SDN–Edge WSN: A Programmable and Scalable Architecture for Efficient Sensor Network Management

Abstract

Wireless Sensor Networks constitute the core sensing infrastructure for modern Internet of Things applications such as smart cities, industrial automation, environmental monitoring, and healthcare systems. However, conventional WSN architectures suffer from limited scalability, static routing, excessive energy consumption, and high latency due to distributed control and resource constraints. These limitations hinder their suitability for emerging real-time and large-scale deployments. To address these challenges, this paper proposes a novel Software Defined Networking–Edge enabled programmable WSN architecture that separates the control and data planes while introducing edge intelligence for localized decision making. The proposed framework integrates centralized SDN control for global network optimization with distributed edge nodes for low-latency processing, adaptive routing, and traffic aggregation. An energy-aware routing mechanism and load-balanced flow management strategy are developed to reduce redundant transmissions and improve overall network lifetime. The architecture is evaluated against five representative routing and clustering protocols using six essential performance metrics: energy consumption, latency, throughput, packet delivery ratio, scalability, and control overhead. Simulation results demonstrate that the proposed model reduces energy usage by up to 35–40%, decreases latency by more than 60%, and improves throughput and reliability compared to conventional and intelligent routing approaches. The findings confirm that integrating SDN programmability with edge computing significantly enhances WSN performance and adaptability. The proposed solution offers a scalable foundation for next-generation IoT deployments requiring real-time responsiveness, efficient resource utilization, and dynamic network management.