Cognitive IoT Networks with Bio-Inspired Embedded AI for Predictive Maintenance and Sustainable Energy Optimization

Increasing system complexity poses significant difficulties for green energy management and predictive maintenance, primarily due to limited adaptability and centralization of AI systems. Such difficulties are eliminated in this work by synthesizing Cognitive IoT (CIoT) and bio-inspired embedded Artificial Intelligence, as a consequence of which a self-adaptive and energy-efficient industrial ecosystem is being designed. The proposed industrial ecosystem utilizes swarm intelligence, neural adaptation, and self-organizing feedback loops for decentralized decisions and real-time fault identification. Embedded AI modules embedded at IoT edge nodes learn automatic behavior during operation, predict likely faults, and optimize scheduling for repairs. Moreover, a hybrid framework implementing evolutionary optimization and deep neuro-symbolic learning maintains dynamic resource allocation and energy-aware computation. The novelty is dependent upon bio-inspired cognitive architecture, and self-evolving and environmentally adaptable operation is achieved by means of multi-agent coordination and hybrid learning paradigms. The end objective is improved reliability of a system, decreased downtime, and ecological sustainability for next-generation industrial networks.