Gain-Based Ant Colony Optimization-Driven Data Routing Mechanism for IoT-Enabled Sensor Networks

A Wireless Sensor Network (WSN) is a major component of any Internet of Things (IoT)-based system. In WSNs, a Mobile Sink (MS) collects sensed data from deployed sensor nodes by visiting Rendezvous Points (RPs) in an energy-efficient manner. The presence of mobile obstacles in WSNs significantly reduces network performance by increasing data transmission delay and degrading overall operation. This paper proposes an Energy-Efficient Intelligent Obstacle Avoidance Data Routing Scheme (EEIOADRS) for IoT-enabled WSNs. It effectively identifies and avoids mobile obstructions with minimal message passing and low delay. A heuristic-based Minimum Spanning Tree algorithm is used to find an optimal path among all Grid Cell Heads (GCHs) for MS-based data gathering. Furthermore, Gain-Based Dynamic Ant Colony Optimization is used to construct an optimal mobile obstacle-free path for MS-based data collection. It significantly reduces data transmission delay and enhances overall network performance. Extensive simulations demonstrate that the proposed scheme significantly improves network performance. The proposed approach improves network lifetime by 50.84% relative to OMCSO, 42.34% relative to CSOBUG, and 39.83% relative to OASPP. Additionally, simulation results indicate that the proposed scheme enhances network throughput by 47.15% compared to OMCSO, 36.69% compared to CSOBUG, and 33.76% compared to OASPP.