AI-based Model for Improving the Network Lifetime of Wireless Sensor Networks (WSNs)

Wireless Sensor Networks (WSNs) are widely used in modern applications that require remote and real-time monitoring. These applications include environmental sensing, industrial process control, agriculture, healthcare monitoring, and smart city development. A WSN typically consists of many small, battery-powered sensor nodes that collect data from their surroundings and transmit it to a central sink node for further processing. One of the most critical challenges facing WSNs is the limited energy supply of sensor nodes. Since these nodes often operate in inaccessible or hostile environments, replacing or recharging batteries is not feasible. As a result, energy efficiency becomes a primary concern, as the failure of nodes due to energy depletion can disrupt data transmission and reduce the overall network lifetime. Traditional approaches to energy management, such as static routing protocols and clustering algorithms, are often rigid and unable to adapt to dynamic network conditions. To address this limitation, recent research has focused on the integration of Artificial Intelligence (AI) into WSN management. AI techniques like machine learning, reinforcement learning, and predictive analytics can help optimize energy usage through intelligent routing, efficient data aggregation, and adaptive node scheduling. This article presents the design and simulation of an AI-based model that significantly improves the operational lifetime and efficiency of WSNs.