A Wi-Fi dynamic routing algorithm based on energy prediction for wildlife monitoring wireless network

Abstract

The routing protocol of wildlife monitoring Wi-Fi (Wireless Fidelity) networks cannot balance node energy consumption, leading to early node death. Therefore, the research on energy balance in wildlife monitoring Wi-Fi networks is a hot topic. In order to balance the energy consumption of Wi-Fi networks and extend the lifespan of wireless networks, we designed the low energy dynamic routing protocol LEACH-EP (Low Energy Adaptive Clustering Hierarchy- Energy Prediction) based on energy prediction by analyzing the long-range dependent characteristics of the remaining energy time series (RETS) of wireless network nodes. This protocol uses the LSTM (Long Short-Term Memory) model to predict the remaining energy of network nodes, and then dynamically plans routes using future remaining energy. We conducted a networking experiment in the Anzihe Nature Reserve in Chengdu, China, and the Energy Balance Factor index of the wireless network significantly improved. The Mean Absolute Error value of network nodes is less than 60 mW, which is less than 10% of the average daily energy consumption of nodes. Half of the surviving network nodes have achieved an increase to 55.2%, and the network death time has been extended by 38.6%. The experimental results show that the energy prediction routing protocol LEACH-EP can significantly extend the node survival life and balance network energy consumption.