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].