Abstract

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Internet of Things (IoT) are increasingly being adopted into practical applications such as security systems, smart infrastructure, traffic management, weather systems, among others. While the scale of these applications is enormous, device capabilities, particularly in terms of battery life and energy efficiency are limited. Despite research being done to ameliorate these shortcomings, wireless IoT networks still cannot guarantee satisfactory network lifetimes and prolonged sensing coverage. Moreover, proposed schemes in literature are convoluted and cannot be easily implemented in real-world scenarios. This necessitates the development of a simple yet energy efficient routing scheme for wireless IoT sensor networks. This paper models the energy constraint problem of devices in IoT applications as an optimization problem. To conserve energy of devices the proposed protocol makes use of clustering, cluster head election and least energy-expensive path computation for efficient and realtime routing. The path computation involves using a formulated equation which characterizes communication intent between transmitter and receiver devices. The features selected for the clustering algorithm contribute towards optimizing the energy conservation effort. This paper also utilizes an evolutionary sleep scheduling technique which can be optionally used to further boost network efficiency. This technique combines the benefits of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The proposed routing protocol has been simulated and compared with two existing routing protocols in terms of metrics such as number of active nodes, energy dynamics and network coverage. The simulation results prove that the proposed protocol outperforms LEACH and FCM.