the clustering unit (214) may employ one or more centroid-based clustering which initializes a cluster centroids for the one or more clusters. The cluster centroids may be initialized based on the calculated number of one or more telemetry units (301). Further, the clustering unit (214) may be configured for executing one or more centroid-based clustering on the one or more clusters using the cluster centroids. Further, the clustering unit (214) may be configured for determining the final centroid locations for one or more telemetry units (301).
[0070] In some embodiments, the generation of the one or more clusters may be performed using a density-based clustering technique. The density-based clustering technique may utilize a distance metric selected from at least one of a Haversine metric, Euclidean distance, Manhattan distance, Chebyshev distance, or a combination thereof. The selected distance metric may be applied to the one or more coordinate positions associated with the one or more devices. The density-based clustering technique may analyze spatial distribution and proximity among the devices to identify dense regions representing clusters. The resulting one or more clusters may be stored in one or more cluster variables for subsequent processing, analysis, or visualization.
[0071] In an embodiment, the clustering process may utilize a configurable node distance, which may correspond to a pre-defined or dynamically determined distance required between the one or more devices in a given cluster. This configurable distance may help control cluster compactness or dispersion and may be used to tune the density-based clustering behaviour, such as in DBScan or HDBSCAN, to ensure meaningful spatial groupings of devices.
[0072] In an exemplary embodiment, the system may estimate the number of telemetry units required based on the size of each generated cluster and a pre-defined number of devices intended per cluster. The cluster size may correspond to the count of devices present in a given cluster. The estimated number of telemetry units may thus be calculated as a ratio of the cluster size to the desirable number of smart meters (or devices) per cluster. This estimation may support capacity planning and optimal telemetry unit deployment across the clusters.
[0073] In one embodiment, the clustering technique may include one of k-means clustering, K-Medoids, Fuzzy C-Means (FCM), Gaussian Mixture Models (GMM), Agglomerative Clustering, or a combination thereof. The clustering unit may utilize one or more clustering inputs, such as radio frequency (RF) range of the devices, connection capabilities of telemetry units, number of telemetry units per cluster, initial device positions, or a combination of such parameters. These parameters may guide the clustering algorithm in forming logical and communication-efficient groups of devices.
[0074] In another embodiment, the clustering unit may be configured to determine the number of telemetry units required per cluster based on a desirable number of smart meters per cluster. The number of telemetry units may be computed as a ratio of the total number of devices in a cluster to the desired per-cluster device count. Based on this computation, the cluster centroids may be initialized to reflect the calculated number of telemetry units, thereby enabling more balanced and efficient cluster formations.
[0075] In another embodiment, the one or more density-based clustering techniques employed by the system may include at least one of DBScan clustering, HDBSCAN, Mean Shift, DENCLUE, Gaussian Mixture Models, or a combination thereof. The one or more density-based clustering techniques may be selected based on the characteristics of the device distribution, desired cluster density, or performance considerations of the telemetry system.
[0076] In an embodiment, the one or more devices may correspond to one of a utility meter, electricity meter, gas meter, water meter, hybrid meters, digital meter, prepaid meter, postpaid meter, residential meter, commercial meter, industrial meter and a combination thereof.
[0077] A person skilled in the art will understand that the scope of the disclosure should not be limited to the field of smart meter operations and using the aforementioned techniques. Further, the examples provided in the supra are for illustrative purposes and should not be construed to limit the scope of the disclosure.
[0078] FIG. 3 illustrates a block diagram (300) of network communication for one or more devices (303) connected to one or more telemetry units (301) coupled through RF mesh connectivity is illustrated, in accordance with an embodiment of the present subject matter. The block diagram (300) illustrates one or more devices (303a, 303b, 303c, 303d, hereinafter referred to as 303 interchangeably), one or more telemetry units (301a, 301b, 301c, 301d, hereinafter referred to as 301 interchangeably). In one embodiment, clusters of one or more devices (303) may be connected with one or more telemetry units (301) through RF mesh connectivity. The one or more devices (303) may be configured to collect the electric utility data. Further, the one or more telemetry units (301) may be configured to collect data from one or more devices (303). Further, the one or more telemetry units (301) may be configured to manage the data collected from one or more devices (303) via one or more telemetry units (301). In an embodiment of the present disclosure, the placement of one or more telemetry units (301) corresponding to one or more clusters of one or more devices (303) may be performed through a network planning unit (212).
[0079] Further, the block diagram (300) may present the network infrastructure tailored for smart metering by leveraging RF connectivity and dynamic one or more telemetry units (301) relocation and allocation to enable efficient and robust data communication and management. Further, one or more telemetry units (301a, 301b, 301c, …., 301n) may be connected with one or more devices (303) via Radio-Frequency (RF) schema. Further, the one or more devices (303) may be interconnected with each other. Further, the RF connectivity may provide a wireless link or path between the one or more telemetry units (301) and the one or more devices (303a, 303b, 303c, …., 303n).
[0080] Now referring to FIG 3.A, RF connectivity (304) of a telemetry units (301) with a single cluster (305) of one or more devices (303), is illustrated in accordance with an embodiment of the present subject matter. Figure 3.A comprises one or more nodes of the one or more devices (303) forming a cluster (305) and a telemetry units (301). Further, the cluster (305) of one or more devices (303) may be connected to the telemetry units (301) via a RF mesh (304) connectivity. Further, the one or more nodes may be within a predefined range to form the cluster (305). In an implementation, a DBScan clustering algorithm may be used to create the cluster (305) using inter node distance. Using DBScan clustering algorithm to create the intricate clustering of the nodes will ensure to select only the nodes which are in range of each other for clustering and prevent non-ranging nodes of the one or more devices (303) to be part of the cluster (305) to be used in planning for optimal placements of telemetry units (301). Further, the number of required telemetry units (301) may be calculated, using the planning algorithm, for the cluster (305) based on the cluster size.
[0081] Referring to FIG 3.B, RF connectivity (304) of a single telemetry units (301) with one or more clusters (305) of one or more nodes is illustrated, in accordance with an embodiment of the present subject matter. Figure 3.B comprises one or more nodes forming multiple clusters (305) and a telemetry units (301). Further, the clusters (305) of one or more nodes may be connected to the telemetry units (301) via RF mesh (304) connectivity. Further, the one or more nodes may be within a predefined range to form the clusters (305). In an implementation, the DBScan clustering algorithm may be used to create multiple clusters (305) using inter node distance. Using DBScan clustering algorithm to create the intricate clustering of the nodes will ensure to select only the nodes which are in range of each other for clustering and prevent non-ranging nodes of one or more devices (303) to be part of the clusters (305) to be used in planning for optimal placements of telemetry units (301). Further, the number of required telemetry units (301) may be calculated, using the planning algorithm, for the clusters (305) based on each cluster size.
[0082] Referring to FIG 3.C, RF connectivity (304) of multiple telemetry units (301) with one or more clusters (305) of one or more nodes is illustrated, in accordance with an embodiment of the present subject matter. Figure 3.B comprises one or more nodes forming multiple clusters (305) and one or more telemetry units (301). Further, each cluster (305) of one or more nodes may be connected to multiple telemetry units (301) via RF mesh (304) connectivity. Further, the one or more nodes may be within a predefined range to form the clusters (305). In an implementation, the DBScan clustering algorithm may be used to create multiple clusters (305) using inter node distance. Using DBScan clustering algorithm to create the intricate clustering of the nodes will ensure to select only the nodes which are in range of each other active nodes for clustering and prevent non-ranging nodes of one or more devices (303) to be part of the clusters (305) to be used in planning for optimal placements of telemetry units (301). Further, the number of required telemetry units (301) may be calculated, using the planning algorithm, for the clusters (305) based on each cluster size.
[0083] Referring to FIG. 4, a flowchart that illustrates a method (400) for network planning, in accordance with at least one embodiment of the present subject matter. The method (400) may be implemented by an electronic device (108) including one or more processors (202) and the memory (204) communicatively coupled to the processor (202) and the memory (204) is configured to store processor-executable programmed instructions and the network planning unit (212) comprising the clustering unit (214) that perform the following steps.
[0084] At step (402), the method (400) comprises a step of identifying one or more coordinate positions of one or more devices (303).
[0085] At step (404), the method (400) comprises a step generating one or more clusters based on the one or more coordinate positions and a configurable node distance.
[0086] At step (406), the method (400) comprises a step of estimating a number of one or more telemetry units required for each of the one or more clusters.
[0087] At step (408), the method (400) comprises a step of identifying a position of each of the one or more telemetry units using a clustering technique. Further, the step may comprise an initial position of each of the number of one or more telemetry units is initially assigned within each of the one or more clusters.
[0088] At step (410), the method (400) comprises a step of providing the position of the each of the one or more telemetry units to a network planning tool.
[0089] Let us delve into a detailed working examples of the present disclosure.
[0090] Example 01: Optimizing the placement of one or more telemetry units within a smart meter network consisting of 50 smart meters distributed across an urban area
[0091] A network planning tool receives geographic coordinates corresponding to 50 smart meters deployed across an urban area. Each smart meter is assigned a coordinate position, which is stored as a node variable. The planning unit of the network planning tool utilizes a density-based clustering technique to generate one or more clusters based on spatial proximity of the one or more smart meters. The density-based clustering technique generates 5 distinct clusters representing different zones: a city center, a residential zone, an industrial area, a suburban region, and a rural area.
[0092] The planning unit estimates the number of one or more telemetry units required by evaluating the size of each cluster. Based on the number of smart meters and the distribution across clusters, the planning unit determines that 5 telemetry units are required, one for each of the 5 clusters. Each of the one or more telemetry units corresponds to a cluster.
[0093] The clustering unit identifies a position of each of the one or more telemetry units using a centroid-based clustering technique. An initial centroid is calculated for each cluster based on the average position of the smart meters in the cluster. The initial position of each of the one or more telemetry units is assigned to the respective centroid location. The centroid-based clustering technique then adjusts the initial positions to optimize placement by minimizing communication distance and avoiding coverage overlap, while considering a configurable node distance and communication range.
[0094] The final centroid locations represent the optimized positions of the one or more telemetry units. The one or more telemetry units are positioned as follows: one in the city center at coordinates (X1, Y1), one in the residential area at (X2, Y2), one near the industrial zone at (X3, Y3), one in the suburban area at (X4, Y4), and one in the rural area at (X5, Y5).
[0095] The network planning tool provides the position of each of the one or more telemetry units to a user interface. The user interface displays the one or more clusters and the corresponding positions of the one or more telemetry units, enabling an operator to visualize and validate the optimized network configuration.
[0096] Example 02: Optimizing the placement of telemetry units within a smart meter network using a density-based clustering algorithm (DBSCAN)
[0097] A network planning tool is configured to manage a smart meter network comprising 100 smart meters distributed across a 5 km² suburban area. Each smart meter has an associated coordinate position, and these positions are input into the network planning tool.
[0098] The network planning tool processes the coordinate data of the smart meters using the density-based clustering algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise). The DBSCAN algorithm identifies clusters of smart meters by evaluating the spatial proximity between devices. The configurable parameters of the DBSCAN algorithm are set as follows:
Epsilon (e): Maximum distance between two points to be considered neighbours, set to 200 meters.
MinPts: Minimum number of points required to form a cluster, set to 5.
[0099] The algorithm groups the smart meters into 6 clusters, with each cluster representing a distinct zone of the suburban area, such as residential, commercial, and recreational zones. The clustering technique also identifies some outliers (smart meters that do not belong to any cluster due to their distance from others). These outliers are excluded from the final clustering.
[00100] The network planning tool estimates the number of telemetry units required for each cluster. Based on the size and density of each cluster, the system determines that each of the 6 clusters requires one telemetry unit. Therefore, a total of 6 telemetry units are planned for deployment.
[00101] The centroid-based clustering technique is then applied to each cluster to identify the optimal placement for the telemetry units. A centroid for each cluster is initially computed by averaging the positions of all smart meters within that cluster. The telemetry unit is initially assigned to each cluster’s centroid.
[00102] The centroid-based technique fine-tunes the placement of the telemetry units by adjusting the positions to minimize communication distance and overlap between neighbouring clusters, while considering signal strength and transmission range constraints. The positions of the telemetry units are finalized after several iterations of optimization.
[00103] The final positions of the 6 telemetry units are determined as follows:
One telemetry unit at the centroid of the residential zone (coordinates X1, Y1),
One telemetry unit at the centroid of the commercial zone (coordinates X2, Y2),
One telemetry unit at the centroid of the recreational zone (coordinates X3, Y3),
One telemetry unit at the centroid of the park zone (coordinates X4, Y4),
One telemetry unit at the centroid of the school area (coordinates X5, Y5),
One telemetry unit at the centroid of the industrial zone (coordinates X6, Y6).
[00104] The network planning tool provides the final telemetry unit positions to the user interface, where an operator can visualize the optimized placement of the telemetry units and review the coverage area for each cluster. The placement minimizes overlap between telemetry units and maximizes the overall communication efficiency of the smart meter network.
[00105] FIG. 5 illustrates a block diagram of an exemplary computer system (501) for implementing embodiments consistent with the present disclosure.
[00106] Variations of computer system (501) may be used for placement of one or more telemetry units (301) within a one or more devices. The computer system (501) may comprise a central processing unit (“CPU” or “processor”) (502). The processor (502) may comprise at least one data processor for executing program components for executing user or system generated requests. A user may include a person, a person using a device such as those included in this disclosure, or such a device itself. Additionally, 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, or the like. In various implementations the processor (502) may include a microprocessor, such as AMD Athlon, Duron or Opteron, ARM’s application, embedded or secure processors, IBM PowerPC, Intel’s Core, Itanium, Xeon, Celeron or other line of processors, for example. Accordingly, the processor (502) may be implemented using mainframe, distributed processor, multi-core, parallel, grid, or other architectures. Some embodiments may utilize embedded technologies like application-specific integrated circuits (ASICs), digital signal processors (DSPs), or Field Programmable Gate Arrays (FPGAs), for example.
[00107] Processor (502) may be disposed in communication with one or more input/output (I/O) devices via I/O interface (503). Accordingly, the I/O interface (503) may employ communication protocols/methods such as, without limitation, audio, analog, digital, monoaural, 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, for example.
[00108] Using the I/O interface (503), the computer system (501) may communicate with one or more I/O devices. For example, the input device (504) may be an antenna, keyboard, mouse, joystick, (infrared) remote control, camera, card reader, fax machine, dongle, biometric reader, microphone, touch screen, touchpad, trackball, sensor (e.g., accelerometer, light sensor, GPS, gyroscope, proximity sensor, or the like), stylus, scanner, storage device, transceiver, video device/source, or visors, for example. Likewise, an output device (505) may be a user’s smartphone, tablet, cell phone, laptop, printer, computer desktop, fax machine, video display (e.g., cathode ray tube (CRT), liquid crystal display (LCD), light- emitting diode (LED), plasma, or the like), or audio speaker, for example. In some embodiments, a transceiver (506) may be disposed in connection with the processor (502). The transceiver (506) may facilitate various types of wireless transmission or reception. For example, the transceiver (506) may include an antenna operatively connected to a transceiver chip (example devices include the Texas Instruments® WiLink WL1283, Broadcom® BCM4750IUB8, Infineon Technologies® X-Gold 618-PMB9800, or the like), providing IEEE 802.11a/b/g/n, Bluetooth, FM, global positioning system (GPS), and/or 2G/3G/5G/6G HSDPA/HSUPA communications, for example.
[00109] In some embodiments, the processor (502) may be disposed in communication with a communication network (508) via a network interface (507). The network interface (507) is adapted to communicate with the communication network (508). The network interface (507) 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, or IEEE 802.11a/b/g/n/x, for example. The communication network (508) may include, without limitation, a direct interconnection, local area network (LAN), wide area network (WAN), wireless network (e.g., using Wireless Application Protocol), or the Internet, for example. Using the network interface (507) and the communication network (508), the computer system (501) may communicate with devices such as shown as a laptop (509) or a mobile/cellular phone (510). Other exemplary devices may include, without limitation, personal computer(s), server(s), fax machines, printers, scanners, various mobile devices such as cellular telephones, smartphones (e.g., Apple iPhone, Blackberry, Android-based phones, etc.), tablet computers, desktop computers, eBook readers (Amazon Kindle, Nook, etc.), laptop computers, notebooks, gaming consoles (Microsoft Xbox, Nintendo DS, Sony PlayStation, etc.), or the like. In some embodiments, the computer system (501) may itself embody one or more of these devices.
[00110] In some embodiments, the processor (502) may be disposed in communication with one or more memory devices (e.g., RAM 413, ROM 414, etc.) via a storage interface (512). The storage interface (512) may connect to memory devices 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), fiber 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, or solid-state drives, for example.
[00111] The memory devices may store a collection of program or database components, including, without limitation, an operating system (516), user interface application (517), web browser (518), mail client/server (519), user/application data (520) (e.g., any data variables or data records discussed in this disclosure) for example. The operating system (516) may facilitate resource management and operation of the computer system (501). Examples of operating systems include, without limitation, Apple Macintosh OS X, UNIX, Unix-like system distributions (e.g., Berkeley Software Distribution (BSD), FreeBSD, NetBSD, OpenBSD, etc.), Linux distributions (e.g., Red Hat, Ubuntu, Kubuntu, etc.), IBM OS/2, Microsoft Windows (XP, Vista/7/8, etc.), Apple iOS, Google Android, Blackberry OS, or the like.
[00112] The user interface (517) is for facilitating the display, execution, interaction, manipulation, or operation of program components through textual or graphical facilities. For example, user interfaces (517) may provide computer interaction interface elements on a display system operatively connected to the computer system (501), such as cursors, icons, check boxes, menus, scrollers, windows, or widgets, for example. Graphical user interfaces (GUIs) may be employed, including, without limitation, Apple Macintosh operating systems’ Aqua, IBM OS/2, Microsoft Windows (e.g., Aero, Metro, etc.), Unix X-Windows, or web interface libraries (e.g., ActiveX, Java, JavaScript, AJAX, HTML, Adobe Flash, etc.), for example.
[00113] In some embodiments, the computer system (501) may implement a web browser (518) stored program component. The web browser (518) may be a hypertext viewing application, such as Microsoft Internet Explorer, Google Chrome, Mozilla Firefox, Apple Safari, or Microsoft Edge, for example. Secure web browsing may be provided using HTTPS (secure hypertext transport protocol), secure sockets layer (SSL), Transport Layer Security (TLS), or the like. Web browsers may utilize facilities such as AJAX, DHTML, Adobe Flash, JavaScript, Java, or application programming interfaces (APIs), for example. In some embodiments the computer system (501) may implement a mail client/server (519) stored program component. The mail server (519) 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, or WebObjects, for example. The mail server (519) 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 (501) may implement a mail client (520) stored program component. The mail client (520) may be a mail viewing application, such as Apple Mail, Microsoft Entourage, Microsoft Outlook, or Mozilla Thunderbird.
[00114] In some embodiments, the computer system (501) may store user/application data (521), such as the data, variables, records, or the like as described in this disclosure. Such databases may be implemented as fault-tolerant, relational, scalable, secure databases such as Oracle or Sybase, for example. Alternatively, such databases may be implemented using standardized data structures, such as an array, hash, linked list, struct, structured text file (e.g., XML), table, or as object-oriented databases (e.g., using ObjectStore, Poet, Zope, etc.). Such databases may be consolidated or distributed, sometimes among the various computer systems discussed above in this disclosure. It is to be understood that the structure and operation of any computer or database component may be combined, consolidated, or distributed in any working combination.
[00115] Furthermore, one or more computer-readable storage media may be utilized in implementing embodiments consistent with the present invention. 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., non-transitory. Examples include Random Access Memory (RAM), Read- Only Memory (ROM), volatile memory, nonvolatile memory, hard drives, Compact Disc (CD) ROMs, Digital Video Disc (DVDs), flash drives, disks, and any other known physical storage media.
[00116] In light of the above-mentioned advantages and the technical advancements provided by the disclosed system and method, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself as the claimed steps provide a technical solution to a technical problem.
[00117] Various embodiments of the disclosure provide a non-transitory computer readable medium and/or storage medium, and/or a non-transitory machine-readable medium and/or storage medium having stored thereon, a machine code and/or a computer program having at least one code section executable by a machine and/or a computer for planning placement of one or more telemetry units (301) within one or more devices. At least one code section in the application server (104) causes the machine and/or computer including one or more processors to perform the steps, which include creating the one or more node variables for the one or more devices (303). Further, the processor may be configured to employ the one or more density-based clustering methods to generate the one or more clusters. Further, the processor may be configured for calculating a number of one or more telemetry units (301) corresponding to the one or more clusters. Further, the processor may be configured to initialise the cluster centroids for the one or more clusters. Further, the processor may be configured to execute the one or more centroid-based clustering on the one or more clusters using the cluster centroids. Further, the processor may be configured to determine the final centroid locations for the one or more telemetry units (301).
[00118] Various embodiments of the disclosure encompass numerous advantages, including a method and system for network planning. The disclosed system has several technical advantages, but are not limited to the following:
• Optimized Data Collection: By employing density-based clustering, the method ensures that One or more telemetry units are strategically placed to maximize data aggregation from smart meters, leading to improved data collection efficiency.
• Dynamic Adaptability: The system can dynamically adjust DCU placements based on real-time energy consumption patterns and geographic changes, enhancing responsiveness to fluctuating demand.
• Enhanced Coverage: The approach reduces gaps in coverage by accurately identifying optimal locations for One or more telemetry units , ensuring that more smart meters are included in the data aggregation process.
• Scalable Solution: The method is scalable, allowing utilities to adapt the system as the network of smart meters grows, maintaining optimal placement without the need for extensive manual re-evaluation.
• Informed Decision-Making: By generating clusters based on smart meter data, the system provides utilities with valuable insights into consumption trends, enabling better resource allocation and demand forecasting.
• Reduced Infrastructure Costs: By optimizing the number of One or more telemetry units needed based on clustering, utilities can minimize the costs associated with deploying and maintaining unnecessary units.
• Improved Consumer Insights: With more comprehensive data collection, consumers gain access to detailed insights about their energy usage, empowering them to make informed decisions about their consumption.
• Accurate Billing: The elimination of unconnected smart meters reduces the likelihood of inaccurate billing and energy distribution disparities, fostering trust between consumers and utility providers.
• Efficient Resource Allocation: The method allows for better planning of energy resources, enabling utilities to allocate energy more effectively across different regions based on actual demand.
• Sustainable Energy Management: By supporting a more interconnected and efficient energy grid, the system promotes sustainable practices in energy consumption and management, contributing to overall environmental goals.
[00119] In summary, these technical advantages solve the technical problems of optimal telemetry units placement and effective data aggregation in one or more devices, thereby addressing the challenges associated with traditional energy management systems, such as inefficient resource allocation, coverage gaps, and inaccurate billing. Additionally, these advantages contribute to improved operational efficiency, enhanced consumer insights, and more accurate forecasting, and the potential for a more sustainable and interconnected energy ecosystem that adapts dynamically to changing consumption patterns and geographic factors.
[00120] The claimed invention of a method and system for network planning involves tangible components, processes, and functionalities that interact to achieve specific technical outcomes. The system integrates various elements such as processors, memory, a clustering unit, and a planning unit to effectively perform the planning and placement of one or more telemetry units within a smart meter network.
[00121] Furthermore, the invention involves a non-trivial combination of technologies and methodologies that provide a technical solution for a technical problem. While individual components like processors, databases, planning unit, and clustering unit are well-known in the field of computer science, their integration into a comprehensive system for planning placement of one or more telemetry units in the one or more devices, brings about an improvement and technical advancement in the field of smart meter operation and energy management systems.
[00122] In light of the above-mentioned advantages and the technical advancements provided by the disclosed method and system, the claimed steps as discussed above are not routine, conventional, or well understood in the art, as the claimed steps enable the following solutions to the existing problems in conventional technologies. Further, the claimed steps clearly bring an improvement in the functioning of the device itself, as the claimed steps provide a technical solution to a technical problem.
[00123] The present disclosure may be realized in hardware, or a combination of hardware and software. The present disclosure may be realised in a centralized fashion, in at least one computer system, or in a distributed fashion, where different elements may be spread across several interconnected computer systems. A computer system or other apparatus adapted for carrying out the methods described herein may be suited. A combination of hardware and software may be a general-purpose computer system with a computer program that, when loaded and executed, may control the computer system such that it carries out the methods described herein. The present disclosure may be realized in hardware that comprises a portion of an integrated circuit that also performs other functions.
[00124] A person with ordinary skills in the art will appreciate that the systems, modules, and sub-modules have been illustrated and explained to serve as examples and should not be considered limiting in any manner. It will be further appreciated that the variants of the above disclosed system elements, modules, and other features and functions, or alternatives thereof, may be combined to create other different systems or applications.
[00125] Those skilled in the art will appreciate that any of the aforementioned steps and/or system modules may be suitably replaced, reordered, or removed, and additional steps and/or system modules may be inserted, depending on the needs of a particular application. In addition, the systems of the aforementioned embodiments may be implemented using a wide variety of suitable processes and system modules, and are not limited to any particular computer hardware, software, middleware, firmware, microcode, and the like. The claims can encompass embodiments for hardware and software, or a combination thereof.
[00126] While the present disclosure has been described with reference to certain embodiments, it will be understood by those skilled in the art that various changes may be made, and equivalents may be substituted without departing from the scope of the present disclosure. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present disclosure without departing from its scope. Therefore, it is intended that the present disclosure is not limited to the particular embodiment disclosed, but that the present disclosure will include all embodiments falling within the scope of the appended claims.
,CLAIMS:WE CLAIM:
1. A method (400) for network planning, wherein the method (400) comprises:
identifying (402) one or more coordinate positions of one or more devices;
generating (404) one or more clusters based on the one or more coordinate positions and a configurable node distance;
estimating (406) a number of one or more telemetry units required for each of the one or more clusters;
identifying (408) a position of each of the one or more telemetry units using a clustering technique, wherein an initial position of each of the number of one or more telemetry units is initially assigned within each of the one or more clusters; and
providing (410) the position of the each of the one or more telemetry units to a network planning tool.
2. The method (400) as claimed in claim 1, wherein the one or more coordinate positions comprises at least one of geographic coordinates, spatial coordinates, latitude, longitude, positioning coordinates, location coordinates, geodetic coordinates, cartographic coordinates, or a combination thereof; wherein the one or more coordinate positions are stored in one or more device variables.
3. The method (400) as claimed in claim 1, wherein the one or more devices corresponds to one of a utility meter, electricity meter, gas meter, water meter, hybrid meters, digital meter, prepaid meter, postpaid meter, residential meter, commercial meter, industrial meter and a combination thereof.
4. The method (400) as claimed in claim 1, wherein the generating (404) of the one or more clusters is performed using density-based clustering technique, wherein the density-based clustering technique utilizes one of a distance metric selected from at least one of a Haversine metric, Euclidean distance, Manhattan distance, Chebyshev distance, or a combination thereof, wherein the distance metric utilizing the one or more coordinate positions of the one or more devices while using with the density-based clustering technique, wherein the one or more clusters are stored in one or more cluster variables.
5. The method (400) as claimed in claim 1, wherein the configurable node distance corresponds to a distance configured to be required between the one or more devices in the one or more clusters.
6. The method (400) as claimed in claim 1, wherein the number of the one or more telemetry units is estimated based on a cluster size and a number of devices required in each cluster, wherein the cluster size corresponds to a count of the one or more devices in the one or more clusters.
7. The method (400) as claimed in claim 1, wherein the clustering technique corresponds to one of k-means clustering, K-Medoids, Fuzzy C-Means (FCM), Gaussian Mixture Models (GMM), Agglomerative Clustering, or a combination thereof, wherein the clustering unit is utilizing at least one of radio frequency range of the one or more devices, connection capabilities of the one or more telemetry units, the number of the one or more telemetry units in each cluster, initial position, and a combination thereof.
8. The method (400) as claimed in claim 1, wherein identifying (408) the position of each of the one or more telemetry units is performed based on a distance between the initial position to the one or more devices within each of the one or more clusters.
9. The method (400) as claimed in claim 1, wherein identifying (408) the position of each of the one or more telemetry units is performed through repeated execution of the clustering technique.
10. The method (400) as claimed in claim 1, wherein the one or more telemetry units comprise at least one of Data Concentrator Units (DCUs) or Gateways OR Radiofrequency (RF) Field Device or a combination thereof.
11. The method (400) as claimed in claim 1, wherein the one or more telemetry units are connected to the one or more devices using one or more wireless mesh protocols.
12. The method (400) as claimed in claim 1, comprises generating a report describing the position of each of the one or more telemetry units corresponding to each of the one or more clusters of the one or more devices.
13. A system (100) for network planning, wherein the system (100) comprises:
a memory (204);
a processor (202) coupled with the memory (204), wherein the processor (202) is configured to execute programmed instructions stored in the memory (204);
one or more devices;
one or more telemetry units; and
a network planning tool, wherein the network planning tool, by utilizing the memory (204) and the processor (202), is configured to:
identify (402) one or more coordinate positions of the one or more devices;
generate (404) one or more clusters based on the one or more coordinate positions and a configurable node distance;
estimate (406) a number of the one or more telemetry units required for each of the one or more clusters;
identify (408) a position of each of the one or more telemetry units using a clustering technique, wherein an initial position of each of the number of the one or more telemetry units is initially assigned within each of the one or more clusters; and
provide (410) the position of the each of the one or more telemetry units to the network planning tool.
14. A non-transitory computer-readable medium storing computer-executable instructions for network planning, the computer-executable instructions configured for:
identifying (402) one or more coordinate positions of one or more devices;
generating (404) one or more clusters based on the one or more coordinate positions and a configurable node distance;
estimating (406) a number of one or more telemetry units required for each of the one or more clusters;
identifying (408) a position of each of the one or more telemetry units using a clustering technique, wherein an initial position of each of the number of one or more telemetry units is initially assigned within each of the one or more clusters; and
providing (410) the position of the each of the one or more telemetry units to a network planning tool
.
Dated this 08th Day of May 2025

ABHIJEET GIDDE
AGENT FOR THE APPLICANT
IN/PA- 4407