Deep Learning-Enabled Predictive Routing for Energy-Efficient Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are extensively used in fields like smart agriculture, healthcare, industrial automation and environmental monitoring. Energy-efficient routing continues to be one of the main issues influencing network longevity and Quality of Service (QoS) because sensor nodes run on limited battery power. Conventional routing protocols such as LEACH, AODV and DSR typically rely on static routing algorithms and are unable to efficiently adjust to dynamic network situations such as varying connection quality, traffic congestion and node energy depletion. The Deep Learning-Enables Predictive Routing (DL-PR) system proposed in this research uses past data such as residual energy, packet delivery ratio, traffic load and communication delay to forecast future network conditions. A composite routing cost function is used to determine dependable and energy-efficient routing paths based on the anticipated network condition. When compared to traditional routing protocols, simulation experiments using MATLAB show that the suggested architecture greatly boosts network lifetime, maintains higher residual energy, raises packet delivery ratio and lowers end-to-end delay. For the future generation of IoT-enabled Wireless Sensor Networks, the suggested framework provides an intelligent routing solution.