Bio-Inspired AI for IoT Networks: Swarm Intelligence and Neural Models for Adaptive Decision Making

Abstract

Internet-of-Things (IoT) networks operate under tight energy, bandwidth, and latency constraints. Bio-inspired AI offers practical tools to adapt under these constraints by combining swarm intelligence for decentralized control with neural models for predictive and learning based decisions. This paper (i) surveys how ant colony optimization, particle swarm optimization, and other swarm methods improve routing, clustering, and resource orchestration in IoT; (ii) reviews neural approaches, including deep reinforcement learning, graph neural networks, multi-armed bandits, federated learning, and spiking neural networks at the edge; and (iii) proposes BioAdapt-IoT, a hybrid architecture where lightweight swarm agents handle local coordination while neural components at the edge (or neuromorphic nodes) learn policies for long-term performance. We outline evaluation metrics and provide a reproducible experiment design to benchmark energy, latency, reliability, and adaptability. The review and the proposed design show that hybrid bio-inspired control can deliver robust gains for large scale IoT deployments [6], [8], [13], [21], [24].