MODEL TO OPTIMIZE ROUTING AND SLEEP WAKE SCHEDULE FOR WIRELESS SENSOR NETWORK TO ENHANCE LIFETIME USING ANN
Keywords:
Energy Efficiency, Deep Learning, Dynamic Programming, Routing Optimization, Sleep Wake SchedulingAbstract
The proposal outlines the development of an Artificial Neural Network (ANN) model to optimize the routing and sleep-wake schedule for wireless sensor networks (WSNs). The primary goal is to extend the lifespan of WSNs by effectively managing energy consumption. WSNs comprise small, low-power sensor nodes deployed in remote or harsh environments, making battery replacement or recharging difficult. Therefore, minimizing energy consumption is critical for maximizing the network's lifetime. Routing protocols are essential in WSNs as they establish data transmission paths. Traditional protocols may overlook individual nodes' energy constraints, leading to inefficient energy usage and reduced network lifetime. By integrating an ANN-based model, the aim is to optimize routing decisions based on real-time energy levels and network conditions. Moreover, the sleep-wake schedule of sensor nodes significantly affects energy consumption. Intelligently scheduling sleep and wake periods can conserve energy during idle times while ensuring timely data transmission. The ANN model will learn from historical data and network conditions to dynamically adjust the sleep-wake schedule, further enhancing the network's lifetime. The proposed model will entail training an ANN using historical data on energy consumption, network topology, and environmental factors. Once trained, the model will be deployed in real-time to make routing and sleep-wake schedule decisions based on current network conditions. The model's performance will be evaluated based on energy efficiency, network lifetime, and data transmission reliability. By optimizing routing decisions and sleep-wake schedules using an ANN-based model, the expectation is for significant improvements in the lifetime of wireless sensor networks. This research holds the potential to enhance the efficiency and reliability of WSNs, enabling their deployment in various applications, including environmental monitoring, industrial automation, and smart cities.
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