EDGE-ENABLED AI APPLICATION FRAMEWORK FOR CYBER-PHYSICAL SYSTEMS

Abstract

Edge-Enabled AI Application Framework for Cyber-Physical Systems TECHNICAL FIELD [0001] The present innovation relates to an edge-enabled application framework for deploying AI in cyber-physical systems (CPS) to optimize real-time processing and system performance. BACKGROUND [0002] In recent years, the rise of connected devices and the Internet of Things (IoT) has led to the proliferation of cyber-physical systems (CPS) across industries like manufacturing, healthcare, and transportation. CPS integrate physical processes with computational algorithms and network connectivity, relying on real-time data processing to enable intelligent decision-making. Traditionally, these systems have depended on cloud computing for data analysis and processing. However, cloud-based architectures face significant challenges, including high latency, bandwidth limitations, and data privacy concerns. In applications like autonomous vehicles or industrial automation, relying on the cloud can cause critical delays, compromising performance and safety. Additionally, the growing volume of data generated by edge devices further strains bandwidth and makes centralized processing inefficient. [0003] Edge computing has emerged as an alternative, allowing data to be processed closer to the source, which reduces latency and improves response times. However, integrating artificial intelligence (AI) with edge devices poses its own challenges. AI algorithms are often resource-intensive, and edge devices, which are typically resource-constrained, struggle to handle such computational loads efficiently. Existing solutions for deploying AI on the edge are either too limited in their functionality or too complex to integrate into diverse environments. They often lack scalability, interoperability, and robust security measures. [0004] The Edge-Enabled AI Application Framework differentiates itself by providing a unified solution that addresses these issues. It enables real-time processing at the edge, minimizes reliance on cloud resources, and ensures secure, efficient AI deployment even on resource-constrained devices. The framework’s novelty lies in its hybrid processing model, adaptive AI optimization, and context-aware decision-making. It also offers scalability, interoperability, and self-healing capabilities, overcoming the limitations of existing edge computing and AI integration systems while ensuring better performance, security, and reliability in CPS. OBJECTS OF THE INVENTION [0005] The primary object of the invention is to enhance the real-time processing capabilities of cyber-physical systems (CPS) by leveraging edge computing for AI deployment. [0006] Another object of the invention is to reduce data latency and bandwidth usage by processing data at the edge, minimizing reliance on cloud-based resources. [0007] Another object of the invention is to improve the security and privacy of CPS by performing localized data processing, reducing the need for data transmission to the cloud. [0008] Another object of the invention is to support the efficient deployment and management of AI applications on resource-constrained edge devices, optimizing performance without overloading system resources. [0009] Another object of the invention is to enable interoperability between diverse hardware and software components, ensuring seamless integration within existing CPS environments. [00010] Another object of the invention is to offer scalability, allowing for both horizontal expansion by adding edge devices and vertical enhancement of existing devices as system requirements grow. [00011] Another object of the invention is to provide adaptive AI optimization, enabling edge devices to adjust algorithm complexity based on available computational resources. [00012] Another object of the invention is to improve system reliability and uptime by incorporating self-healing capabilities, allowing edge devices to autonomously detect and recover from faults. [00013] Another object of the invention is to enable context-aware decision-making in CPS, allowing edge devices to make informed decisions based on environmental factors and situational awareness. [00014] Another object of the invention is to offer a hybrid processing model that intelligently balances computational workloads between edge devices and the cloud, ensuring optimal performance and resource efficiency SUMMARY OF THE INVENTION [00015] In accordance with the different aspects of the present invention, edge-enabled AI application framework for cyber-physical systems is presented. It optimizes real-time data processing and decision-making using AI on edge devices. It includes a communication layer for seamless data transfer, a cloud layer for advanced analytics, and modules for managing AI models and dynamic resource allocation. The system ensures security, real-time anomaly detection, and context-aware decision-making. Additionally, it integrates multi-modal data, supports decentralized learning, and provides user interaction and compliance monitoring. The framework enhances system performance, adaptability, and responsiveness in various environments. [00016] Additional aspects, advantages, features and objects of the present disclosure would be made apparent from the drawings and the detailed description of the illustrative embodiments constructed in conjunction with the appended claims that follow. [00017] It will be appreciated that features of the present disclosure are susceptible to being combined in various combinations without departing from the scope of the present disclosure as defined by the appended claims. BRIEF DESCRIPTION OF DRAWINGS [00018] The summary above, as well as the following detailed description of illustrative embodiments, is better understood when read in conjunction with the appended drawings. For the purpose of illustrating the present disclosure, exemplary constructions of the disclosure are shown in the drawings. However, the present disclosure is not limited to specific methods and instrumentalities disclosed herein. Moreover, those in the art will understand that the drawings are not to scale. Wherever possible, like elements have been indicated by identical numbers. [00019] Embodiments of the present disclosure will now be described, by way of example only, with reference to the following diagrams wherein: [00020] FIG. 1 is component wise drawing for edge-enabled AI application framework for cyber-physical systems. [00021] FIG 2 is working methodology for edge-enabled AI application framework for cyber-physical systems. DETAILED DESCRIPTION [00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible. [00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of edge-enabled AI application framework for cyber-physical systems and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention. [00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure. [00025] The terms “comprises”, “comprising”, “include(s)”, or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by “comprises... a” does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus. [00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense. [00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail. [00028] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of edge devices 102, communication layer 104, cloud layer 106, AI model deployment module 108, data aggregation module 110, security module 112, real-time anomaly detection module 114, adaptive ai optimization module 116, interoperability protocols 118, energy management system 120, self-healing module 122, resource allocation engine 124, context-aware decision-making module 126, decentralized learning mechanism 128, multi-modal data fusion module 130, user interface module 132, compliance monitoring module 134, collaborative ai model sharing platform 136, smart data retention policy module 138, user feedback loop 140 and geospatial awareness module 142. [00029] Referring to Fig. 1, the present disclosure provides details of edge-enabled AI application framework for cyber-physical systems 100 is designed to optimize AI deployment and real-time processing on resource-constrained edge devices 102 while maintaining cloud integration 106. It features adaptive AI model deployment 108, decentralized learning 128, and real-time anomaly detection 114 to enhance decision-making and system reliability. The communication layer 104 ensures low-latency data transfer between edge and cloud, while the security module 112 protects data privacy. The framework is highly scalable, offering interoperability protocols 118 for seamless integration across diverse devices, and includes energy management 120 and self-healing capabilities 122 for efficient operation and system robustness. [00030] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with edge devices 102, which collect and process real-time data from sensors and actuators within the system. These edge devices 102 run lightweight AI algorithms that allow for data analysis, anomaly detection, and decision-making at the source. They interact closely with the communication layer 104 to transmit relevant data to the cloud layer 106 for further processing or storage when needed. Edge devices 102 ensure that only critical information requiring deeper analysis is sent to the cloud, optimizing bandwidth usage. [00031] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the communication layer 104, which facilitates data exchange between edge devices 102 and the cloud layer 106. This layer uses optimized protocols to reduce latency and increase throughput, enabling seamless integration between localized edge processing and centralized cloud resources. The communication layer 104 works in tandem with the data aggregation module 110 to compress and streamline data before transmission, ensuring efficient bandwidth use and faster processing across the system. [00032] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the cloud layer 106, which serves as a central repository for data storage, advanced analytics, and complex computational tasks. While edge devices 102 handle real-time decision-making, the cloud layer 106 supports resource-intensive processes like AI model training and historical data analysis. The cloud layer 106 dynamically interacts with the resource allocation engine 124 to distribute workloads, balancing the computational effort between edge and cloud for optimal system performance. [00033] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the AI model deployment module 108, which manages the installation, updating, and optimization of AI models on edge devices 102. This module ensures that AI algorithms are tailored to the computational limitations of edge devices 102, allowing them to operate efficiently in resource-constrained environments. The AI model deployment module 108 also integrates with the adaptive AI optimization module 116 to fine-tune models based on real-time feedback from edge devices 102. [00034] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the data aggregation module 110, which collects and compacts data from multiple edge devices 102 before transmitting it to the cloud layer 106. This module plays a crucial role in reducing bandwidth usage by compressing data and eliminating redundancies. The data aggregation module 110 works closely with the communication layer 104 to ensure that only essential and aggregated data is transferred, improving the overall efficiency of the system. [00035] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the security module 112, which ensures the protection of data both at the edge and during transmission to the cloud. This module employs encryption techniques and secure authentication mechanisms to safeguard sensitive information collected by edge devices 102. The security module 112 works in conjunction with the communication layer 104 to maintain data integrity and prevent unauthorized access during data exchange. By handling data locally on edge devices 102, the security module 112 reduces the risk of breaches, enhancing privacy. [00036] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the real-time anomaly detection module 114, which continuously monitors data streams from edge devices 102 for unusual patterns or behaviors. This module leverages AI algorithms to detect anomalies as they occur, enabling swift corrective actions. The real-time anomaly detection module 114 works closely with the AI model deployment module 108 to refine the models based on new data and anomalies detected. By operating directly at the edge, this module ensures timely responses to critical events, improving overall system safety and efficiency. [00037] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the adaptive AI optimization module 116, which dynamically adjusts AI models based on available computational resources at the edge. This module enables the system to optimize performance under varying operational conditions by simplifying or enhancing AI algorithms depending on the current capacity of edge devices 102. The adaptive AI optimization module 116 interacts with the AI model deployment module 108 to ensure that AI models remain efficient without sacrificing accuracy. [00038] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with interoperability protocols 118, which allow seamless communication between heterogeneous edge devices 102 and cloud layer 106. These protocols ensure that devices with different hardware and software configurations can work together within the same framework. Interoperability protocols 118 work in tandem with the communication layer 104 to manage data exchange across various components, ensuring that the system remains flexible and adaptable as new devices are introduced or upgraded. [00039] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the energy management system 120, which monitors and optimizes the power consumption of edge devices 102. This system is especially important for battery-operated or remote devices, where energy efficiency is critical. The energy management system 120 dynamically adjusts processing tasks based on the device’s power levels and operational demands, working closely with the resource allocation engine 124 to balance workloads in a way that conserves energy without compromising performance. [00040] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the self-healing module 122, which allows edge devices 102 to detect and recover from faults autonomously. This module continuously monitors system health and can initiate corrective actions without human intervention. The self-healing module 122 works in conjunction with the real-time anomaly detection module 114 to detect potential failures before they escalate, ensuring high system reliability and uptime. [00041] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the resource allocation engine 124, which dynamically distributes computational tasks between edge devices 102 and the cloud layer 106 based on real-time system conditions. This engine optimizes resource usage by assigning tasks that require immediate attention to edge devices 102 while delegating resource-intensive processes to the cloud layer 106. The resource allocation engine 124 works closely with the communication layer 104 to ensure that data is efficiently routed between the edge and cloud. [00042] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the context-aware decision-making module 126, which enables edge devices 102 to make informed decisions based on real-time environmental data and situational awareness. This module uses data collected from sensors and external inputs to adapt decisions according to the current context. The context-aware decision-making module 126 works closely with the AI model deployment module 108 and adaptive AI optimization module 116 to ensure that edge devices 102 respond accurately and effectively to changing conditions. [00043] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the decentralized learning mechanism 128, which enables edge devices 102 to participate in federated learning without sharing raw data. This mechanism ensures data privacy by transmitting only model updates to the cloud layer 106 for aggregation and improvement. The decentralized learning mechanism 128 interacts with the security module 112 to ensure secure transmission of model updates, enhancing privacy and reducing the risk of data breaches. [00044] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the multi-modal data fusion module 130, which integrates diverse data types, such as sensor data and video feeds, to provide more accurate insights. This module combines inputs from various edge devices 102 to enhance the overall decision-making process. The multi-modal data fusion module 130 works closely with the real-time anomaly detection module 114 to identify complex patterns and enhance the accuracy of AI-driven decisions. [00045] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the user interface module 132, which offers customizable interactions for end-users to configure, monitor, and control system operations. The user interface module 132 is designed to be intuitive and adaptable, allowing users to personalize settings based on specific application needs. This module works closely with the context-aware decision-making module 126 and the feedback loop 140 to offer real-time updates and insights to users, enhancing their control over system functions. [00046] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the compliance monitoring module 134, which ensures that all data processing and transmission activities adhere to relevant regulatory standards, such as GDPR or HIPAA. This module continuously monitors system operations to ensure compliance and prevents violations by adjusting workflows as necessary. The compliance monitoring module 134 works in conjunction with the security module 112 to maintain data privacy and integrity across edge devices 102 and the cloud layer 106. [00047] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the collaborative AI model sharing platform 136, which facilitates cross-organizational learning and innovation. This platform allows users to share AI models and algorithms, promoting the reuse of successful models across different environments. The collaborative AI model sharing platform 136 interacts with the AI model deployment module 108 and decentralized learning mechanism 128 to enable a collaborative ecosystem that accelerates the development of AI applications. [00048] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the smart data retention policy module 138, which dynamically adjusts data storage strategies based on regulatory requirements and usage patterns. This module ensures that data is retained for appropriate durations and is disposed of securely when no longer needed. The smart data retention policy module 138 works closely with the compliance monitoring module 134 to ensure adherence to data lifecycle regulations and optimize storage management. [00049] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the user feedback loop 140, which allows end-users to provide real-time input on the system's performance and AI application accuracy. This feedback is continuously used to refine AI models and improve system responsiveness. The user feedback loop 140 interacts closely with the AI model deployment module 108 and adaptive AI optimization module 116 to incorporate user preferences and enhance the overall functionality of the system. [00050] Referring to Fig. 1, edge-enabled AI application framework for cyber-physical systems 100 is provided with the geospatial awareness module 142, which integrates location-based data and analytics to enhance decision-making. This module allows edge devices 102 to incorporate geographic context, making it especially useful in applications like logistics and transportation. The geospatial awareness module 142 works closely with the context-aware decision-making module 126 to provide accurate, location-specific insights that optimize system performance in diverse environments [00051] Referring to Fig 2, there is illustrated method 200 for edge-enabled AI application framework for cyber-physical systems 100. The method comprises: At step 202, method 200 includes edge devices 102 collecting real-time data from sensors and actuators within the cyber-physical system; At step 204, method 200 includes edge devices 102 processing the collected data using lightweight AI algorithms to perform tasks such as anomaly detection and decision-making; At step 206, method 200 includes communication layer 104 transmitting processed data from the edge devices 102 to the cloud layer 106 using optimized low-latency protocols; At step 208, method 200 includes cloud layer 106 performing advanced analytics and storing data that requires more computational power or longer-term analysis; At step 210, method 200 includes AI model deployment module 108 updating or deploying AI models to the edge devices 102 based on real-time data and cloud computations; At step 212, method 200 includes adaptive AI optimization module 116 dynamically adjusting the complexity of AI algorithms on edge devices 102 based on available resources and system performance; At step 214, method 200 includes security module 112 encrypting and securing the data being transmitted between edge devices 102 and cloud layer 106 to maintain data privacy and integrity; At step 216, method 200 includes real-time anomaly detection module 114 continuously monitoring data streams for unusual patterns or behaviors across the edge devices 102 and triggering corrective actions when necessary; At step 218, method 200 includes resource allocation engine 124 dynamically distributing computational tasks between edge devices 102 and cloud layer 106 to optimize overall system performance based on workload and system conditions; At step 220, method 200 includes context-aware decision-making module 126 enabling edge devices 102 to make informed decisions based on localized data and environmental factors; At step 222, method 200 includes energy management system 120 monitoring power usage of the edge devices 102 and dynamically adjusting processing tasks to conserve energy and optimize performance; At step 224, method 200 includes decentralized learning mechanism 128 enabling edge devices 102 to participate in federated learning, updating models based on local data without sharing sensitive information with the cloud layer 106; At step 226, method 200 includes multi-modal data fusion module 130 integrating diverse data types from various edge devices 102 (such as sensor data, video feeds) to enhance decision-making accuracy and insight generation; At step 228, method 200 includes user interface module 132 enabling end-users to interact with the system, configure settings, and receive real-time updates on system performance and AI model operations; At step 230, method 200 includes compliance monitoring module 134 ensuring that all data processing and transmission activities adhere to relevant regulations such as GDPR or HIPAA, maintaining secure and lawful operations; At step 232, method 200 includes collaborative AI model sharing platform 136 allowing users to share and reuse AI models and algorithms across different edge devices 102 and environments, promoting collaboration and innovation; At step 234, method 200 includes smart data retention policy module 138 dynamically adjusting data storage and retention strategies based on usage patterns and regulatory requirements, optimizing storage resources and compliance; At step 236, method 200 includes user feedback loop 140 collecting real-time feedback from users regarding the system’s performance, refining AI models and system operations based on user inputs; At step 238, method 200 includes geospatial awareness module 142 incorporating location-based data into the decision-making process, enabling edge devices 102 to consider geographic context for enhanced system performance, particularly in applications such as logistics and transportation. [00052] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms “fixed” “attached” “disposed,” “mounted,” and “connected” are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art. [00053] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as “including”, “comprising”, “incorporating”, “have”, “is” used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate. [00054] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.

Specification

Description:ABSTRACT
Edge-Enabled AI Application Framework for Cyber-Physical Systems
The present disclosure introduces edge-enabled AI application framework for cyber-physical systems 100 that optimizes real-time data processing and decision-making using edge devices 102 equipped with AI algorithms. The system features a communication layer 104 for low-latency data transmission and cloud layer 106 for advanced analytics. AI models are managed by the AI model deployment module 108, and the adaptive AI optimization module 116 adjusts model complexity. The data aggregation module 110 collects data. The other components of the invention security module 112, real-time anomaly detection module 114, interoperability protocols 118,energy management system 120, self-healing module 122, resource allocation engine 124, context-aware decision-making module 126, decentralized learning mechanism 128, multi-modal data fusion module 130, user interface module 132, compliance monitoring module 134, collaborative ai model sharing platform 136, smart data retention policy module 138, user feedback loop 140 and geospatial awareness module 142.
Reference Fig 1
, Claims:WE CLAIM:
1. An edge-enabled AI application framework for cyber-physical systems 100 comprising of
edge devices 102 to collect and process real-time data from sensors and actuators within the system;
communication layer 104 to transmit data between edge devices and the cloud with optimized low-latency protocols;
cloud layer 106 to perform advanced data analytics and provide storage for computationally intensive tasks;
AI model deployment module 108 to manage and deploy AI models on edge devices based on real-time needs;
data aggregation module 110 to collect and compress data from multiple edge devices for efficient transmission;
security module 112 to encrypt and secure data during transmission between edge and cloud systems;
real-time anomaly detection module 114 to continuously monitor data streams for unusual patterns and trigger corrective actions;
adaptive AI optimization module 116 to dynamically adjust AI algorithms based on edge device resources and conditions;
interoperability protocols 118 to ensure seamless communication and integration between heterogeneous devices and systems;
energy management system 120 to monitor and optimize power consumption of edge devices;
self-healing module 122 to detect faults and autonomously recover edge devices from system failures;
resource allocation engine 124 to distribute computational tasks between edge and cloud layers based on performance demands;
context-aware decision-making module 126 to enable informed decisions based on localized data and environmental context;
decentralized learning mechanism 128 to allow edge devices to participate in federated learning while preserving data privacy;
multi-modal data fusion module 130 to integrate diverse data types from various sources for enhanced decision-making;
user interface module 132 to provide customizable user interactions and real-time updates on system performance;
compliance monitoring module 134 to ensure adherence to data regulations during system operations;
collaborative AI model sharing platform 136 to enable users to share and reuse AI models across different environments;
smart data retention policy module 138 to dynamically manage data storage and retention strategies based on regulatory requirements;
user feedback loop 140 to collect real-time feedback from users and refine system performance and AI models; and
geospatial awareness module 142 to incorporate location-based data into decision-making for enhanced system performance.


2. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein edge devices 102 are configured to collect real-time data from sensors and actuators, perform local processing using lightweight AI algorithms, and make decisions based on the localized data.

3. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein the communication layer 104 is configured to transmit data between edge devices and the cloud layer using low-latency protocols, ensuring seamless integration and minimizing bandwidth usage.

4. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein the cloud layer 106 is configured to perform advanced analytics, store long-term data, and support computationally intensive tasks such as AI model training and analysis of historical data.

5. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein the AI model deployment module 108 is configured to manage the deployment, updating, and optimization of AI models on resource-constrained edge devices, allowing for continuous operation and real-time decision-making.

6. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein the adaptive AI optimization module 116 is configured to dynamically adjust the complexity of AI algorithms based on the available resources of the edge devices, ensuring optimal performance without overloading the system.

7. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein the real-time anomaly detection module 114 is configured to continuously monitor data streams from the edge devices, detect unusual patterns or behaviors, and trigger corrective actions to maintain system stability.

8. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein the resource allocation engine 124 is configured to dynamically distribute computational tasks between edge devices and the cloud layer, optimizing system performance based on real-time conditions and workload.

9. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein the context-aware decision-making module 126 is configured to enable edge devices to make informed decisions based on environmental data and situational awareness, ensuring responsive actions in dynamic environments.

10. The Edge-Enabled AI Application Framework for Cyber-Physical Systems 100 as claimed in claim 1, wherein method comprises of
edge devices 102 collecting real-time data from sensors and actuators within the cyber-physical system;
edge devices 102 processing the collected data using lightweight ai algorithms to perform tasks such as anomaly detection and decision-making;
communication layer 104 transmitting processed data from the edge devices 102 to the cloud layer 106 using optimized low-latency protocols;
cloud layer 106 performing advanced analytics and storing data that requires more computational power or longer-term analysis;
AI model deployment module 108 updating or deploying ai models to the edge devices 102 based on real-time data and cloud computations;
adaptive AI optimization module 116 dynamically adjusting the complexity of ai algorithms on edge devices 102 based on available resources and system performance;
security module 112 encrypting and securing the data being transmitted between edge devices 102 and cloud layer 106 to maintain data privacy and integrity;
real-time anomaly detection module 114 continuously monitoring data streams for unusual patterns or behaviors across the edge devices 102 and triggering corrective actions when necessary;
resource allocation engine 124 dynamically distributing computational tasks between edge devices 102 and cloud layer 106 to optimize overall system performance based on workload and system conditions;
context-aware decision-making module 126 enabling edge devices 102 to make informed decisions based on localized data and environmental factors;
energy management system 120 monitoring power usage of the edge devices 102 and dynamically adjusting processing tasks to conserve energy and optimize performance;
decentralized learning mechanism 128 enabling edge devices 102 to participate in federated learning, updating models based on local data without sharing sensitive information with the cloud layer 106;
multi-modal data fusion module 130 integrating diverse data types from various edge devices 102 (such as sensor data, video feeds) to enhance decision-making accuracy and insight generation;
user interface module 132 enabling end-users to interact with the system, configure settings, and receive real-time updates on system performance and ai model operations;
compliance monitoring module 134 ensuring that all data processing and transmission activities adhere to relevant regulations such as gdpr or hipaa, maintaining secure and lawful operations;
collaborative AI model sharing platform 136 allowing users to share and reuse ai models and algorithms across different edge devices 102 and environments, promoting collaboration and innovation;
smart data retention policy module 138 dynamically adjusting data storage and retention strategies based on usage patterns and regulatory requirements, optimizing storage resources and compliance;
user feedback loop 140 collecting real-time feedback from users regarding the system's performance, refining AI models and system operations based on user inputs; and
geospatial awareness module 142 incorporating location-based data into the decision-making process, enabling edge devices 102 to consider geographic context for enhanced system performance, particularly in applications such as logistics and transportation.

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