Energy-Efficient Clustering and Routing Optimization for IoT-Based Wireless Sensor Networks

Abstract

Wireless Sensor Networks (WSNs) are an important component of the contemporary Internet of Things (IoT) applications as they are used to monitor real-time, collect data, and communicate in different settings. Nevertheless, the small power of sensor nodes is still a significant issue that impacts the lifetime of the network, its reliability, and the general performance. The present study aims at designing an energy efficient clustering and routing optimization model of IoT based WSNs to enhance the use of energy as well as extend the network life. The suggested methodology will help to maximize the choice of cluster heads, uniformity in energy consumption of sensor nodes, and to determine efficient routing paths to transmit data. The model minimizes unnecessary energy use and maximizes the delivery of data through factors like residual energy, distance of nodes, cost of communication and network conditions. Key parameters such as network lifetime, energy consumption, ratio of packet delivery, throughput, and latency are used to evaluate the performance of the proposed method. The optimized clustering and routing plan proves to be more scalable, less energy depleting and more reliable in communication than traditional routing methods. The results show that smart energy-conscious optimization can greatly enhance the efficiency and sustainability of the IoT-based wireless sensor networks, rendering them more applicable to long-term and large-scale IoT deployments.