A Multi-Node Energy Prediction Approach Combined with Optimum Prediction Interval for RF Powered WSNs

Abstract

Energy prediction plays a vital role in designing an efficient power management system for any environmentally powered Wireless Sensor Networks (WSNs). Most of the Moving Average (MA)-based energy prediction methods depend on past energy readings of the concerned node to predict its future energy availability. However, in case of RF powered WSNs the harvesting history of the main node along with neighbouring nodes can also be used to develop a more robust prediction technique. In this paper, we propose a Multi-Node energy prediction method for Radio Frequency Energy Harvesting (RF-EH) WSNs, which predicts the future energy availability by taking into account harvesting history of all nodes surrounding the main node. We analyse the effective distance for prediction and also develop a mathematical model to compute the optimum value of prediction interval, which has a major effect in prediction accuracy and system design, considering energy neutrality. Results show that Multi-Node prediction is less sensitive to prediction interval while inheriting the advantages of MA techniques. Also, nodes located at a larger distance were utilized less for prediction, and as the prediction interval increased, the utilization of more distant nodes decreased. Furthermore, we also establish a linear relation between the prediction interval and the energy threshold limit.

Energy Prediction for Energy-Harvesting Wireless Sensor: A Systematic Mapping Study

“…In addition, they also proposed a configuration-file-compression method that can reduce memory requirements by 50% while achieving 90% accuracy. Koirala et al [36] proposed a multi-node energy-prediction method for the harvesting of RF energy using neighboring nodes' energy-harvesting information to predict future energy availability. They also developed a mathematical model to calculate the optimal value of the prediction interval, which can effectively improve prediction accuracy.…”