Wireless Sensor and IoT Precision Agriculture

Abstract

Wireless Sensor Networks (WSNs) often face challenges related to limited energy resources, especially in agricultural monitoring applications where long-term deployment is crucial. This study evaluates and compares three clustering protocols—SEP, DEEC, and DEEP (our previously proposed protocol) [13]—alongside a new Software-Defined Networking (SDN)-based approach aimed at enhancing energy efficiency. Our simulation results indicate that the SDN-based method significantly outperforms existing protocols. It reduces energy consumption and extends the network's lifetime by 15% compared to DEEP, 20 % over DEEC, and up to 60 % versus SEP, while also dynamically adapting to changes in network topology. The SDN framework achieves this efficiency through centralized control and intelligent flow management. Additionally, we implemented the Routing Protocol for Low-Power and Lossy Networks (RPL) on the Contiki OS, demonstrating its effectiveness in supporting IoT-based smart irrigation systems. To complete our work, we designed and deployed a cost-effective smart irrigation prototype using FC-28 soil moisture sensors and Arduino Uno R3 microcontrollers, achieving a 30 % reduction in water usage through real-time sensor feedback. Our contributions are threefold: (1) a comparative performance analysis of clustering protocols under varying node energy levels, (2) the development of an SDN-based architecture tailored for energy-efficient WSNs, and (3) the implementation of a functional, open-source smart irrigation system based on RPL and low-cost hardware. This research effectively connects theoretical models with real-world deployment, offering scalable solutions for precision agriculture.