Hybrid bio-inspired optimization based routing protocol for enhancing data transmission in clustered network
Kirandeep Kaur, Satinder Kaur · Array · 2025
The Internet of Things (IoT) incorporates Wireless Sensor Networks (WSNs) to gather data in real time for a range of applications, including smart homes and healthcare. Energy efficiency is an essential concern considering sensor nodes have limited energy resources. Early node failures, network segmentation, and reduced quality of service (QoS) are driven by constant and uneven energy consumption among sensor nodes, particularly during data transmission and cluster head (CH) processes. For addressing this issue, the current study proposes a hybrid optimization approach for a clustering protocol that mitigates transmission latency and optimises energy efficiency by integrating bi-objective Tabu Search and Ant Colony Optimization (ACO). The primary goals include to extend the network lifetime via efficient data transmission and the most optimal possible cluster head (CH) selection. In two deployment scenarios, the protocol is simulated in MATLAB and assessed based on residual energy, transmission delay, network stability, and lifetime. Results indicate a 73 % lifetime increase, a 25 % improvement in network stability, and a 36 % decrease in delivery latency when compared to GWO, ESO, GECR, and LEACH. The proposed protocol surpasses other protocols in extending WSN capabilities in Internet of Things systems. • A hybrid ACO2 and Tabu Search-based routing protocol is proposed to optimize data transmission in clustered WSNs. •The protocol significantly enhances network lifetime, energy efficiency, and reduces delivery delay. •Multi-hop communication routes are optimized using transmission capacity and distance-based heuristics. •Simulation results show up to 73 % improvement in network lifetime and 36 % reduction in latency over existing methods.