Secure and Energy-Efficient M2M Communication Strategies for Next-Generation Industrial IoT Environments

Abstract

Machine-to-Machine (M2M) serves as the foundation of next-generation Industrial Internet of Things (IIoT) systems that are capable of autonomously coordinating, making real-time decisions, and one-way industrial automation on a large scale across many different mission-critical settings. With the advancement of IIoT systems to ultra-dense deployments and high-quality of service (QoS) needs, secure, reliable, and energy-efficient M2M communication is quite a challenge to ensure with limited device resources, heterogeneous network topology, and growing cyber-physical vulnerabilities. The desired solution to these problems should be communication strategies that reduce energy overhead, enhance data security and integrity, and be able to extend scalability when the industrial is operating dynamically. The paper under consideration offers an in-depth examination and a detailed analysis of the new strategies that come together to increase the security and energy efficiency of M2M communication with future IIoT networks. The major developments presented are lightweight cryptographic primitives that are designed so as to optimise towards constrained nodes, smart spectrum allocation algorithms, energy-sensitive and adaptive routing algorithms, blockchain-based distributed trust management, and AI-driven mechanism systems of intrusion detection that detect anomalous behaviours with high accuracy. This paper presents a coherent Secure and Energy-Efficient M2M (SEE-M2M) model that will merge the concepts of secure access control, distributed authentication, context-based scheduling of communication, and machine-learning-aided optimization of the network to bring about robust, resilient and low-power functioning in heterogeneous IIoT systems. The available framework, however, exhibits high gains in communication integrity, minimised latency, resistance to attacks, and long network/devices lifespan with the help of thorough comparative assessment, simulation-based analysis, and architectural modelling. Lastly, the paper also identifies promising directions of future research, such as Quantum-Resilient M2M, neuromorphic edge intelligence to support low-power inference, and 6G-enables tactile IIoT systems that should support ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC) and AI-native industrial automation. The proposed research paper brings its own single vision on how to create safe, scalable and power-efficient M2M communication systems of the next-generation intelligent industrial systems.