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

COMPLETE SPECIFICATION
(See section 10 and rule 13)
1. TITLE OF THE INVENTION
SYSTEM AND METHOD FOR MANAGING FUEL IN ONE OR MORE NETWORK RESOURCES OF A NETWORK
2. APPLICANT(S)
NAME NATIONALITY ADDRESS
JIO PLATFORMS LIMITED INDIAN OFFICE-101, SAFFRON, NR. CENTRE POINT, PANCHWATI 5 RASTA, AMBAWADI, AHMEDABAD 380006, GUJARAT, INDIA
3.PREAMBLE TO THE DESCRIPTION

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE NATURE OF THIS INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

FIELD OF THE INVENTION
[0001] The present invention relates to the field of wireless communication systems, more particularly relates to a method and system for managing fuel in one or more network resources of a network.
BACKGROUND OF THE INVENTION
[0002] Traditionally, cell towers play a vital role in facilitating seamless communication across vast geographical areas. These towers require a reliable and continuous power source to maintain uninterrupted services. Currently, diesel generators are widely employed to meet this demand. However, the prevalence of fuel theft has emerged as a critical concern for cell tower operators. The unauthorized siphoning off fuel from generators poses financial risks and jeopardizes the provision of reliable services.
[0003] Fuel theft adversely affects the financial stability of cell tower operators. Substantial investments are made in procuring and maintaining fuel supplies, ensuring the uninterrupted operation of generators. When fuel is stolen, operators incur significant monetary losses, which can impede infrastructure improvements, maintenance activities, and technological advancements.
[0004] In addition to financial repercussions, fuel theft can disrupt the seamless operation of cell towers, leading to service outages. The loss of fuel renders generators ineffective, causing interruptions in communication services. These disruptions impact not only individual users but also emergency services, businesses, and public safety organizations that rely on consistent connectivity.
SUMMARY OF THE INVENTION
[0005] One or more embodiments of the present disclosure provides a method and system for managing fuel in one or more network resources of a network.
[0006] In one aspect of the present invention, a method for managing fuel in one or more network resources of the network is disclosed. The method includes the step of collecting, by one or more processors, data pertaining to one or more parameters of the one or more network resources from each of a base station in the network based on monitoring the one or more parameters of the one or more network resources. The method further includes the step of normalizing, by the one or more processors, the collected data pertaining to the one or more parameters to train an analysis engine utilizing the normalized data. The method further includes the step of forecasting, by the one or more processors, utilizing the analysis engine, one or more values pertaining to the one or more parameters of the one or more network resources. The method further includes the step of detecting, by the one or more processors, a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources.
[0007] In another embodiment, the data pertaining to the one or more parameters includes at least one of, a fuel consumption data, a fuel level data, and a fuel flow rate data.
[0008] In yet another embodiment, the one or more network resources is at least one of an electric generator and a diesel generator.
[0009] In yet another embodiment, normalizing the collected data pertaining to the one or more parameters is related to at least one of, cleaning the collected data, removing null values from the collected data.
[0010] In yet another embodiment, the analysis engine is at least one of a, an Artificial Intelligence/Machine Learning (AI/ML) model.
[0011] In yet another embodiment, the analysis engine learns at least one of trends, patterns and behavior related to the one or more parameters of the one or more network resources.
[0012] In yet another embodiment, the one or more processors forecasts the one or more values pertaining to the one or more parameters based on the learnt trends and patterns.
[0013] In yet another embodiment, the step of detecting, a variation in the one or more forecasted one or more values pertaining to the one or more parameters of the one or more network resources, includes the steps of: comparing, by the one or more processors, current one or more parameters of the one or more network resources received from each of the base station in the network with a threshold and in response to determining, by the one or more processors, a deviation in at least one of, the current one or more parameters in comparison to the threshold, inferring, by the one or more processors, the variation in the one or more parameters.
[0014] In yet another embodiment, the threshold is set by the one or more processors utilizing the forecasted one or more values pertaining to the one or more parameters.
[0015] In yet another embodiment, subsequent to the detection of the variation, a notification is transmitted by the one or more processors to a UE associated with the detection of the variation.
[0016] In yet another embodiment, the one or more processors, utilizing the analysis engine, correlates the collected data from one or more nodes with the collected data pertaining to one or more parameters of the one or more network resources from each of base station in the network.
[0017] In another aspect of the present invention a system for managing fuel in one or more network resources of the network is disclosed. The system includes a collection unit, configured to, collect, data pertaining to one or more parameters of the one or more network resources from each of base station in the network based on monitoring the one or more parameters of the one or more network resources. The system includes a normalization unit, configured to, normalize, the collected data pertaining to the one or more parameters to train an analysis engine utilizing the normalized data. The system further includes a forecasting unit, configured to, forecast, utilizing the analysis engine, one or more values pertaining to the one or more parameters of the one or more network resources. The system further includes a detection unit, configured to, detect, a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources.
[0018] In yet another aspect of the present invention, a non-transitory computer-readable medium having stored thereon computer-readable instructions that, when executed by a processor, causes the processor, to collect data pertaining to one or more parameters of the one or more network resources from each of the base station in the network based on monitoring the one or more parameters of the one or more network resources. The processor is further configured to normalize the collected data pertaining to the one or more parameters to train an analysis engine utilizing the normalized data. The processor is further configured to forecast, utilizing the analysis engine, one or more values pertaining to the one or more parameters of the one or more network resources and detect, a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources.
[0019] In another aspect of the present invention, a User Equipment (UE) is disclosed. One or more primary processors is communicatively coupled to one or more processors. The one or more primary processors is further coupled with a memory. The memory stores instructions which when executed by the one or more primary processors causes the UE to receive the notifications from the one or more processors related to the detection of the variation.
[0020] Other features and aspects of this invention will be apparent from the following description and the accompanying drawings. The features and advantages described in this summary and in the following detailed description are not all-inclusive, and particularly, many additional features and advantages will be apparent to one of ordinary skill in the relevant art, in view of the drawings, specification, and claims hereof. Moreover, it should be noted that the language used in the specification has been principally selected for readability and instructional purposes and may not have been selected to delineate or circumscribe the inventive subject matter, resort to the claims being necessary to determine such inventive subject matter.
BRIEF DESCRIPTION OF THE DRAWINGS
[0021] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems in which like reference numerals refer to the same parts throughout the different drawings. Components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure. Some drawings may indicate the components using block diagrams and may not represent the internal circuitry of each component. It will be appreciated by those skilled in the art that disclosure of such drawings includes disclosure of electrical components, electronic components or circuitry commonly used to implement such components.
[0022] FIG. 1 is an exemplary block diagram of an environment for managing fuel in one or more network resources of a network, according to one or more embodiments of the present invention;
[0023] FIG. 2 is an exemplary block diagram of a system for managing fuel in the one or more network resources of the network, according to one or more embodiments of the present invention;
[0024] FIG. 3 is an exemplary architecture of the system of FIG. 2, according to one or more embodiments of the present invention;
[0025] FIG. 4 is an exemplary signal flow diagram illustrating the flow for managing fuel in the one or more network resources of the network, according to one or more embodiments of the present disclosure; and
[0026] FIG. 5 is a flow diagram of a method for managing fuel in the one or more network resources of the network, according to one or more embodiments of the present invention.
[0027] The foregoing shall be more apparent from the following detailed description of the invention.
DETAILED DESCRIPTION OF THE INVENTION
[0028] Some embodiments of the present disclosure, illustrating all its features, will now be discussed in detail. It must also be noted that as used herein and in the appended claims, the singular forms "a", "an" and "the" include plural references unless the context clearly dictates otherwise.
[0029] Various modifications to the embodiment will be readily apparent to those skilled in the art and the generic principles herein may be applied to other embodiments. However, one of ordinary skill in the art will readily recognize that the present disclosure including the definitions listed here below are not intended to be limited to the embodiments illustrated but is to be accorded the widest scope consistent with the principles and features described herein.
[0030] A person of ordinary skill in the art will readily ascertain that the illustrated steps detailed in the figures and here below are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0031] The present invention discloses a system and method for managing fuel in the one or more network resources of the network. More particularly, the system and method for fuel theft identification is disclosed. The proposed invention offers a novel solution to address a persistent issue of the fuel theft and unauthorized fuel usage from the one or more network resources related to one or more base stations/cell towers. By leveraging the power of Artificial Intelligence (AI)/Machine Learning (ML) logics, the invention aims to detect and prevent such illicit activities by closely monitoring the fuel consumption of the one or more network resources.
[0032] Referring to FIG. 1, FIG. 1 illustrates an exemplary block diagram of an environment 100 for managing fuel in one or more network resources of the network, according to one or more embodiments of the present invention. The environment 100 includes a User Equipment (UE) 102, a server 104, a network 106, a system 108, one or more base stations 110, and one or more network resources 112. A user interacts with the system 108 utilizing the UE 102.
[0033] For the purpose of description and explanation, the description will be explained with respect to one or more user equipment’s (UEs) 102, or to be more specific will be explained with respect to a first UE 102a, a second UE 102b, and a third UE 102c, and should nowhere be construed as limiting the scope of the present disclosure. Each of the at least one UE 102 namely the first UE 102a, the second UE 102b, and the third UE 102c is configured to connect to the server 104 via the telecommunication network 106.
[0034] In an embodiment, each of the first UE 102a, the second UE 102b, and the third UE 102c is one of, but not limited to, any electrical, electronic, electro-mechanical or an equipment and a combination of one or more of the above devices such as Virtual Reality (VR) devices, Augmented Reality (AR) devices, laptop, a general-purpose computer, desktop, personal digital assistant, smartphone, tablet computer, mainframe computer, or any other computing device.
[0035] The network 106 includes, by way of example but not limitation, one or more of a wireless network, a wired network, an internet, an intranet, a public network, a private network, a packet-switched network, a circuit-switched network, an ad hoc network, an infrastructure network, a Public-Switched Telephone Network (PSTN), a cable network, a cellular network, a satellite network, a fiber optic network, or some combination thereof. The network 106 may include, but is not limited to, a Third Generation (3G), a Fourth Generation (4G), a Fifth Generation (5G), a Sixth Generation (6G), a New Radio (NR), a Narrow Band Internet of Things (NB-IoT), an Open Radio Access Network (O-RAN), and the like.
[0036] The network 106 may also include, by way of example but not limitation, at least a portion of one or more networks having one or more nodes that transmit, receive, forward, generate, buffer, store, route, switch, process, or a combination thereof, etc. one or more messages, packets, signals, waves, voltage or current levels, some combination thereof, or so forth.
[0037] The environment 100 includes the server 104 accessible via the network 106. The server 104 may include by way of example but not limitation, one or more of a standalone server, a server blade, a server rack, a bank of servers, a server farm, hardware supporting a part of a cloud service or system, a home server, hardware running a virtualized server, a processor executing code to function as a server, one or more machines performing server-side functionality as described herein, at least a portion of any of the above, some combination thereof. In an embodiment, the entity may include, but is not limited to, a vendor, a network operator, a company, an organization, a university, a lab facility, a business enterprise side, a defense facility side, or any other facility that provides service.
[0038] The environment 100 includes the one or more base stations 110 which is communicably coupled to the server 104 via the network 106. The one or more base stations are the gNodeB (gNB) or eNodeB (eNB). The one or more base stations 110 acts as a central connection point for a wireless device to communicate. The one or more base stations 110 further connects a device to other networks or devices, usually through a dedicated high bandwidth wire or a fiber optic connection. The one or more base stations 110 acts as a gateway between a wired network and the wireless network.
[0039] The environment 100 includes the one or more network resources 112. The purpose of these one or more network resources 112 is to generate electricity. The one or more network resources 112 plays a pivotal role in ensuring uninterrupted power supply for the one or more base stations 110 in the network 106, offering a reliable and efficient backup solution. In the dynamic telecom industry, where downtime can result in significant losses, the one or more network resources 112 serves as a lifeline during power outages. The one or one or more network resources 112 includes at least one of, but not limited to, a diesel generator, a petrol generator, a gasoline generator and any electricity generator which utilizes fuel for generating electricity.
[0040] The environment 100 further includes the system 108 communicably coupled to the server 104, the one or more base stations 110, the one or more network resources 112 and the UE 102 via the network 106. The system 108 is adapted to be embedded within the server 104 or is embedded as the individual entity.
[0041] Operational and construction features of the system 108 will be explained in detail with respect to the following figures.
[0042] FIG. 2 is an exemplary block diagram of the system 108 managing fuel in the one or more network resources 112 of the network 106, according to one or more embodiments of the present invention.
[0043] As per the illustrated and preferred embodiment, the system 108 for managing fuel in the one or more network resources 112 of the network 106, the system 108 includes one or more processors 202, a memory 204, and a storage unit 206. The one or more processors 202, hereinafter referred to as the processor 202, may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions. However, it is to be noted that the system 108 may include multiple processors as per the requirement and without deviating from the scope of the present disclosure. Among other capabilities, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204.
[0044] As per the illustrated embodiment, the processor 202 is configured to fetch and execute computer-readable instructions stored in the memory 204 as the memory 204 is communicably connected to the processor 202. The memory 204 is configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to manage fuel in the one or more network resources 112 of the network 106. The memory 204 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0045] As per the illustrated embodiment, the storage unit 206 is configured to store collected data pertaining to the one or more parameters of the one or more network resources 112 integrated with the one or more base stations 110. The storage unit 206 is further configured to store normalized data pertaining to the one or more parameters of the one or more network resources 112. The storage unit 206 is one of, but not limited to, a centralized database, a cloud-based database, a commercial database, an open-source database, a distributed database, an end-user database, a graphical database, a No-Structured Query Language (NoSQL) database, an object-oriented database, a personal database, an in-memory database, a document-based database, a time series database, a wide column database, a key value database, a search database, a cache databases, and so forth. The foregoing examples of storage unit 206 types are non-limiting and may not be mutually exclusive e.g., the database can be both commercial and cloud-based, or both relational and open-source, etc.
[0046] As per the illustrated embodiment, the system 108 includes the processor 202 for managing fuel in the one or more network resources 112 of the network 106. The processor 202 includes a collection unit 208, a normalization unit 210, an analysis engine 212, a forecasting unit 214, a detection unit 216, a correlation unit 218, and a notification unit 220. The processor 202 is communicably coupled to the one or more components of the system 108 such as the storage unit 206, and the memory 204. In an embodiment, operations and functionalities of the collection unit 208, the normalization unit 210, the analysis engine 212, the forecasting unit 214, the detection unit 216, the correlation unit 218, the notification unit 220, and the one or more components of the system 108 can be used in combination or interchangeably.
[0047] In one embodiment, the one or more network resources 112 are integrated with a plurality of Internet of Things (IoT) devices. The IoT refers to the collective network of connected devices and the technology that facilitates communication between one or more components of the system 108 and a cloud, as well as between the one or more components of the system 108 themselves. The IoT devices are pieces of hardware, such as sensors, actuators, gadgets, appliances, or machines, that are programmed for certain applications and can transmit data over the internet or other networks 106. In particular, the IoT devices are equipped with sensors capable of collecting real-time data related to one or more parameters of the one or more network resources 112. Utilizing the IoT devices, the users can remotely monitor and manage the one or more parameters of the one or more network resources 112.
[0048] In an embodiment, the collection unit 208 of the processor 202 is configured to collect data pertaining to the one or more parameters of the one or more network resources 112 from each of the base station among the one or more base stations 110 in the network 106. In particular, the collection unit 208 collects data pertaining to the one or more parameters from the IoT devices which are installed on the one or more network resources 112 of the one or more base stations 110. The data pertaining to the one or more parameters includes at least one of, but not limited to, fuel consumption data, fuel level data, fuel usage rates, and a fuel flow rate data. In one embodiment, the data pertaining to the one or more parameters further includes at least one of, but not limited to, temperature, timestamp, location, generated power/electricity, and a fuel type related to the one or more network resources 112.
[0049] In an alternate embodiment, the collection unit 208 is configured to collect data pertaining to the pertaining to the one or more parameters from one or more nodes in the network 106. The one or more nodes includes at least one of, but not limited to, Radio Access Network (RAN) or one or more base stations 110. The data collected by the collection unit 208 includes data pertaining to at least one of, but not limited to, power supply, power consumption, an operating temperature, a cell-ID, carrier, users connected, a signal strength, a cell throughput, a data capacity, and a Call Release Reason (CRR).
[0050] In another embodiment, the collection unit 208 is configured to collect data flowing across one or more nodes in the network 106. The data may be related to Fault, Configuration, Accounting, Performance, and Security (FCAPS). The FCAPS is a network management framework created by the International Organization for Standardization (ISO). The primary objective of this network management model is to better understand the major functions of network management systems. The FCAPS data includes data pertaining to at least one of, a fault, a configuration, an alarm, a performance, a counter, Key performance Indicators (KPIs), probes, Call Detail Record (CDR) metric data, and log, etc.
[0051] In an embodiment, the normalization unit 210 of the processor 202 is configured to normalize the data collected by the collection unit 208 pertaining to the one or more parameters of the one or more network resources 112 to train an analysis engine 212 utilizing the normalized data. In particular, the normalization unit 210 preprocesses the collected data which includes at least one of, but not limited to, a data normalization. The data normalization is the process of at least one of, but not limited to, reorganizing the collected data, creating new fields in data, removing the redundant data in the collected data, formatting the collected data and removing null values from the collected data. The main goal of the the normalization unit 210 is to achieve a standardized data format across the entire system 108. The normalization unit 210 ensures that the normalized data is stored appropriately in the storage unit 206 for subsequent retrieval and analysis. In one embodiment, the data collected by the collection unit 208 from the one or more nodes in the network 106 is normalized by the normalization unit 210. In yet another embodiment, the data flowing across one or more nodes is collected by the collection unit 208 and further normalized by the normalization unit 210.
[0052] In one embodiment, the correlation unit 218 of the processor 202 is configured to correlate the collected data from the one or more nodes with the collected data pertaining to one or more parameters of the one or more network resources 112 from each of the base station among the one or more base stations 110 in the network 106. The correlated data may provide insights related to complex relationships among the one or more network resources 112, and the one or more nodes which facilitates the system 108 to make predictions. Further, the correlated is data normalized by the normalization unit 210.
[0053] Further, the normalized data is utilized by the analysis engine 212 in order to train itself. The analysis engine is at least one of, but not limited to, an Artificial Intelligence/Machine Learning (AI/ML) model. While training, the analysis engine 212 learns at least one of, but not limited to, trends, patterns and behavior related to the one or more parameters of the one or more network resources 112. In one embodiment, the system 108 selects an appropriate AI/ML model, such as at least one of, but not limited to, a neural network or a decision tree logic, from a set of available options of AI/ML models. Thereafter, the selected AI/ML model is trained using the normalized data. During the training process, the selected AI/ML model learns the relationships and patterns utilizing the normalized data. In one embodiment, the analysis engine 212 is trained on historical data pertaining to the one or more network resources 112. In another embodiment, the analysis engine 212 is trained utilizing the normalized data pertaining to the one or more nodes in the network 106. In yet another embodiment, the analysis engine 212 is trained utilizing the normalized data pertaining to the correlated data.
[0054] In an alternate embodiment, it is to be noted that the analysis engine 212 may be trained by a separate entity such as a training unit utilizing the normalized data without deviating from the scope of the present disclosure. In particular, the training unit receives the normalized data from the normalization unit 210 and trains the analysis engine 212 utilizing the normalized data.
[0055] In an embodiment, the forecasting unit 214 of the processor 202 is configured to forecast, utilizing the analysis engine 212, one or more values pertaining to the one or more parameters of the one or more network resources 112. In particular, the forecasting unit 214 forecasts the one or more values pertaining to the one or more parameters based on the learnt trends/patterns related to the one or more parameters of the one or more network resources 112. In other words, the forecasting unit 214 predicts the one or more values pertaining to the one or more parameters of the one or more network resources 112. For example, the forecasting unit 214 predicts utilizing the analysis engine 212 the expected fuel consumption value for a given time period, such as the next hour or day. These predicted one or more values act as thresholds for detecting anomalies. In one embodiment, the thresholds pertaining to the one or more parameters may be configured by the user via the UE 102.
[0056] In an embodiment, the detection unit 216 of the processor 202 is configured to detect a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources 112. In order to detect variation, the detection unit 216 compares current one or more parameters of the one or more network resources 112 received from each of the one or more base stations 110 in the network 106 with the thresholds. In one embodiment, the threshold is set by the forecasting unit 214 utilizing the forecasted one or more values pertaining to the one or more parameters. Further, in response to determining a deviation in at least one of, the current one or more parameters in comparison to the thresholds, the detection unit 216 infers that there is variation in the one or more parameters. In other words, when the variation in current values of the one or more parameters are detected, the detection unit 216 infers the variation as at least one of, but not limited to, an anomaly, fuel theft and unauthorized fuel usage.
[0057] In an embodiment, the notification unit 220 of the processor 202 is configured to transmit notification pertaining to the detection of the variation of the one or more parameters of the one or more network resources 112 to the UE 102 of the user. The notification may be at least one of, but not limited to, a message, an alarm, an alert, and a mail. Based on the notification, the user performs one or more actions such as at least one of, raising a trouble ticket for resolving the detected anomaly. The trouble ticket is a request for assistance transmitted by the user. In other words, the trouble ticket is a record of user’s complaints or problem. The trouble ticket remains active until the issue has been resolved.
[0058] The collection unit 208, the normalization unit 210, the analysis engine 212, the forecasting unit 214, the detection unit 216, the correlation unit 218, and the notification unit 220 in an exemplary embodiment, are implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processor 202. In the examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processor 202 may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processor may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the memory 204 may store instructions that, when executed by the processing resource, implement the processor 202. In such examples, the system 108 may comprise the memory 204 storing the instructions and the processing resource to execute the instructions, or the memory 204 may be separate but accessible to the system 108 and the processing resource. In other examples, the processor 202 may be implemented by electronic circuitry.
[0059] FIG. 3 illustrates an exemplary architecture for the system 108, according to one or more embodiments of the present invention. More specifically, FIG. 3 illustrates the system 108 for managing fuel in the one or more network resources 112 of the network 106. It is to be noted that the embodiment with respect to FIG. 3 will be explained with respect to the UE 102 for the purpose of description and illustration and should nowhere be construed as limited to the scope of the present disclosure.
[0060] FIG. 3 shows communication between the UE 102, the system 108, the one or more network resources 112 and the one or more base stations 110. For the purpose of description of the exemplary embodiment as illustrated in FIG. 3, the UE 102, the one or more network resources 112 and the one or more base stations 110 use network protocol connection to communicate with the system 108. In an embodiment, the network protocol connection is the establishment and management of communication between the UE 102, the one or more network resources 112, the one or more base stations 110 and system 108 over the network 106 (as shown in FIG. 1) using a specific protocol or set of protocols. The network protocol connection includes, but not limited to, Session Initiation Protocol (SIP), System Information Block (SIB) protocol, Transmission Control Protocol (TCP), User Datagram Protocol (UDP), File Transfer Protocol (FTP), Hypertext Transfer Protocol (HTTP), Simple Network Management Protocol (SNMP), Internet Control Message Protocol (ICMP), Hypertext Transfer Protocol Secure (HTTPS) and Terminal Network (TELNET).
[0061] In an embodiment, the UE 102 includes a primary processor 302, and a memory 304 and a User Interface (UI) 306. In alternate embodiments, the UE 102 may include more than one primary processor 302 as per the requirement of the network 106. The primary processor 302, may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, single board computers, and/or any devices that manipulate signals based on operational instructions.
[0062] In an embodiment, the primary processor 302 is configured to fetch and execute computer-readable instructions stored in the memory 304. The memory 304 may be configured to store one or more computer-readable instructions or routines in a non-transitory computer-readable storage medium, which may be fetched and executed to manage fuel in the one or more network resources 112 of the network 106. The memory 304 may include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as disk memory, EPROMs, FLASH memory, unalterable memory, and the like.
[0063] In an embodiment, the User Interface (UI) 306 includes a variety of interfaces, for example, a graphical user interface, a web user interface, a Command Line Interface (CLI), and the like. The User Interface (UI) 306 allows the user to manage fuel in the one or more network resources 112 of the network 106. In one embodiment, the user may include at least one of, but not limited to, a network operator, cell tower operators, a security personnel, and a law enforcement agency.
[0064] For example, let us assume in particular one or more base stations 110 such as cell tower X, the one or more network resources 112 such as a diesel generator is used for generating electricity in order to maintain an uninterrupted power supply. Further, let us assume the IoT devices are installed on the diesel generator. These IoT devices collects real-time data related to the fuel consumption of the diesel generator. Thereafter, the collected data is provided to the system 108 in order to detect any fuel theft or unauthorized fuel usage. The collected fuel consumption data is then subjected to the AI/ML model specifically designed to identify anomalies or deviations from expected patterns. Through extensive training and analysis, the AI/ML model learns to recognize normal fuel consumption behaviors exhibited by the diesel generator under regular operating conditions. Thereafter, by continuously monitoring the fuel consumption patterns and comparing the fuel consumption against thresholds, the system 108 effectively detects anomalies and flag potential instances of the fuel theft or the unauthorized fuel usage. The anomaly include, at least one of, but not limited to, a sudden drops in fuel levels or unusual consumption rates, that serve as indicators of suspicious activities. Based on the detection of the anomalies, the notifications are generated by the system 108 to notify the users regarding the detected anomalies via the UI 306.
[0065] As mentioned earlier in FIG.2, the system 108 includes the processors 202, the memory 204, and the storage unit 206, for managing fuel in the one or more network resources 112 of the network 106 are already explained in FIG. 2. For the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition.
[0066] Further, as mentioned earlier the processor 202 includes the collection unit 208, the normalization unit 210, the analysis engine 212, the forecasting unit 214, the detection unit 216, the correlation unit 218, the notification unit 220 which are already explained in FIG. 2. Hence, for the sake of brevity, a similar description related to the working and operation of the system 108 as illustrated in FIG. 2 has been omitted to avoid repetition. The limited description provided for the system 108 in FIG. 3, should be read with the description provided for the system 108 in the FIG. 2 above, and should not be construed as limiting the scope of the present disclosure.
[0067] Advantageously, the utilization of IoT devices and AI/ML model included in the analysis engine 212 provides a proactive approach to combating fuel theft in cell tower X. By constantly monitoring fuel consumption and promptly detecting any deviations, the invention enables swift response, mitigating potential financial losses and minimizing service disruptions caused by the fuel theft incidents.
[0068] FIG. 4 is a signal flow diagram illustrating the flow for managing fuel in the one or more network resources 112 of the network 106, according to one or more embodiments of the present disclosure.
[0069] At step 402, the IOT devices such as sensors which are installed on the one or more network resources transmits the data related to one or more parameters of the one or more network resources to the system 108 subsequent to collecting real-time data related to the one or more parameters of the one or more network resources 112.
[0070] At step 404, the system 108 notifies the user on the UE 102 regarding the identification of the fuel theft in the one or more network resources 112. Initially, the system 108 collects the data related to the one or more parameters of the one or more network resources 112 from the IOT devices. Based on the collected data, the system 108 normalizes the collected data and trains the analysis engine 212 utilizing the collected data. Further, the system 108 forecasts the one or more values related to one or more parameters utilizing the trained analysis engine 212. Thereafter, the system 108 detects a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources 112. The variation in the forecasted one or more values pertaining to the one or more parameters is inferred as the identification of the at least one of, but not limited to, an anomaly, fuel theft and unauthorized fuel usage.
[0071] At step 406, the user transmits the request to the system 108 via the UE 102. The request pertains to take one or more actions in order to resolve the detected anomaly or fuel theft. For example, the user may raise the trouble ticket.
[0072] FIG. 5 is a flow diagram of a method 500 for managing fuel in the one or more network resources 112 of the network 106, according to one or more embodiments of the present invention. For the purpose of description, the method 500 is described with the embodiments as illustrated in FIG. 2 and should nowhere be construed as limiting the scope of the present disclosure.
[0073] At step 502, the method 500 includes the step of collecting data pertaining to the one or more parameters of the one or more network resources 112 from each of the base station 110 in the network 106 based on monitoring the one or more parameters of the one or more network resources 112. In particular, the collection unit 208 is configured to collect data pertaining to the one or more parameters of the one or more network resources 112 from the IoT devices. The IoT devices continuously monitors the one or more parameters of the one or more network resources 112 and provides the real-time data related to the one or more parameters to the collection unit 208. For example, the collection unit 208 receives at least one of, but not limited to, the fuel consumption data from the sensors installed on the one or more network resources 112 such as the diesel generator. By collecting data related to one or more parameters, the system 108 gains insights related to typical fuel consumption patterns/behaviour of the generators. The fuel consumption behaviour pertains to how much fuel is consumed by the diesel generator in an hour or a day in order to produce electricity for the one or more base stations 110.
[0074] At step 504, the method 500 includes the step of normalizing the collected data pertaining to the one or more parameters to train an analysis engine 212 utilizing the normalized data. In particular, the normalization unit 210 is configured to normalize the collected data pertaining to the one or more parameters. For example, the normalization unit 210 is configured to remove the null data, redundant data, repetitive data from the collected data. In one embodiment, inconsistencies in the collected data are corrected by the normalization unit 210 in order to put together the collected data in the standard format. In one embodiment, the normalized data is stored in the storage unit 206 which ensures that the normalized data is available for at least one of, but not limited to, retrieval and analysis. Further, utilizing the normalized data, the analysis engine 212 trains itself. The analysis engine 212 learns at least one of, but not limited to, trends, pattens, behaviour, of the one or more parameters. For example, based on training, the analysis engine 212 such as the AI/ML model comes to know about the fuel consumption of the diesel generator in a particular day.
[0075] At step 506, the method 500 includes the step of forecasting, utilizing the analysis engine 212, one or more values pertaining to the one or more parameters of the one or more network resources 112. In particular, the forecasting unit 214 is configured to forecast the one or more values pertaining to the one or more parameters of the one or more network resources 112 utilizing the analysis engine 212. The analysis engine 212 forecasts the one or more values pertaining to the one or more parameters based on the learnt trends, the pattens, and the behaviour of the one or more parameters. For example, the forecasting unit 214 predicts the one or more values related to the future fuel consumption of the one or more network resources 112, these predicted one or more values acts as the thresholds for detecting anomalies. In other words, the forecasting unit 214 predicts the expected fuel consumption for a given time period, such as the next hour or day.
[0076] At step 508, the method 500 includes the step of detecting the variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources 112. In particular, the detection unit 216 is configured to detect the variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources 112. For example, the detection unit 216 compares the current values of the one or more parameters with the forecasted one or more values i.e. thresholds pertaining to the one or more parameters. Based on comparison, the detection unit 216 determines the variation such as a difference between the current values and the forecasted one or more values. If the detection unit 216 determines that the current values pertaining to the one or more parameters are beyond the forecasted one or more values, then the detection unit 216 infers the difference between the current values and the forecasted one or more values as an anomaly.
[0077] In other words, if the fuel consumption of the one or more network resources 112 such as the diesel generator is significantly higher than the predicted fuel consumption of the one or more network resources 112, then the detection unit 216 infers as unauthorized fuel usage or fuel theft. In yet another example, let us assume on a regular basis for a particular hour, the one or more network resources 112 are consuming 100 liters of fuel per hour. Further, when there is a sudden variation in the fuel consumption of the one or more network resources 112 such as consumption of 500 liters of fuel per hour, then this sudden variation indicates unauthorized fuel usage or fuel theft. Subsequent to the detection of the anomaly, the notification unit 220 transmits the notification to the user regarding the detected anomaly.
[0078] The present invention further discloses a non-transitory computer-readable medium having stored thereon computer-readable instructions. The computer-readable instructions are executed by the processor 202. The processor 202 is configured to collect data pertaining to one or more parameters of the one or more network resources 112 from each of base station 110 in the network 106 based on monitoring the one or more parameters of the one or more network resources 112. The processor 202 is further configured to normalize the collected data pertaining to the one or more parameters to train an analysis engine 212 utilizing the normalized data. The processor 202 is further configured to forecast, utilizing the analysis engine 212, one or more values pertaining to the one or more parameters of the one or more network resources 112. The processor 202 is further configured to detect a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources 112.
[0079] A person of ordinary skill in the art will readily ascertain that the illustrated embodiments and steps in description and drawings (FIG.1-5) are set out to explain the exemplary embodiments shown, and it should be anticipated that ongoing technological development will change the manner in which particular functions are performed. These examples are presented herein for purposes of illustration, and not limitation. Further, the boundaries of the functional building blocks have been arbitrarily defined herein for the convenience of the description. Alternative boundaries can be defined so long as the specified functions and relationships thereof are appropriately performed. Alternatives (including equivalents, extensions, variations, deviations, etc., of those described herein) will be apparent to persons skilled in the relevant art(s) based on the teachings contained herein. Such alternatives fall within the scope and spirit of the disclosed embodiments.
[0080] The present disclosure provides technical advancement. Firstly, the installation of the IoT devices on the one or more network resources such as electricity generators enables real-time monitoring of the one or more parameters such as fuel consumption, providing accurate and up-to-date data for analysis. This allows for prompt detection of anomalies and potential fuel theft incidents. Additionally, the use of analysis engine enhances the system's capability to forecast future fuel consumption patterns, enabling proactive identification of deviations from expected values. By leveraging advanced analytics and anomaly detection logics, the invention minimizes false positives and improves the accuracy of fuel theft identification, ensuring efficient allocation of resources for investigation and mitigation efforts. Moreover, the integration of a system facilitates streamlined data collection, processing, and visualization, empowering users such as cell tower operators to make informed decisions and take immediate action in response to fuel theft incidents. Ultimately, these advantages contribute to enhance operational efficiency, reduced financial losses, and the uninterrupted provision of reliable services to end-users.
[0081] The present invention offers multiple advantages over the prior art and the above listed are a few examples to emphasize on some of the advantageous features. The listed advantages are to be read in a non-limiting manner.

REFERENCE NUMERALS

[0082] Environment - 100;
[0083] User Equipment (UE) - 102;
[0084] Server - 104;
[0085] Network- 106;
[0086] System -108;
[0087] One or more base stations – 110;
[0088] One or more network resources – 112;
[0089] Processor - 202;
[0090] Memory - 204;
[0091] Storage unit – 206;
[0092] Collection unit – 208;
[0093] Normalization unit – 210;
[0094] Analysis engine – 212;
[0095] Forecasting engine – 214;
[0096] Detection unit 216;
[0097] Correlation unit – 218;
[0098] Notification unit – 220;
[0099] Primary Processor – 302;
[00100] Memory – 304;
[00101] User Interface (UI) – 306.

,CLAIMS:CLAIMS
We Claim:
1. A method (500) for managing fuel in one or more network resources (112) of a network (106), the method (500) comprising the steps of:
collecting, by one or more processors (202), data pertaining to one or more parameters of the one or more network resources (112) from each base station (110) in the network (106) based on monitoring the one or more parameters of the one or more network resources (112);
normalizing, by the one or more processors (202), the collected data pertaining to the one or more parameters to train an analysis engine (212) utilizing the normalized data;
forecasting, by the one or more processors (202), utilizing the analysis engine (212), one or more values pertaining to the one or more parameters of the one or more network resources (112);
detecting, by the one or more processors (202), a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources (112).

2. The method (500) as claimed in claim 1, wherein the data pertaining to the one or more parameters includes at least one of, a fuel consumption data, a fuel level data, and a fuel flow rate data.

3. The method (500) as claimed in claim 1, wherein the one or more network resources (112) is at least one of an electric generator and a diesel generator.

4. The method (500) as claimed in claim 1, wherein normalizing the collected data pertaining to the one or more parameters is related to at least one of, cleaning the collected data, removing null values from the collected data.

5. The method (500) as claimed in claim 1, wherein the analysis engine (212) is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.

6. The method (500) as claimed in claim 1, wherein the analysis engine (212) learns at least one of trends, patterns and behaviour related to the one or more parameters of the one or more network resources (112).

7. The method (500) as claimed in claim 1, wherein the one or more processors (202) forecasts the one or more values pertaining to the one or more parameters based on the learnt trends and patterns.

8. The method (500) as claimed in claim 1, wherein the step of detecting, a variation in the one or more forecasted one or more values pertaining to the one or more parameters of the one or more network resources (112), includes the steps of:
comparing, by the one or more processors (202), current one or more parameters of the one or more network resources (112) received from each of the base station (110) in the network (106) with a threshold; and
in response to determining, by the one or more processors (202), a deviation in at least one of, the current one or more parameters in comparison to the threshold, inferring, by the one or more processors (202), the variation in the one or more parameters.

9. The method (500) as claimed in claim 8, wherein the threshold is set by the one or more processors (202) utilizing the forecasted one or more values pertaining to the one or more parameters.

10. The method (500) as claimed in claim 1, wherein subsequent to the detection of the variation, a notification is transmitted by the one or more processors (202) to a UE (102) associated with the detection of the variation.

11. The method (500) as claimed in claim 1, wherein the one or more processors (202) is configured to collect data from one or more nodes in the network (106).

12. The method (500) as claimed in claim 11, wherein the one or more processors (202), utilizing the analysis engine (212), correlates the collected data from one or more nodes with the collected data pertaining to one or more parameters of the one or more network resources (112) from each of base station (110) in the network (106).

13. A system (108) for managing fuel in one or more network resources (112) of a network (106), the system (108) comprises:
a collection unit (208), configured to, collect, data pertaining to one or more parameters of the one or more network resources (112) from each of base station (110) in the network (106) based on monitoring the one or more parameters of the one or more network resources (112);
a normalization unit (219), configured to, normalize, the collected data pertaining to the one or more parameters to train an analysis engine (212) utilizing the normalized data;
a forecasting unit (214), configured to, forecast, utilizing the analysis engine (212), one or more values pertaining to the one or more parameters of the one or more network resources (112); and
a detection unit (216), configured to, detect, a variation in the forecasted one or more values pertaining to the one or more parameters of the one or more network resources (112).

14. The system (108) as claimed in claim 13, wherein the data pertaining to the one or more parameters includes at least one of, a fuel consumption data, a fuel level data, and a fuel flow rate data.

15. The system (108) as claimed in claim 13, wherein the one or more network resources (112) is at least one of an electric generator and a diesel generator.

16. The system (108) as claimed in claim 13, wherein normalizing the collected data pertaining to the one or more parameters is related to at least one of, cleaning the collected data, removing null values from the collected data.

17. The system (108) as claimed in claim 13, wherein the analysis engine (212) is at least one of, an Artificial Intelligence/Machine Learning (AI/ML) model.

18. The system (108) as claimed in claim 13, wherein the analysis engine (212) learns at least one of trends, patterns and behaviour related to the one or more parameters of the one or more network resources (112).

19. The system (108) as claimed in claim 13, wherein the forecasting unit (214) forecasts the one or more values pertaining to the one or more parameters based on the learnt trends and patterns.

20. The system (108) as claimed in claim 13, wherein the detection unit (216) detects, a variation in the one or more forecasted one or more values pertaining to the one or more parameters of the one or more network resources (112), by:
comparing, current one or more parameters of the one or more network resources (112) received from each of the base station (110) in the network (106) with a threshold; and
in response to determining, a deviation in at least one of, the current one or more parameters in comparison to the threshold, inferring the variation in the one or more parameters.

21. The system (108) as claimed in claim 20, wherein the threshold is set by the forecasting unt (214) utilizing the forecasted one or more values pertaining to the one or more parameters.

22. The system (108) as claimed in claim 13, wherein subsequent to the detection of the variation, a notification is transmitted to a UE (102) associated with the detection of the variation.

23. The system (108) as claimed in claim 13, wherein the collection unit (208) is configured to collect data from one or more nodes in the network (106).

24. The system (108) as claimed in claim 13, wherein a correlation unit (218), utilizing the analysis engine (212), correlates the collected data from one or more nodes with the collected data pertaining to one or more parameters of the one or more network resources (112) from each of base station (110) in the network (106).

25. A User Equipment (UE) (102), comprising:
one or more primary processors (302) communicatively coupled to one or more processors (202), the one or more primary processors (302) coupled with a memory (304), wherein said memory (304) stores instructions which when executed by the one or more primary processors (302) causes the UE (102) to:
receive, the notification from the one or more processors (202) related to the detection of the variation; and
wherein the one or more processors (202) is configured to perform the steps as claimed in claim 1.