AI-ENHANCED SYSTEM FOR OPTIMIZED MACHINE-TO-MACHINE (M2M) COMMUNICATION

Abstract

ABSTRACT AI-ENHANCED SYSTEM FOR OPTIMIZED MACHINE-TO-MACHINE (M2M) COMMUNICATION The present disclosure introduces AI-enhanced system for optimized machine-to-machine (M2M) communication 100 designed to improve the efficiency, scalability, and security of interconnected devices. The system comprises of connected devices 102 for data collection, an AI processing unit 104 for machine learning-driven analytics, and a communication network 106 for seamless data exchange. The other key components are data management layer 108, edge computing capability 110, anomaly detection framework 112 , modular integration architecture 114, User-configurable AI models 116 , comprehensive security framework 118, predictive analytics engine 120 generates insights, real-time feedback mechanism 122 , collaborative decision-making framework 124, context-aware communication protocols 128, blockchain integration for data integrity 130, and customized alerts and notifications 138 to inform users. Resource optimization algorithms 126 and self-healing network capabilities 132, cross-domain compatibility 134, enhanced data visualization tools 136, and multi-tenancy architecture 140. Reference Fig 1

Specification

Description:AI-ENHANCED SYSTEM FOR OPTIMIZED MACHINE-TO-MACHINE (M2M) COMMUNICATION
TECHNICAL FIELD
[0001] The present innovation relates to an AI-enhanced system designed to optimize machine-to-machine (M2M) communication, enabling autonomous decision-making and efficient data exchange across interconnected devices.

BACKGROUND

[0002] Machine-to-Machine (M2M) communication has become a cornerstone of modern technology, facilitating data exchange between devices across industries such as healthcare, transportation, and smart cities. However, traditional M2M systems are often constrained by predefined rules and static logic, limiting their adaptability and responsiveness to dynamic environments. These systems can struggle with processing vast amounts of data generated by interconnected devices, leading to inefficiencies, delayed decision-making, and resource wastage. Moreover, many current M2M solutions rely on centralized control, which increases latency and bandwidth consumption, making them unsuitable for real-time applications that require immediate responses.

[0003] The available options for enhancing M2M communication include systems using basic data analytics or predefined automation rules. While these systems offer some improvements in managing data flow, they are unable to make autonomous decisions or adapt to changing conditions without human intervention. Additionally, these solutions lack scalability and flexibility, making them difficult to expand or modify for more complex or larger networks. Security concerns also remain, as traditional M2M systems do not have robust mechanisms to detect and mitigate cyber threats in real-time.


[0004] The invention of the AI-Enhanced System for Optimized Machine-to-Machine (M2M) Communication overcomes these drawbacks by integrating advanced artificial intelligence (AI) algorithms into the M2M framework. This system allows devices to autonomously analyze data, predict trends, and make real-time decisions without human input. The system's edge computing capabilities minimize latency and bandwidth issues, enabling faster response times. The modular architecture supports seamless scalability, allowing the integration of new devices and services without requiring significant changes to the existing infrastructure. Key features such as predictive analytics, anomaly detection, and autonomous decision-making differentiate the invention from current options. By introducing AI-driven intelligence, the invention enhances operational efficiency, security, and adaptability, making it a novel solution for the evolving needs of interconnected device networks

OBJECTS OF THE INVENTION

[0005] The primary object of the invention is to optimize machine-to-machine (M2M) communication by integrating advanced AI algorithms for enhanced decision-making and data analysis.

[0006] Another object of the invention is to enable autonomous operation of interconnected devices, reducing the need for human intervention in real-time decision-making processes.

[0007] Another object of the invention is to improve the efficiency of data exchange between devices by minimizing latency and optimizing bandwidth usage through edge computing capabilities.

[0008] Another object of the invention is to provide a scalable and flexible system that can easily accommodate the addition of new devices and services without the need for significant infrastructure changes.
[0009] Another object of the invention is to enhance security in M2M communication systems by incorporating real-time anomaly detection and threat mitigation mechanisms powered by AI.

[00010] Another object of the invention is to increase operational efficiency across various sectors, including smart cities, industrial automation, and healthcare, by enabling predictive analytics and proactive maintenance.

[00011] Another object of the invention is to provide a modular architecture that allows for easy integration of diverse AI services and protocols, ensuring adaptability to evolving technological landscapes.

[00012] Another object of the invention is to ensure interoperability with legacy M2M systems, enabling organizations to adopt AI enhancements without extensive overhauls of existing systems.

[00013] Another object of the invention is to offer a comprehensive security framework that ensures the integrity and confidentiality of data exchanged between devices.

[00014] Another object of the invention is to foster sustainability by optimizing resource allocation and minimizing energy consumption through intelligent, AI-driven decision-making processes.

SUMMARY OF THE INVENTION

[00015] In accordance with the different aspects of the present invention, AI- enhanced system for optimised machine - to - machine communication is presented. It is designed to optimize machine-to-machine (M2M) communication by integrating advanced artificial intelligence algorithms. It enables autonomous decision-making, predictive analytics, and real-time anomaly detection, improving data exchange and operational efficiency across various sectors. The system supports edge computing, ensuring low-latency responses while offering scalability and flexibility for future expansion. Its modular architecture allows easy integration of AI services and interoperability with legacy systems. The invention also includes robust security features, safeguarding data integrity and enhancing system reliability.

[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 AI- enhanced system for optimised machine to machine communication.

[00021] FIG 2 is working methodology of AI- enhanced system for optimised machine to machine communication.


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 AI- enhanced system for optimised machine to machine communication 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, AI-enhanced system for optimised machine to machine communication 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of connected devices 102, AI processing unit 104, communication network 106, data management layer 108, edge computing capability 110, anomaly detection framework 112, modular integration architecture 114, user-configurable AI models 116, comprehensive security framework 118, predictive analytics engine 120, real-time feedback mechanism 122, collaborative decision-making framework 124, resource optimization algorithms 126, context-aware communication protocols 128, blockchain integration for data integrity 130, self-healing network capabilities 132, cross-domain compatibility 134, enhanced data visualization tools 136, customized alerts and notifications 138, multi-tenancy architecture 140.

[00029] Referring to Fig. 1, the present disclosure provides details of AI- enhanced system for optimised machine to machine communication 100 designed to improve the efficiency, scalability, and security of inter-device interactions. The system integrates connected devices 102 with an AI processing unit 104 that leverages machine learning for predictive analytics and autonomous decision-making. It utilizes a communication network 106 and data management layer 108 to handle data exchange and processing efficiently. Key components include edge computing capability 110, anomaly detection framework 112, and modular integration architecture 114 for scalability and real-time responsiveness. Additional components such as user-configurable AI models 116 and a comprehensive security framework 118 ensure adaptability and data integrity in diverse application environments.

[00030] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with connected devices 102, which include sensors, actuators, and controllers. These devices continuously gather data from their environments and communicate with other devices or the central processing unit. The connected devices 102 serve as the fundamental input nodes for the system, collecting real-time data for analysis. They work closely with the AI processing unit 104 to relay relevant data for predictive analytics and decision-making, enabling efficient system-wide communication and interaction across various applications.


[00031] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with AI processing unit 104, which acts as the system's core intelligence. It processes incoming data from connected devices 102, using advanced machine learning algorithms to perform predictive analytics, anomaly detection, and decision-making. The AI processing unit 104 interacts with the data management layer 108 to ensure that only cleansed and preprocessed data is used for analysis. This component also works with edge computing capability 110 to minimize latency and allow real-time data processing at the device level.

[00032] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with communication network 106, responsible for transferring data between connected devices 102 and the AI processing unit 104. The communication network 106 supports various protocols like MQTT, CoAP, and 5G, enabling smooth data flow across the system. It works closely with the data management layer 108 to handle large volumes of data efficiently. By utilizing edge computing capability 110, the communication network 106 ensures low-latency transmission, facilitating real-time decision-making and responses.

[00033] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with data management layer 108, which organizes, cleanses, and processes raw data collected from connected devices 102. It ensures data quality before sending it to the AI processing unit 104 for analysis. The data management layer 108 interacts with the communication network 106 to facilitate seamless data exchange and preprocessing. Additionally, it plays a key role in maintaining data integrity and reliability, ensuring the entire system operates with accurate information.

[00034] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with edge computing capability 110, which enables real-time processing closer to the source of data generation, significantly reducing latency. The edge computing capability 110 interacts with the connected devices 102 to analyze and act on data locally before sending it to the AI processing unit 104. This component is particularly beneficial for applications requiring immediate responses, such as industrial automation and smart cities, and works in conjunction with the communication network 106 to minimize bandwidth usage.

[00035] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with anomaly detection framework 112, which continuously monitors the data exchanged between connected devices 102 and the AI processing unit 104 for unusual patterns. The anomaly detection framework 112 plays a crucial role in maintaining system security by identifying and mitigating potential threats in real-time. It works with the comprehensive security framework 118 to ensure that any detected anomalies are addressed promptly, protecting the integrity of the M2M communication network.

[00036] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with modular integration architecture 114, which facilitates the seamless integration of various AI services, protocols, and devices into the system. The modular integration architecture 114 allows the system to scale easily, adding or removing connected devices 102 or upgrading AI capabilities without significant disruption. It works closely with the AI processing unit 104 and communication network 106 to ensure that all components interact harmoniously, promoting adaptability across different technological environments.

[00037] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with user-configurable AI models 116, allowing users to train and modify AI models according to specific needs. These models can be customized for various applications, such as predictive maintenance in industrial settings or traffic management in smart cities. The user-configurable AI models 116 work with the AI processing unit 104 to optimize decision-making and with the real-time feedback mechanism 122 to continually refine the model's accuracy based on operational data.

[00038] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with comprehensive security framework 118, which combines encryption, authentication, and real-time threat monitoring to protect the system from cyber threats. This framework works with the anomaly detection framework 112 to identify and address security breaches or unusual activity in real-time. The comprehensive security framework 118 ensures that all data exchanged between connected devices 102 and the AI processing unit 104 is secure and trustworthy, preserving the integrity of M2M communication.
[00039] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with predictive analytics engine 120, which processes real-time and historical data to forecast future trends and events. The predictive analytics engine 120 works closely with the AI processing unit 104 to provide actionable insights, enabling devices to perform tasks such as predictive maintenance or traffic optimization. This component interacts with the real-time feedback mechanism 122 to adjust predictions based on actual outcomes, continually improving its forecasting accuracy.

[00040] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with real-time feedback mechanism 122, which delivers instantaneous feedback to connected devices 102 based on the insights generated by the AI processing unit 104. This component ensures that devices can quickly adapt their behavior based on real-time data analysis. The real-time feedback mechanism 122 also works in conjunction with the predictive analytics engine 120 to refine AI models, allowing continuous learning and optimization. This dynamic interaction improves system responsiveness and accuracy across various applications.

[00041] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with collaborative decision-making framework 124, which enables multiple connected devices 102 to share data insights and make coordinated decisions. This framework enhances the overall system intelligence by allowing devices to collaborate on tasks such as resource optimization or traffic management. The collaborative decision-making framework 124 interacts with the AI processing unit 104 to aggregate data from various sources and ensure that all devices are aligned in their actions, promoting efficient and synchronized operations.

[00042] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with resource optimization algorithms 126, which are designed to efficiently allocate system resources such as energy, bandwidth, or processing power. These algorithms work closely with the connected devices 102 and the AI processing unit 104 to monitor resource consumption and dynamically adjust operations to minimize waste. The resource optimization algorithms 126 are particularly useful in applications like smart grids or industrial automation, where efficient resource management is crucial for sustainability and cost-effectiveness.

[00043] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with context-aware communication protocols 128, which allow devices to adapt their data transmission strategies based on environmental factors. These protocols ensure that only relevant and necessary data is transmitted, optimizing bandwidth usage and reducing network congestion. The context-aware communication protocols 128 work with the communication network 106 and the AI processing unit 104 to dynamically adjust communication parameters, making the system more efficient and responsive to real-time conditions.

[00044] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with blockchain integration for data integrity 130, which ensures the authenticity and security of data exchanged between connected devices 102. This component provides a tamper-proof record of all communications and transactions, enhancing trust and transparency within the system. The blockchain integration 130 works alongside the comprehensive security framework 118 to provide an additional layer of protection, ensuring that data integrity is maintained across the entire M2M network.

[00045] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with self-healing network capabilities 132, which enable the system to automatically detect and resolve communication issues. This component works with the communication network 106 to ensure seamless operation by identifying network disruptions or failures and initiating corrective actions. The self-healing network capabilities 132 enhance system reliability and reduce downtime, making the system robust and capable of maintaining continuous operation without manual intervention.

[00046] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with cross-domain compatibility 134, allowing the system to operate across different sectors, such as healthcare, transportation, and smart cities. This component ensures that devices from various industries can communicate and collaborate seamlessly. The cross-domain compatibility 134 works with the modular integration architecture 114 to facilitate interoperability between diverse ecosystems, enabling the system to adapt to different use cases and industry requirements.

[00047] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with enhanced data visualization tools 136, which present AI-generated insights in an intuitive, user-friendly format. These tools include dashboards and graphical representations, making complex data more accessible to users. The enhanced data visualization tools 136 work closely with the AI processing unit 104 and predictive analytics engine 120 to display real-time trends, forecasts, and anomalies, helping users make informed decisions quickly and accurately.

[00048] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with customized alerts and notifications 138, allowing users to set specific criteria for receiving real-time updates. These alerts can be triggered by thresholds, anomalies, or other conditions identified by the AI processing unit 104. The customized alerts and notifications 138 work with the real-time feedback mechanism 122 to ensure that stakeholders are promptly informed of critical issues or changes, enabling swift action when necessary.

[00049] Referring to Fig. 1, AI-enhanced system for optimized machine-to-machine (M2M) communication 100 is provided with multi-tenancy architecture 140, which supports multiple users or organizations while maintaining secure data segregation. This component allows the system to scale effectively, accommodating different users' needs while keeping their data and operations isolated. The multi-tenancy architecture 140 works with the comprehensive security framework 118 to ensure that each user's data remains confidential and protected, making it ideal for service providers managing multiple clients within the same system.

[00050] Referring to Fig 2, there is illustrated method 200 for AI-enhanced system for optimized machine-to-machine (M2M) communication 100. The method comprises:

At step 202, method 200 includes connected devices 102 continuously collecting data from their environment;

At step 204, method 200 includes communication network 106 transmitting the collected data to the AI processing unit 104 for analysis;

At step 206, method 200 includes the AI processing unit 104 applying machine learning algorithms to perform predictive analytics and detect anomalies in the data;

At step 208, method 200 includes edge computing capability 110 processing real-time data locally to reduce latency and bandwidth usage;

At step 210, method 200 includes the anomaly detection framework 112 identifying any unusual patterns or potential threats in the data and notifying the AI processing unit 104;
At step 212, method 200 includes the modular integration architecture 114 facilitating the integration of additional AI services and devices into the system;

At step 214, method 200 includes the user-configurable AI models 116 allowing users to customize and train AI models according to their specific needs;

At step 216, method 200 includes the predictive analytics engine 120 generating actionable insights, such as predicting equipment failures or traffic congestion;

At step 218, method 200 includes the real-time feedback mechanism 122 sending the AI-generated insights back to connected devices 102 to adjust their operations accordingly;

At step 220, method 200 includes the collaborative decision-making framework 124 enabling multiple devices to share insights and make coordinated decisions based on aggregated data;

At step 222, method 200 includes the resource optimization algorithms 126 optimizing the allocation of resources like energy or bandwidth across the system;

At step 224, method 200 includes the context-aware communication protocols 128 adapting data transmission strategies based on environmental context;

At step 226, method 200 includes the blockchain integration for data integrity 130 ensuring the authenticity and security of data exchanged between devices;

At step 228, method 200 includes the self-healing network capabilities 132 automatically detecting and resolving communication issues to maintain seamless operation;

At step 230, method 200 includes the cross-domain compatibility 134 enabling the system to operate across different sectors such as healthcare, transportation, and smart cities;

At step 232, method 200 includes the enhanced data visualization tools 136 presenting AI-generated insights in user-friendly formats, such as dashboards or graphs;

At step 234, method 200 includes the customized alerts and notifications 138 informing users of critical issues or changes based on AI analysis;

At step 236, method 200 includes the comprehensive security framework 118 ensuring the security and integrity of all data transmitted between devices and the AI processing unit 104;

At step 238, method 200 includes the multi-tenancy architecture 140 allowing multiple users or organizations to securely operate within the same system, ensuring data isolation and protection.

[00051] 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.

[00052] 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.

[00053] 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.

, Claims:WE CLAIM:
1. An AI-enhanced system for optimized machine-to-machine (M2M) communication 100 comprising of
connected devices 102 continuously collecting data from their environment;
communication network 106 transmitting the collected data to the AI processing unit 104 for analysis;
AI processing unit 104 applying machine learning algorithms to perform predictive analytics and detect anomalies in the data;
edge computing capability 110 processing real-time data locally to reduce latency and bandwidth usage;
anomaly detection framework 112 identifying any unusual patterns or potential threats in the data and notifying the AI processing unit 104;
modular integration architecture 114 facilitating the integration of additional AI services and devices into the system;
user-configurable AI models 116 allowing users to customize and train AI models according to their specific needs;
predictive analytics engine 120 generating actionable insights, such as predicting equipment failures or traffic congestion;
real-time feedback mechanism 122 sending the AI-generated insights back to connected devices 102 to adjust their operations accordingly;
collaborative decision-making framework 124 enabling multiple devices to share insights and make coordinated decisions;
resource optimization algorithms 126 optimizing the allocation of resources like energy or bandwidth across the system;
context-aware communication protocols 128 adapting data transmission strategies based on environmental context;
blockchain integration for data integrity 130 ensuring the authenticity and security of data exchanged between devices;
self-healing network capabilities 132 automatically detecting and resolving communication issues to maintain seamless operation;
cross-domain compatibility 134 enabling the system to operate across different sectors such as healthcare, transportation, and smart cities;
enhanced data visualization tools 136 presenting AI-generated insights in user-friendly formats, such as dashboards or graphs;
customized alerts and notifications 138 informing users of critical issues or changes based on AI analysis;
comprehensive security framework 118 ensuring the security and integrity of all data transmitted between devices and the AI processing unit 104; and
multi-tenancy architecture 140 allowing multiple users or organizations to securely operate within the same system, ensuring data isolation and protection.

2. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein connected devices 102 are configured to continuously collect data from their environment and transmit it across the system for analysis, enabling autonomous operation and real-time monitoring of interconnected devices.

3. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein the AI processing unit 104 is configured to apply machine learning algorithms to perform predictive analytics, detect anomalies in the data, and make autonomous decisions to improve system efficiency and responsiveness.

4. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein the communication network 106 is configured to enable data transmission between connected devices 102 and the AI processing unit 104, supporting multiple communication protocols to ensure seamless and scalable connectivity across the system.

5. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein edge computing capability 110 is configured to process data locally, reducing latency and bandwidth usage by performing computations closer to the source of data generation, thereby enabling real-time decision-making.

6. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein the anomaly detection framework 112 is configured to continuously monitor data for unusual patterns and potential security threats, enabling real-time threat detection and mitigation across the M2M network.

7. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein the predictive analytics engine 120 is configured to generate actionable insights by analyzing real-time and historical data, predicting trends such as equipment failures or traffic congestion, and enabling proactive system adjustments.

8. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein the comprehensive security framework 118 is configured to protect data integrity and security by employing encryption, authentication, and real-time monitoring of data exchanged between devices and the AI processing unit 104.

9. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein the self-healing network capabilities 132 are configured to automatically detect and resolve communication issues, ensuring system reliability and minimizing operational downtime.

10. The AI-enhanced system for optimized machine-to-machine (M2M) communication 100 as claimed in claim 1, wherein method comprises of
connected devices 102 continuously collecting data from their environment;
communication network 106 transmitting the collected data to the AI processing unit 104 for analysis;
AI processing unit 104 applying machine learning algorithms to perform predictive analytics and detect anomalies in the data;
edge computing capability 110 processing real-time data locally to reduce latency and bandwidth usage;
anomaly detection framework 112 identifying any unusual patterns or potential threats in the data and notifying the AI processing unit 104;
modular integration architecture 114 facilitating the integration of additional AI services and devices into the system;
user-configurable AI models 116 allowing users to customize and train AI models according to their specific needs;
predictive analytics engine 120 generating actionable insights, such as predicting equipment failures or traffic congestion;
real-time feedback mechanism 122 sending the AI-generated insights back to connected devices 102 to adjust their operations accordingly;
collaborative decision-making framework 124 enabling multiple devices to share insights and make coordinated decisions;
resource optimization algorithms 126 optimizing the allocation of resources like energy or bandwidth across the system;
context-aware communication protocols 128 adapting data transmission strategies based on environmental context;
blockchain integration for data integrity 130 ensuring the authenticity and security of data exchanged between devices;
self-healing network capabilities 132 automatically detecting and resolving communication issues to maintain seamless operation;
cross-domain compatibility 134 enabling the system to operate across different sectors such as healthcare, transportation, and smart cities;
enhanced data visualization tools 136 presenting AI-generated insights in user-friendly formats, such as dashboards or graphs;
customized alerts and notifications 138 informing users of critical issues or changes based on AI analysis;
comprehensive security framework 118 ensuring the security and integrity of all data transmitted between devices and the AI processing unit 104; and
multi-tenancy architecture 140 allowing multiple users or organizations to securely operate within the same system, ensuring data isolation and protection.

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