Enhanced mobile sink path optimization using RPP-RNN algorithm for energy efficient data acquisition in WSNs

Energy use and data collection are the main issues in real time wireless sensor network scenarios. Mobile sinks help balance energy usage, reduce multi-hop transmission, and extend network lifetime through moving around the network to collect data at predetermined locations. The proposed novel Rank-Based Path Planning algorithm with Recurrent Neural Networks identifies hotspot nodes based on energy dissemination, network traffic, and multi-hop transmission. Mobile sink only visits the hotspot nodes to collect data, while other nodes forward data to the nearest hotspot. Experimental results shows 35% decrease in energy consumption and 13% increase in network life compared to existing state-of-art algorithms and moderate increases in simulation time, ensuring efficient data collection.