Description:TECHNICAL FIELD
[0001] The embodiments of the present disclosure generally relate to the field Internet of Things (IoT) systems for data collection in industrial environments. More particularly, the present disclosure relates to a system and a method for generating maintenance strategies for sustainable IoT systems.

BACKGROUND
[0002] The following description of the related art is intended to provide background information pertaining to the field of the disclosure. This section may include certain aspects of the art that may be related to various features of the present disclosure. However, it should be appreciated that this section is used only to enhance the understanding of the reader with respect to the present disclosure, and not as admissions of the prior art.
[0003] Conventional maintenance approaches for Internet of Things (IoT) systems often operate independently, with insufficient coordination between sustainability initiatives, condition-based maintenance, reliability-cantered maintenance, and predictive maintenance. While each approach offers distinct advantages, where Predictive Maintenance (PdM) aims to forecast and mitigate failures, Condition-Based Maintenance (CBM) focuses on monitoring equipment conditions to optimize maintenance schedules, and Reliability-Centered Maintenance (RCM) focuses on minimizing downtime and costs. This disjointed approach restricts the full potential of data analytics and advanced machine learning technologies, which are crucial for optimizing maintenance schedules and accurately predicting failures. Furthermore, these strategies often fail to incorporate sustainability adequately. Many existing frameworks prioritize immediate operational needs without sufficiently considering the long-term environmental consequences, including electronic waste and resource depletion. This lack of consideration can lead to strategies that, while effective in the short term, fail to address the sustainability challenges present in contemporary IoT ecosystems effectively.
[0004] Robust maintenance strategies are essential to ensure optimal performance and longevity of IoT devices, as their rapid proliferation has introduced various challenges. Significant obstacles to implementing effective IoT maintenance strategies include the frequent need for device replacements due to shortened lifespans, inefficient resource utilization, unexpected downtime, and inadequate proactive maintenance alerts. These challenges increase operational costs and contribute to greater environmental impact through increased electronic waste and resource depletion. To effectively address these issues, it is crucial to focus on critical areas, including extending device lifetimes, optimizing resource use, improving upkeep time, providing proactive maintenance alerts, reducing environmental impact, and enhancing cost efficiency. These focus areas will guide developing and implementing more effective maintenance strategies for IoT devices.
[0005] Evaluating and improving IoT maintenance strategies requires considering both quantitative and qualitative metrics. Quantitative metrics include the average increase in device lifetime, resource efficiency, downtime and carbon footprint reduction, and maintenance cost savings. These provide measurable data to analyze the effectiveness of maintenance strategies. Qualitative metrics cover the effectiveness of proactive alerts and user satisfaction with the maintenance process. Integrating quantitative and qualitative factors enables a better understanding of the strengths and limitations of each maintenance strategy, leading to more informed decisions and targeted improvements.
[0006] Patent document WO2023/131445A1 relates to a method for creating a maintenance plan for a rail vehicle which consists of a plurality of components. For one component, the maintenance plan includes maintenance measures to be carried out in the context of a preventive maintenance. For a further component, the maintenance plan includes maintenance measures to be carried out on the component within the context of a performance-based, preventive maintenance. Operating data on associated components of the rail vehicle are determined and transmitted to an IoT platform to determine the maintenance measures for the performance-based, preventive maintenance. A data-driven update of the maintenance plan is performed there taking into account the operating data.
[0007] Patent document US20230382504A1 describes a method which includes accessing a vessel’s data profiles. The method includes determining, a first data profile configured for assessing condition or integrity risks, a second data profile configured for assessing statutory, regulatory, and port state control. The method includes determining, a third data profile configured for assessing quality of management systems, a fourth data profile configured for assessing class trend of sister vessels, and a fifth data profile configured for assessing sustainability based on fuel consumption and emissions. Further, the method includes analyzing the accessed data profiles by a predictive compliance model configured for quantifying and assessing an overall risk of vessels being out of compliance with standards. The method includes determining a class-related risk profiling capability and risks of systems and components of the vessel with respect to condition. The method includes class compliance based on the analysis, and sending instructions to a client system for presenting the class-related risk profiling capability and the risks to a user (e.g., a vessel operator).
[0008] Patent Document IN202441065273A discloses an enhanced predictive maintenance framework for heavy machinery that integrates the IoT and Artificial Intelligence (AI) to optimize maintenance processes. The framework employs IoT sensors to continuously monitor critical operational parameters, such as vibration, temperature, pressure, and usage patterns, in real-time. Data collected from these sensors is processed by an AI-based analytics engine, which uses machine learning algorithms to detect anomalies, predict potential failures, and optimize maintenance schedules. The invention reduces unexpected downtime, lowers maintenance costs, and extends machinery lifespan by providing maintenance teams with actionable insights and timely alerts. The system is scalable and adaptable, making it suitable for a wide range of industrial applications, including manufacturing, construction, mining, and transportation. Additionally, a second embodiment of the invention incorporates edge computing, allowing real-time data processing, and predictive analysis directly at the machinery site, further enhancing the system’s responsiveness and applicability in remote or harsh environments.
[0009] Non-patent literature (NPL) maintenance optimization in industry 4.0, reviews maintenance optimization from different and complementary points of view. Specifically, the NPL systematically analyses the knowledge, information, and data that can be exploited for maintenance optimization within the industry 4.0 paradigm. Then, the possible objectives of the optimization are critically discussed, together with the maintenance features to be optimized, such as maintenance periods and degradation thresholds. The main challenges and trends of maintenance optimization are, then, highlighted and the need is identified for methods that do not require a-priori selection of a predefined maintenance strategy. Further, methods that deal with large amounts of heterogeneous data collected from different sources are identified. Methods that can properly treat all the uncertainties affecting the behavior of the systems and the environment are identified. Further, methods that can jointly consider multiple optimization objectives, including the emerging ones related to sustainability and resilience are further investigated.
[0010] NPL insertion of sustainability concepts in the maintenance strategies to achieve sustainable manufacturing aims to explain the concepts of sustainability being inserted in the maintenance strategies. For this purpose, a literature review and a systematic literature review were performed. Further, the NPL verifies the concepts of sustainability integrated into maintenance strategies by means of sustainable criteria, with emphasis on lost production cost, spare parts cost, and expenditures associated with energy consumption. Further, greenhouse gas emissions (economic dimension), on pollutant emission due to energy consumption during machining/manufacturing (environmental dimension) and on health and safety at work (social dimension) were investigated.
[0011] Previous research has explored various approaches to IoT maintenance, including condition-based monitoring, predictive analytics, and reliability-centered maintenance. However, these strategies have often been implemented independently, with limited integration and consideration for sustainability. Existing frameworks prioritize operational efficiency without adequately addressing the long-term environmental impact of IoT systems. There is a need for a more holistic and harmonized approach that can optimize IoT performance, reliability, and longevity while minimizing resource consumption and environmental footprint.
[0012] Previous research has explored various approaches to IoT maintenance, including condition-based monitoring, predictive analytics, and reliability-centered maintenance. However, these strategies have often been implemented independently, with limited integration and consideration for sustainability. Further, these strategies often fail to incorporate sustainability adequately and do not consider the long-term environmental consequences, including electronic waste and resource depletion. This can lead to strategies that are effective in the short term but do not effectively address the sustainability challenges present in contemporary IoT ecosystems.
[0013] Therefore, there is a need for a system and a method that can mitigate the problems associated with conventional systems and provide an efficient system and method for IoT maintenance.

OBJECTS OF THE PRESENT DISCLOSURE
[0014] Some of the objects of the present disclosure, which at least one embodiment herein satisfies are listed herein below.
[0015] It is an object of the present disclosure to provide a system and a method for generating maintenance strategies for sustainable Internet of Things (IoT) systems that receives data associated with entities through a centralized server, where multiple devices are configured to record information associated with one or more entities.
[0016] It is an object of the present disclosure to provide a system that identifies anomalies from the information recorded by the devices.
[0017] It is an object of the present disclosure to provide a system that instantaneously generates artifacts associated with the devices based on the identified anomalies.
[0018] It is an object of the present disclosure to provide a system that analyzes the artifacts and generates a schedule associated with the maintenance of the one or more devices based on the identified anomalies.
[0019] It is an object of the present disclosure to provide a system that predicts, via a machine learning engine, performance metrics associated with the devices based on the generated artifacts and the identified anomalies.
[0020] It is an object of the present disclosure to provide a system that provides a harmonized maintenance strategy for optimizing the performance, reliability, and sustainability of IoT systems.
[0021] It is an object of the present disclosure to provide a system that uses a comprehensive, integrated approach, seamlessly combining various maintenance methodologies and data analytics tools to address the shortcomings of existing siloed strategies.
[0022] It is an object of the present disclosure to provide a system that employs sophisticated data analysis techniques, including predictive analytics and condition-based monitoring, to enhance the effectiveness and proactivity of IoT maintenance practices.
[0023] It is an object of the present disclosure to provide a system that emphasizes sustainability, addressing the environmental impact and resource depletion challenges associated with the rapid proliferation of IoT devices.

SUMMARY
[0024] This section is provided to introduce certain objects and aspects of the present disclosure in a simplified form that are further described below in the detailed description. This summary is not intended to identify the key features or the scope of the claimed subject matter.
[0025] In an aspect, the present disclosure relates to an integrated framework for maintenance of devices. The system includes a processor and a memory operatively coupled with the processor, where said memory stores instructions which, when executed by the processor, cause the processor to receive data associated one or more entities from a centralized server, where the data includes information associated with the one or more entities recorded by one or more devices. The processor identifies an anomaly from the information recorded by the one or more devices. The processor instantaneously generates, one or more artifacts associated with the one or more devices based on the identified anomaly. The processor analyzes the generated one or more artifacts and generates a schedule associated with the maintenance of the one or more devices based on the identified anomaly. The processor predicts, via a machine learning engine, one or more performance metrics associated with the one or more devices based on the identified anomaly and the analyzed one or more artifacts.
[0026] In an embodiment, to predict the one or more performance metrics, the processor may be configured to determine that a predetermined number of devices o are in a non-functional state based on one or more unplanned interruptions and subsequently generate an alert associated with the one or more artifacts based on the non-functional state of the predetermined number of devices.
[0027] In an embodiment, to generate the analyzed one or more artifacts, the processor may be configured to determine real-time data associated with the generated one or more artifacts, analyze the real-time data, and predict via the machine learning engine, the non-functional state of the predetermined number of devices for one or more time periods based on the analyzed real-time data.
[0028] In an embodiment, the processor may be configured to subsequently optimize the generated schedule based on the analyzed real-time data and enable maintenance of the predetermined number of devices for a predetermined period, where the processor may be configured to enable recording by the predetermined number of devices based on a functional-state of the predetermined devices predicted by the machine learning engine.
[0029] In an embodiment, the processor may be configured to determine one or more greenhouse gas emissions associated with the non-functional state of the predetermined number of devices. The processor may be configured to utilize the predetermined number of devices based on the functional-state of the predetermined number of devices for recording of the information and minimize power consumption during the optimized schedule. The processor may be configured to determine malfunctioning of at least a device among the predetermined number of devices emitting the one or more greenhouse gas emissions.
[0030] In an aspect, the present disclosure relates to a method for maintenance of devices. The method includes receiving, by a processor, associated with a system, data associated one or more entities from a centralized server, where the data includes information associated with the one or more entities recorded by one or more devices. The method includes identifying, by the processor, an anomaly from the information recorded by the one or more devices. The method includes instantaneously generating, by the processor, one or more artifacts associated with the one or more devices based on the identified anomaly. The method includes analyzing the generated one or more artifacts and generating a schedule associated with the maintenance of the one or more devices based on the analyzed one or more artifacts.. The method includes predicting, by the processor, via a machine learning engine, one or more performance metrics associated with the one or more devices based on the analyzed one or more artifacts.
[0031] In an embodiment, for predicting the one or more performance metrics, the method may include, determining, by the processor that a predetermined number of devices of the one or more devices are in a non-functional state based on one or more unplanned interruptions and generating an alert associated with the one or more artifacts based on the non-functional state of the predetermined number of devices.
[0032] In an embodiment, for analyzing the one or more artifacts, the method may include determining, by the processor, real-time data associated with the generated one or more artifacts, analyzing the real-time data, and predicting via the machine learning engine, the non-functional state of the predetermined number of devices for one or more time periods based on the analyzed real-time data.
[0033] In an embodiment, the method may include subsequently optimizing, by the processor, the generated schedule based on the analyzed real-time data and enabling maintenance of the predetermined number of devices for a predetermined period, where the optimization of the generated schedule may include recording by the processor through the predetermined number of devices based on a functional-state of the predetermined devices predicted by the machine learning engine.
[0034] In an embodiment, the method may include determining, by the processor, one or more greenhouse gas emissions associated with the non-functional state of the predetermined number of devices and utilizing, by the processor, the predetermined number of devices based on the functional-state of the predetermined number of devices for recording of the information and minimizing power consumption during the optimized schedule. The method may include determining malfunctioning of at least a device among the predetermined number of devices emitting the one or more greenhouse gas emissions.

BRIEF DESCRIPTION OF DRAWINGS
[0035] The accompanying drawings, which are incorporated herein, and constitute a part of this disclosure, illustrate exemplary embodiments of the disclosed methods and systems 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 the disclosure of electrical components, electronic components, or circuitry commonly used to implement such components.
[0036] FIG. 1 illustrates an example system architecture (100) of the proposed system (108), in accordance with an embodiment of the present disclosure.
[0037] FIG. 2 illustrates an example block diagram (200) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0038] FIG. 3 illustrates an example flow diagram (300) of the proposed system (108), in accordance with an embodiment of the present disclosure.
[0039] FIG. 4 illustrates an example flow diagram (400) of Internet of Things (IoT) system usability by the proposed system (108), in accordance with an embodiment of the present disclosure.
[0040] The foregoing shall be more apparent from the following more detailed description of the disclosure.

DETAILED DESCRIPTION
[0041] In the following description, for the purposes of explanation, various specific details are set forth in order to provide a thorough understanding of embodiments of the present disclosure. It will be apparent, however, that embodiments of the present disclosure may be practiced without these specific details. Several features described hereafter can each be used independently of one another or with any combination of other features. An individual feature may not address all of the problems discussed above or might address only some of the problems discussed above. Some of the problems discussed above might not be fully addressed by any of the features described herein.
[0042] The present disclosure describes a system for generating maintenance strategies for sustainable Internet of Things (IoT) systems. The system uses an integrated approach that addresses four critical aspects such as sustainability, predictive maintenance, condition-based maintenance, and reliability-centered maintenance. The system aims to harmonize the competing priorities of performance, sustainability, and reliability, ensuring a well-balanced IoT maintenance strategy. The system implements a comprehensive, integrated approach, seamlessly combining various maintenance methodologies and data analytics tools to address the shortcomings of existing siloed strategies. The system employs sophisticated data analysis techniques, including predictive analytics, and condition-based monitoring, to enhance the effectiveness and proactivity of IoT maintenance practices. The system emphasizes sustainability, addressing the environmental impact and resource depletion challenges associated with the rapid proliferation of IoT devices.
[0043] Various embodiments of the present disclosure will be explained in detail with reference to FIGs. 1-3.
[0044] FIG. 1 illustrates an example system architecture (100) of the proposed system (108), in accordance with an embodiment of the present disclosure.
[0045] As illustrated in FIG. 1, one or more devices (102-1, 102-2…102-N) may be connected to the proposed system (108) through a centralized server (104). A person of ordinary skill in the art will understand that the one or more devices (102-1, 102-2…102-N) may be collectively referred as the devices (102) and individually referred as the device (102). The devices (102) may be connected to the centralized server (104) through a network (106). The devices (102) may also include a mix of wireless Internet-of-Things (IoT) devices that are connected to one or more entities. The one or more entities may include but not limited to various industry sectors, including manufacturing, healthcare, smart cities, and transportation.
[0046] In an embodiment, the network (106) may 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. The network (104) may also include, 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.
[0047] In an embodiment, the system (108) (also referred as the integrated framework) may include an integrated IoT maintenance strategy across different levels. This may include monitoring standalone device levels where maintenance tasks such as direct interventions including firmware updates, recalibration, and hardware checks. The integrated IoT maintenance strategy may include notification methods where local indicators like LEDs, buzzers, or display messages for immediate status updates may be incorporated. The integrated IoT maintenance strategy may include maturity considerations where maturity is characterized by the ability of individual devices to perform self-diagnostics and simple maintenance tasks autonomously, without external commands. Further, the integrated IoT maintenance strategy may include edge level scope estimation that includes maintenance strategies for devices within a specific physical location or network.
[0048] In an embodiment, the system (108) may combine different maintenance strategies to maximize benefits. The system (106) may use Predictive Maintenance (PdM) with advanced predictive analytics to anticipate and prevent failures. The system (108) may use Condition-Based Maintenance (CBM) which monitors real-time device conditions to enable timely interventions. Further, the system (108) may use Reliability-Centered Maintenance (RCM) to focus on optimizing maintenance schedules based on criticality and risk. The system (108) may incorporate best practices such as advanced data analytics, cross-layer system integration, scalability, adaptability, and sustainable practices to maximize the benefits. These elements may work together to establish a new standard for IoT system maintenance that is predictive, proactive, and environmentally responsible. Further, the present disclosure includes a system that extends device lifetimes, optimizes resource use, improves uptime, provides proactive maintenance alerts, reduces environmental impacts, and enhances cost efficiency. These objectives are strategically aligned with user/customer needs and design inputs to ensure a comprehensive and effective approach. The system incorporates best practices such as advanced data analytics, cross-layer system integration, scalability, adaptability, and sustainable practices to maximize the benefits. These elements work together to establish a new standard for IoT system maintenance that is predictive, proactive, and environmentally responsible.
[0049] In an embodiment, the system (108) may integrate multiple maintenance strategies, including predictive diagnostics, condition-based monitoring, reliability-centered maintenance, and sustainability-focused approaches, provides a comprehensive and tailored solution for diverse IoT applications. The system (108) may utilize advanced predictive algorithms and failure mode analysis capabilities to proactively identify and mitigate potential issues, going beyond traditional reactive threshold-based monitoring. The system (108) may enable sustainability aspects, such as energy efficiency, waste reduction, and performance and reliability optimization, setting it apart from prior art solutions. The system (108) may implement modular and adaptable design for the unique maintenance requirements of IoT devices across various industries, ensuring optimal outcomes.
[0050] In an embodiment, the system (108) may provide an Integrated IoT Maintenance Strategy across different levels. For standalone device levels the system (108) may implement maintenance tasks with direct interventions such as but not limited to firmware updates, recalibration, and hardware checks.
[0051] In an embodiment, the system (108) may provide notification methods where local indicators like Light Emitting Diodes (LEDs), buzzers, or display messages for immediate status updates may be used.
[0052] In an embodiment, the system (108) may provide maturity consideration where, maturity may be characterized by the ability of individual devices to perform self-diagnostics and simple maintenance tasks autonomously, without external commands.
[0053] In an embodiment, the system (108) may provide edge level integration where maintenance covers devices within a specific physical location or network.
[0054] In an embodiment, the system (108) may schedule maintenance tasks where aggregate data analysis, localized decision-making, and data flow may be managed to optimize cloud resource utilization.
[0055] In an embodiment, the system (108) may provide notification and data handling where the system (108) may filter and process relevant data locally to enhance response times and reduce data sent to the cloud server.
[0056] In an embodiment, the system (108) may provide maturity at the edge level with advanced data.
[0057] In an embodiment, the system (108) may implement a modular architecture that integrates various sensing technologies, data analytics engines, and maintenance strategies, enabling customization for diverse IoT applications. The system (108) may utilize machine learning algorithms to continuously refine its predictive models and failure mode analysis, improving the accuracy of diagnostics and prognostics over time.
[0058] FIG. 2 illustrates an example block diagram (200) of a proposed system (108), in accordance with an embodiment of the present disclosure.
[0059] Referring to FIG. 2, the system (108) may comprise one or more processor(s) (202) that may be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that process data based on operational instructions. Among other capabilities, the one or more processor(s) (202) may be configured to fetch and execute computer-readable instructions stored in a memory (204) of the system (108). The memory (204) 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 create or share data packets over a network service. The memory (204) may comprise any non-transitory storage device including, for example, volatile memory such as random-access memory (RAM), or non-volatile memory such as erasable programmable read only memory (EPROM), flash memory, and the like.
[0060] In an embodiment, the system (108) may include an interface(s) (206). The interface(s) (206) may comprise a variety of interfaces, for example, interfaces for data input and output (I/O) devices, storage devices, and the like. The interface(s) (206) may also provide a communication pathway for one or more components of the system (108). Examples of such components include, but are not limited to, processing engine(s) (208) and a database (210), where the processing engine(s) (208) may include, but not be limited to, a data ingestion engine (212), and a machine learning engine (214).
[0061] In an embodiment, the processing engine(s) (208) may be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) (208). In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) (208) may be processor-executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) (208) may comprise a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) (208). In such examples, the system (108) may comprise the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the system (108) and the processing resource. In other examples, the processing engine(s) (208) may be implemented by electronic circuitry.
[0062] In an embodiment, the processor (202) may receive data associated with one or more entities through the data ingestion engine (212). The data may be received through a centralized server (104). The received data may include information associated with the one or more entities, recorded by the one or more devices (102). The processor (202) may record the data in the database (210). The one or more devices (102) may include but not limited to one or more sensors, one or more actuators, and one or more control systems associated with the monitoring of the one or more entities. The one or more entities may include but not limited to an industrial Internet of Things (IoT) entity, a smart city IoT entity, a consumer electronics IoT entity, and a healthcare IoT entity.
[0063] In an embodiment, the processor (202) may optimize the maintenance of industrial IoT devices and provide predictive analytics to anticipate equipment failures, condition-based monitoring to enable timely interventions, and sustainability measures to reduce energy consumption and waste.
[0064] In an embodiment, the processor (202) may adapt a harmony framework to manage the maintenance of IoT devices used in smart city applications, including traffic monitoring, public lighting, and environmental sensing. The processor (202) through the framework may implement an integrated approach to ensure efficient resource utilization, proactive maintenance, and minimized environmental impact as the smart city infrastructure scales.
[0065] In an embodiment, the processor (202) may implement the harmony framework to optimize the maintenance of IoT-enabled consumer electronics, such as wearable devices, home automation systems, and personal assistants. The processor (202) may implement predictive diagnostics to extend product lifespans, condition-based monitoring to enable user-friendly maintenance, and sustainable design to reduce electronic waste.
[0066] In an embodiment, the processor (202) may address unique maintenance requirements of IoT devices in the healthcare sector, including medical sensors, remote patient monitoring systems, and smart hospital equipment. For example, the processor (202) may prioritize reliability-centered maintenance to ensure the safety and performance of critical medical devices while also addressing sustainability aspects like energy efficiency and responsible disposal. The processor (202) may monitor and maintain critical medical devices, ensuring their reliability and energy efficiency. This may lead to improved patient care, reduced equipment downtime, and a lower environmental impact on healthcare facilities.
[0067] In an embodiment, the processor (202) may optimize the maintenance of factory equipment, machinery, and supply chain logistics. This can result in increased productivity, reduced downtime, and enhanced sustainability through energy conservation and waste reduction.
[0068] In an embodiment, the processor (202) may be integrated with urban infrastructure like street lights, traffic signals, and public transportation. This may provide predictive maintenance, resource optimization, and data-driven decision-making to create more efficient and environmentally friendly cities.
[0069] In an embodiment, the processor (202), through the harmony framework's adaptable nature, may allow precision agriculture, optimizing the maintenance of farming equipment and sensors. This may lead to improved crop yields, reduced resource consumption, and more sustainable agricultural practices.
[0070] In an embodiment, the processor (202), through the harmony framework may be integrated with building management systems, optimizing Heating, Ventilation, and Air Conditioning (HVAC) maintenance, lighting, and other building systems. This may result in energy savings, better indoor air quality, and reduced environmental impact for commercial and residential buildings.
[0071] In an embodiment, the processor (202) may be communicatively coupled to an energy management module that optimizes the power consumption of IoT devices, minimizing their environmental impact through intelligent scheduling and load balancing. The processor (202) may provide a user-friendly dashboard and reporting interface that enables stakeholders to monitor system performance, track maintenance activities, and analyze sustainability metrics. Further, the processor (202) may enable remote diagnostics and predictive maintenance capabilities, allowing for proactive issue resolution, and reduced on-site service requirements. The processor (202) may provide seamlessly integration with existing enterprise asset management, building management, and smart city platforms to incorporate maintenance and sustainability features.
[0072] In an embodiment, the processor (202), through the harmony framework may monitor and maintain wind turbines, solar panels, and energy storage devices. This may enhance the reliability and efficiency of renewable energy infrastructure, supporting the transition to a more sustainable energy future. The harmony framework's integrated maintenance approach may include significant potential to benefit both industry and consumers. For industries, the harmony framework may optimize the performance, reliability, and sustainability of IoT devices, leading to increased productivity, reduced downtime, and enhanced environmental responsibility. Consumers may also indirectly benefit through improved product quality, energy-efficient operations, and a smaller carbon footprint. Overall, the harmony framework may offer a comprehensive and customizable solution that may drive innovation and environmental stewardship across a wide range of applications.
[0073] For example, in an embodiment, in a health care use case with adaption to a micro-infusion pump, the micro-infusion pump components may be constructed using biocompatible materials, such as medical-grade plastics, stainless steel, and silicone, to ensure safe and long-term contact with the patient's body. The micro-infusion pump may leverage a high-precision stepper motor and an advanced flow control technique to deliver medication or therapeutic fluids at accurately regulated flow rates, enabling precise dosage administration. The micro-infusion pump may include wireless communication capabilities, allowing the micro-infusion pump to transmit real-time data on flow rates, pressure, and pump status to a central monitoring system (108). This may enable remote patient monitoring and medication delivery adjustment as needed. The micro-infusion pump may integrate with the harmony framework predictive maintenance techniques, which analyze sensor data and historical performance to anticipate potential device failures or degradation. This may allow for proactive maintenance and minimizes the risk of unplanned interruptions to patient treatment. Further, the micro-infusion pump may incorporate power-efficient design principles, such as low-power microcontrollers and adaptive duty-cycling, to extend the battery life and reduce the frequency of battery replacements, improving the overall user experience and reducing healthcare waste.
[0074] In an embodiment, the processor (202) may identify an anomaly from the information recorded by the one or more devices (102). The processor (202) may instantaneously generate, one or more artifacts associated with the one or more devices (102) based on the identified anomaly.
[0075] In an embodiment, the processor (202) may analyze the generated one or more artifacts and generates a schedule associated with the maintenance of the one or more devices based on the identified anomaly. Further, the processor (202) may predict, via the machine learning engine (214), one or more performance metrics associated with the one or more devices (102) based on the identified anomaly and the analyzed one or more artifacts. To monitor the performance and condition of the predetermined number of devices, the processor (202) may monitor a specific parameter or parameters such as but not limited to temperature, voltage, current, signal strength, or measurement readings. For example, when a sensor (of the one or more sensors) exceeds a predefined threshold in one of these parameters, this may indicate that the sensor is malfunctioning or has failed.
[0076] In an embodiment, for analyzing the one or more artifacts, the processor (202) may determine real-time data associated with the generated one or more artifacts, analyze the real-time data, and predict via the machine learning engine (214), a non-functional state of the predetermined number of devices for one or more time periods based on the analyzed real-time data.
[0077] In an embodiment, the processor (202) may subsequently optimize the generated schedule based on the analyzed real-time data and enable maintenance of the predetermined number of devices for a predetermined period. To optimize the generated schedule the processor (202) may be configured to enable recording by the predetermined number of devices based on a functional-state of the predetermined devices predicted by the machine learning engine (214).
[0078] In an embodiment, for predicting the one or more performance metrics, the processor (202) may determine that the predetermined number of devices are in the non-functional state based on one or more unplanned interruptions and generate an alert associated with the one or more artifacts based on the non-functional state of the predetermined number of devices. For example, the one or more unplanned interruptions may include a sudden loss of power to the predetermined number of devices causing loss of data. The one or more unplanned interruptions may include interruptions in communication networks preventing data transmission to the processor (202). Further, the one or more unplanned interruptions may include failure of the predetermined number of devices due to physical defects, wear and tear, or environmental factors like temperature extremes or humidity. Errors occurred during the setup, calibration, or operation of the predetermined number of devices may further lead to unexpected disruptions in the monitoring process.
[0079] In an embodiment, the processor (202) may integrate quantitative and qualitative factors enables a better understanding of the strengths and limitations of each maintenance strategy, leading to more informed decisions and targeted improvements. The processor (202) may determine resource efficiency by minimizing resource consumption and waste. The processor (202) may consider determine monitoring by tracking key performance indicators. The processor (202) may determine predictive analytics for anticipating and mitigating potential issues. The processor (202) may determine IoT connectivity ensuring real-time data sharing between devices. The processor (202) may enable data centralization for aggregating data for comprehensive analysis. The processor (202) may consider environmental impact by tracking and reducing environmental impact. The processor (202) may ensure redundancy and resilience through system reliability. The processor (202) may enable failure analysis by understanding and preventing failures. The processor (202) may provide maintenance optimization by implementing optimized maintenance schedules. The processor (202) may provide carbon footprint reduction by minimizing carbon footprints. The processor (202) may provide cybersecurity threat monitoring for intrusion detection and monitoring. Hence, the processor (202) may collectively create a more sustainable and efficient IoT maintenance framework that maximizes the longevity and performance of IoT devices while minimizing environmental impact and operational costs. Further, the processor (202) provides a holistic approach that addresses the challenges associated with the rapid proliferation of IoT devices and implements effective maintenance practices that balance operational efficiency, environmental impact, and cost-effectiveness.
[0080] In an embodiment, the processor (202) may determine one or more greenhouse gas emissions associated with the non-functional state of the predetermined number of devices. Further, the processor (202) may utilize the predetermined number of devices based on the functional-state of the predetermined number of devices for recording of the information and minimizing power consumption during the optimized schedule. The processor (202) may determine malfunctioning of at least a device among the predetermined number of devices emitting the one or more greenhouse gas emissions. Hence, the processor (202) may provide sustainability-centered maintenance, where deviations from the defined carbon footprint thresholds trigger maintenance actions, even if the device remains operational. This addresses environmental impacts proactively, integrating, sustainability-centered maintenance as a critical operational consideration.
[0081] FIG. 3 illustrates an example flow diagram (300) of the proposed system (108), in accordance with an embodiment of the present disclosure.
[0082] As illustrated in FIG. 3, at step 302, the method may include receiving, by a system (108), data associated one or more entities from a centralized server (104), where the data may include information associated with the one or more entities recorded by one or more devices (102). At step 304, the method may include identifying, by the processor (202), an anomaly from the information recorded by the one or more devices (102). At step 306, the method may include instantaneously generating, by the processor (202), one or more artifacts associated with the one or more devices (102) based on the identified anomaly. At step 308, the method may include analyzing, by the processor (202) the generated one or more artifacts and generating a schedule associated with the maintenance of the one or more devices based on the identified anomaly. At step 310, the method may include predicting, by the processor (202), via a machine learning engine (214), one or more performance metrics associated with the one or more devices (102) based on the identified anomaly and the analyzed one or more artifacts.
[0083] FIG. 4 illustrates an example flow diagram (400) of Internet of Things (IoT) system usability by the proposed system (108), in accordance with an embodiment of the present disclosure.
[0084] In an embodiment, an end user (402) may interact at may monitor and control the IoT system depending on their role, device-level adjustments, local (edge) coordination, regional-level fleet management, and enterprise-level sustainability insights.
[0085] In an embodiment, at the device level, the system (108) may provide localized real-time decision-making with ML models. The system (108) may detect anomalies, optimize battery usage, and tracks carbon footprint.
[0086] In an embodiment, at the edge level, the system (108) may aggregate data from multiple devices. The system (108) may handle fleet-wide optimizations at a small location level.
[0087] In an embodiment, at the regional level, the system (108) may manage compliance, fleet monitoring, and cross-location sustainability.
[0088] In an embodiment, at the cloud level, the system (108) may provide long-term analytics and predictive maintenance for the entire ecosystem. Further, the system (108) may support enterprise-wide decision-making and sustainability reporting.
[0089] As illustrated in Fig. 4, in an embodiment, the IoT device (404) may receive sensor data through a standalone device level (406) including interface adapters (408) and a core domain layer (410). Further, the system (108) may provide local processing of the information provided the IoT device (404) using the hardware interface (412).
[0090] In an embodiment, the IoT device (404) may include an edge level data aggregation (414) of the information received from the IoT device (404).
[0091] In an embodiment, the IoT device (404) may include regional level (fleet-wide optimization) (416) of the processed information from the edge level data aggregation (414).
[0092] In an embodiment, the IoT device (404) may include cloud level processing co-ordination (enterprise co-ordination) (418) of data received from the regional level (fleet-wide optimization) (416) through analysis of the processed information. The end user (402) may receive insights (420) through the analysis of the processed information.
[0093] In an embodiment, the end user (402) may access the device (424) through the interface adapters (408), manage local devices through the edge level data aggregation (414), monitor regions through the regional level (fleet-wide optimization) (416), and generate enterprise insights (422) through the cloud level processing co-ordination (enterprise co-ordination) (418).
[0094] In an embodiment, the edge level data aggregation (414) may be provided by an edge-processing unit (426). The edge-processing unit (426) manages data (428) through multi-device co-ordination (430) based on run aggregated ML models (432) processed by edge AI models (434) and further aggregates carbon data (436) through a carbon fleet aggregator (438).
[0095] In an embodiment, for fleet wide optimization (416), a regional AI cluster (440) receives fleet wide analytics through the edge-processing unit (426) and sends large scale data (442). Further, the regional AI cluster (440) monitors devices (444) through fleet monitoring (446), performs regulatory checks (448) through regulatory compliance (450), and optimizes sustainability metrics (452) through sustainability metrics (454).
[0096] In an embodiment, a cloud data lake (456) associated with the cloud level enterprise co-ordination (418) receives the large scale data (442) and optimizes energy use (458) through sustainability analytics (460) and further trains advance models (462) using long-term AI model training (464).
[0097] Hence, the system (108) may provide user-centric interaction where the user can operate at any level, ensuring flexibility based on their role or access level. The system (108) may also provide scalability that supports single-device operations (standalone) to global enterprise-level management. The system (108) may provide sustainability integration and track carbon footprint and optimizes energy usage at all levels. The system (108) may include a modular hexagonal architecture that ensures that each level of the IoT system operates independently but can seamlessly integrate into larger systems.
[0098] While considerable emphasis has been placed herein on the preferred embodiments, it will be appreciated that many embodiments can be made and that many changes can be made in the preferred embodiments without departing from the principles of the disclosure. These and other changes in the preferred embodiments of the disclosure will be apparent to those skilled in the art from the disclosure herein, whereby it is to be distinctly understood that the foregoing descriptive matter is to be implemented merely as illustrative of the disclosure and not as a limitation.

ADVANTAGES OF THE INVENTION
[0099] The present disclosure provides comprehensive optimization and addresses a holistic set of factors, including resource efficiency, performance monitoring, predictive analytics, and environmental impact, providing a more balanced and optimized approach to Internet of Things (IoT) maintenance. These results in increased operational efficiency, enhanced reliability, and reduced environmental footprints.
[00100] The present disclosure provides proactive maintenance through data analytics and predictive capabilities, which enable a more proactive and preventive approach to maintenance, anticipating and mitigating potential issues before they occur, improving system uptime, reducing unplanned downtime, and extending the overall lifespan of IoT devices.
[00101] The present disclosure provides sustainability by addressing the environmental impact and resource consumption challenges associated with the proliferation of IoT devices. This leads to reduced electronic waste, a lower carbon footprint, and more efficient resource utilization, making the maintenance of IoT systems more environmentally responsible.
[00102] The present disclosure provides an integrated approach and offers a more comprehensive and integrated solution by seamlessly combining various maintenance methodologies, including condition-based monitoring, reliability-centered maintenance, and predictive analytics. This holistic approach addresses the shortcomings of previous standalone strategies, resulting in greater operational resilience and optimization.
[00103] The present disclosure provides sustainability-centered maintenance, where deviations from the defined carbon footprint thresholds trigger maintenance actions, even if the device remains operational. This addresses environmental impacts proactively, integrating, sustainability-centered maintenance as a critical operational consideration.
, Claims:1. An integrated system (108) for maintenance of devices, comprising:
a processor (202); and
a memory (204) operatively coupled with the processor (202), wherein said memory (204) stores instructions which, when executed by the processor (202), cause the processor (202) to:
receive data associated one or more entities from a centralized server (104), wherein the data comprises information associated with the one or more entities recorded by one or more devices (102);
identify an anomaly from the information recorded by the one or more devices (102);
instantaneously generate one or more artifacts associated with the one or more devices (102) based on the identified anomaly;
analyze the one or more artifacts and generate a schedule associated with the maintenance of the one or more devices (102) based on the identified anomaly; and
predict, via a machine learning engine (214), one or more performance metrics associated with the one or more devices (102) based on the identified anomaly and the analyzed one or more artifacts.
2. The system (108) as claimed in claim 1, wherein to predict the one or more performance metrics, the processor (202) is configured to:
determine that a predetermined number of devices of the one or more devices (102) are in a non-functional state based on one or more unplanned interruptions; and
subsequently generate an alert associated with the one or more artifacts based on the non-functional state of the predetermined number of devices.
3. The system (108) as claimed in claim 2, wherein to analyze the one or more artifacts, the processor (202) is configured to:
determine real-time data associated with the generated one or more artifacts;
analyze the real-time data; and
predict, via the machine learning engine (214), the non-functional state of the predetermined number of devices for one or more time periods based on the analyzed real-time data.
4. The system (108) as claimed in claim 3, wherein the processor (202) is configured to:
subsequently optimize the generated schedule based on the analyzed real-time data; and
enable maintenance of the predetermined number of devices for a predetermined period,
wherein to optimize the generated schedule, the processor (202) is configured to enable recording by the predetermined number of devices based on a functional-state of the predetermined number of devices predicted by the machine learning engine (214).
5. The system (108) as claimed in claim 4, wherein the processor (202) is configured to:
determine one or more greenhouse gas emissions associated with the non-functional state of the predetermined number of devices; and
utilize the predetermined number of devices based on the functional-state of the predetermined number of devices for recording of the information and minimizing power consumption during the optimized schedule;
determine malfunctioning of at least a device among the predetermined number of devices emitting the one or more greenhouse gas emissions
6. A method (300) for maintenance of devices, the method (300) comprising:
receiving (302), by a processor (202) associated with a system (108), data associated one or more entities from a centralized server (104), wherein the data comprises information associated with the one or more entities recorded by one or more devices (102);
identifying, by the processor (202), an anomaly from the information recorded by the one or more devices (102);
instantaneously generating, by the processor (202), one or more artifacts associated with the one or more devices (102) based on the identified anomaly;
analyzing, by the processor (202), the one or more artifacts and generating a schedule associated with the maintenance of the one or more devices (102) based on the identified anomaly; and
predicting, by the processor (202), via a machine learning engine (214), one or more performance metrics associated with the one or more devices (102) based on the identified anomaly and the analyzed one or more artifacts.
7. The method (300) as claimed in claim 6, wherein for predicting the one or more performance metrics, the method comprises, determining, by the processor (202), that a predetermined number of devices are in a non-functional state based on one or more unplanned interruptions and subsequently generating an alert associated with the one or more artifacts based on the non-functional state of the predetermined number of devices.
8. The method (300) as claimed in claim 7, wherein for analyzing the one or more artifacts, the method comprises determining, by the processor (202), real-time data associated with the generated one or more artifacts, analyzing the real-time data, and predicting via the machine learning engine, the non-functional state of the predetermined number of devices for one or more time periods based on the analyzed real-time data.
9. The method (300) as claimed in claim 8, comprising subsequently optimizing, by the processor (202), the generated schedule based on the analyzed real-time data and enabling maintenance of the predetermined number of devices for a predetermined period, wherein the optimization of the generated schedule comprises recording by the processor (202) through the predetermined number of devices based on a functional-state of the predetermined devices predicted by the machine learning engine (214).
10. The method (300) as claimed in claim 9, comprising determining, by the processor (202), one or more greenhouse gas emissions associated with the non-functional state of the predetermined number of devices and utilizing, by the processor (202), the predetermined number of devices based on the functional-state of the predetermined number of devices for recording of the information and minimizing power consumption during the optimized schedule, determining malfunctioning of at least a device among the predetermined number of devices emitting the one or more greenhouse gas emissions.