ACO-Based Scalable SSDWSN for Energy-Efficient and Congestion-Aware Wireless Sensor Networks

Scalable Software-Defined Wireless Sensor Networks (SSDWSNs) have emerged as a promising solution for enhancing flexibility, programmability, and centralized control in large-scale wireless sensor deployments. However, conventional routing mechanisms often suffer from high energy consumption, network congestion, and limited scalability under dynamic conditions. To address these challenges, this paper proposes an Ant Colony Optimization Based Scalable Software-Defined Wireless Sensor Networks (SSDWSN) framework for energy-efficient and congestion-aware routing. The proposed approach integrates Ant Colony Optimization sinto the software-defined architecture, enabling pheromone-guided probabilistic routing for adaptive multi-hop data transmission. The routing strategy considers critical parameters such as residual energy, distance, and link quality to select optimal paths. Additionally, the centralized controller maintains a global network view to support efficient decision-making and load balancing. Simulation results demonstrate that the proposed SSDWSN-ACO Scalable Software-Defined Wireless Sensor Networks-Ant Colony Optimization model significantly improves packet delivery ratio, reduces end-to-end delay, and enhances network lifetime while minimizing energy consumption compared to traditional routing techniques. The results confirm that the proposed framework provides an effective, scalable, and lightweight solution for next-generation wireless sensor networks.