Hybrid Ant-Swarm Coverage and Routing Optimization for Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), effective network coverage, energy-aware routing and reliable data delivery are major challenges. This paper addresses an intelligent optimized WSN using a two-phase hybrid network, thereby tackling these challenges. In the first phase, a proposed Improved Particle Swarm Optimization (IPSO) algorithm locates the collector node in the best position to achieve full coverage. In the second phase, the proposed Modified Elitist Ant Colony Optimization (MEACO) is used to determine the optimized multi-hop routing paths in terms of energy and delay. To evaluate the strength of the proposed framework, a patient network scenario is formulated where patients and collectors are placed to check baseline performance. In addition, a priority-based QoS Scheduling mechanism is introduced for data transfer, in which priority patients are monitored with the least end-to-end delay, while maintaining network lifetime. Reliable delivery of physiological information is challenged by packet loss and unstable wireless links. The proposed framework improves reliability through link quality evaluation, bounded retransmissions, and route recovery. The simulation results show that the proposed IPSO-MEACO approach improves network coverage by 13.2%, minimizes energy utilization by 14.7%, and drastically reduces end-to-end delay by 58.2% compared with classical algorithms. The results also affirm that the algorithm is suitable for full coverage with minimum end-to-end delay and energy consumption.