Research Article | ![]()
A Cross Layer Aware Hybrid Routing Algorithm Using Federated Q-Learning and Ant Colony Optimization for Wireless Sensor Networks
Author(s): Haripriya R1*, and Suresh M2
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 13, Issue 4
Publisher : FOREX Publication
Published : 30 November 2025
e-ISSN : 2347-470X
Page(s) : 673-678
Abstract
Efficient routing in Wireless Sensor Networks (WSNs) remains challenging due to energy constraints, link instability, and dynamic topologies. While machine learning and bio-inspired methods offer improvements, many existing protocols struggle with scalability, computational demands, and limited consideration of critical QoS metrics like delay, PDR, and network lifetime. To overcome these limitations, this paper introduces Fed-QL-ACO-X, a hybrid routing protocol combining Federated Q-Learning (FQL) for decentralized learning, Ant Colony Optimization (ACO) for adaptive path selection, and cross-layer awareness using MAC and PHY layer metrics. Unlike centralized models, it supports local training and lightweight updates, reducing communication overhead. Simulations on a 100-node network benchmarked Fed-QL-ACO-X against 4 advanced routing protocols. The model achieved up to 20% lower energy use, 30% reduced delay, 94% PDR, and a 12–16% increase in network lifetime. These results highlight its effectiveness and scalability, positioning Fed-QL-ACO-X as a practical solution for real-world WSN deployments.
Keywords: Wireless Sensor Networks, Federated Learning, Q-Learning, Ant Colony Optimization, Energy Efficiency, Cross-Layer Design, Delay Reduction, Packet Delivery Ratio.
Haripriya R, Research Scholar, Department of Electronics and Communication Engineering, SSIT, SSAHE, Tumkur, Karnataka, India; Email: priyakushi18@gmail.com
Suresh M, Professor, Department of Electronics and Communication Engineering, SSIT, SSAHE, Tumkur, Karnataka, India
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