MACHINE LEARNING-AUGMENTED MULTI-CRITERIA DECISION-MAKING (MCDM) FOR IOT SERVICE SELECTION

Abstract

This invention describes a machine learning-enhanced IoT service selection method that combines predictive machine learning with multi-criteria decision-making (MCDM) across the computation, communication, and device layers of IoT architecture. Machine learning algorithms predict service performance metrics, such as latency and energy consumption, based on historical data, real-time feedback, and environmental conditions. MCDM models dynamically adjust criteria weights using these predictions, optimizing service selection based on the unique requirements of each layer. This method improves efficiency, reduces latency, and enhances power conservation across IoT systems.

Specification

Description: FIELD OF THE INVENTION:
This invention relates to the field of Internet of Things (IoT) service selection, specifically focusing on optimizing the selection of services across the multi-layer IoT architecture using a machine learning-augmented multi-criteria decision-making (MCDM) framework. This approach enhances performance across the computation, communication, and device layers of IoT systems by dynamically evaluating and selecting services based on layer-specific criteria and real-time context.________________________________________
3. BACKGROUND OF THE INVENTION:
The Internet of Things (IoT) consists of interconnected devices that communicate and process data, enhancing automation and decision-making across various sectors like healthcare, smart cities, and industrial applications. The architecture of IoT systems typically comprises three layers, each serving distinct functions and requirements:
IoT Architecture Layers
1. Device (Things) Layer: This layer includes physical devices such as sensors, actuators, and wearables responsible for data acquisition and monitoring environmental conditions. Each device varies in power consumption, processing capabilities, and communication interfaces. Key challenges include ensuring interoperability among diverse devices, maintaining energy efficiency for battery-operated devices, and implementing robust security to protect sensitive data.
2. Communication Layer: This layer facilitates data transfer between devices and cloud or edge computing resources using various communication protocols like Wi-Fi, Bluetooth, and cellular networks. It must ensure reliable, low-latency data transmission while adapting to fluctuating network conditions. Additionally, it addresses security and data integrity issues to safeguard transmitted information from potential attacks.
3. Computation (Processing) Layer: Comprising cloud or edge computing resources, this layer processes and analyses data generated by devices. It handles extensive data storage and complex analytics while ensuring scalability and low response times. As IoT applications evolve, there is a growing demand for processing power and high availability to meet the real-time requirements of time-sensitive applications.
Limitations of Existing IoT Service Selection Approaches
Traditional IoT service selection methods often utilize multi-criteria decision-making (MCDM) frameworks but lack the adaptability required for the dynamic nature of IoT environments. Key limitations include:
1. Static Decision Models: Conventional MCDM approaches rely on fixed criteria weights, which do not adapt to real-time changes in device status or network conditions. This rigidity can lead to suboptimal service selection, where previously optimal services become less suitable due to changing conditions like increased latency.
2. Limited Context Awareness: Existing models often fail to incorporate contextual factors that influence service performance, such as time of day or device mobility. Without this awareness, selected services may not align with current operational needs, leading to inefficiencies.
3. Inefficient Resource Allocation: Static decision models can result in poor resource utilization, such as selecting high-bandwidth services during network congestion, leading to delays and failures. Additionally, services that are not energy-efficient can drain the battery life of devices prematurely.
4. Inability to Adapt to Security Threats: As IoT devices are increasingly integrated into critical infrastructures, they become vulnerable to security breaches. Current selection methods often overlook evolving security needs, making it essential for a service selection approach to adapt dynamically to real-time threat assessments.
5. Lack of Learning Mechanisms: Many existing methods do not learn from historical performance data, limiting their ability to identify patterns that could optimize service selection. By not leveraging past data, these approaches miss opportunities for continuous improvement.
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4. OBJECTIVES OF THE INVENTION:
The objectives of this invention are:
• To improve IoT service selection by integrating machine learning with MCDM, allowing for dynamic adjustments based on real-time network conditions and service requirements.
• To enhance adaptability by using machine learning to predict service performance across computation, communication, and device layers.
• To optimize criteria weight adjustments within the MCDM framework based on machine learning insights, ensuring that selected services best meet the current needs of each layer in the IoT architecture.
• To improve resource efficiency, reduce latency, and conserve power across IoT systems by selecting optimal services tailored to the unique requirements of each architecture layer.
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5. SUMMARY OF THE INVENTION:
This invention provides a machine learning-augmented MCDM framework for selecting IoT services across a multi-layer architecture. By integrating predictive machine learning with MCDM models, this invention dynamically adjusts criteria weights to optimize service selection within each IoT layer (computation, communication, and device). Machine learning algorithms predict performance metrics based on historical data and real-time feedback, allowing MCDM criteria weights to be adjusted accordingly. The system continuously monitors and adapts to changes, improving service selection in terms of efficiency, latency, and security.________________________________________
6. DETAILED DESCRIPTION OF THE INVENTION:
The invention comprises a multi-layer IoT service selection system with the following components:
1. Service Discovery and Machine Learning Module: This module monitors service performance data across each IoT layer and uses historical data to train machine learning models to predict service performance under varying conditions. Predictions include metrics such as latency, energy consumption, and processing speed.
2. Layered Criteria Evaluation Engine: This component utilizes MCDM models such as Analytic Hierarchy Process (AHP), TOPSIS, or VIKOR to rank services within each layer. The machine learning module's predictions adjust criteria weights dynamically, tailoring service ranking based on the unique requirements of each layer:
o Device Layer: Prioritizes criteria like battery efficiency, security, and device compatibility.
o Communication Layer: Focuses on criteria such as bandwidth, latency, and network reliability.
o Computation Layer: Emphasizes processing power, storage capacity, and data processing speed.
3. Real-Time Optimization Module: This module continuously monitors changes in network and device conditions, adjusting MCDM criteria weights in real-time according to machine learning predictions. This allows the system to select services that are best suited to current environmental conditions, enhancing performance across all layers.
Operational Flow:
• Data Logging: Logs and stores service performance data across all IoT layers for machine learning analysis.
• Machine Learning Prediction: Predicts service performance metrics based on logged data and adjusts MCDM weights accordingly.
• Layered MCDM Ranking: Ranks services within each layer, adjusted by the machine learning-optimized weights.
• Cross-Layer Optimization: Prioritizes services across layers based on current network conditions, device state, and user requirements, ensuring optimal resource allocation.
, Claims:Claim 1: An IoT service selection method using a machine learning-augmented multi-criteria decision-making framework to evaluate and select services across IoT layers, with criteria weights adjusted in real-time based on machine learning predictions.
Claim 2: A layered MCDM evaluation engine within the IoT service selection system, which evaluates and ranks services across device, communication, and computation layers based on criteria adjusted by machine learning predictions.
Claim 3: A real-time optimization module that dynamically adjusts criteria weights within the MCDM model to accommodate changing network conditions, user preferences, and device states.
Claim 4: A machine learning module that predicts service performance metrics across IoT architecture layers and refines the MCDM ranking process, enabling context-aware service selection tailored to layer-specific requirements.

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