Cognitive Swarm Intelligence with Self-Evolving Neural Networks and Trust-Aware Edge Analytics for IOT

Abstract

The rapid proliferation of Internet of Things (IoT) devices has resulted in widespread deployment of interconnected systems generating vast data volumes. Conventional IoT architectures depend excessively on centralized cloud computing, leading to elevated latency, substantial communication burdens, and constrained scalability. This paper presents a Cognitive Swarm Intelligence framework incorporating self-evolving neural networks and trust-aware edge analytics tailored for IoT ecosystems. Lightweight neural network models are integrated directly into IoT devices to facilitate edge-based data processing and autonomous local decision-making. IoT nodes collaborate via swarm intelligence protocols, exchanging predictive outputs to achieve decentralized consensus, with node contributions weighted by demonstrated reliability. A self-evolving adaptation mechanism ensures ongoing model refinement in response to dynamic environments. Evaluations using an Edge AI prototype and swarm simulations reveal significant reductions in cloud data transfers, greater resilience to unreliable nodes, and superior decision accuracy. This framework empowers IoT devices to operate as intelligent, collaborative agents, ideal for applications in smart cities, healthcare monitoring, and industrial IoT.