Adaptive LINE-P: An Adaptive Linear Energy Prediction Model for Wireless Sensor Network Nodes

Abstract

In the context of wireless sensor networks, energy prediction models are increasingly useful tools that can facilitate the power management of the wireless sensor network (WSN) nodes. However, most of the existing models suffer from the so-called fixed weighting parameter, which limits their applicability when it comes to, e.g., solar energy harvesters with varying characteristics. Thus, in this article we propose the Adaptive LINE-P (all cases) model that calculates adaptive weighting parameters based on the stored energy profiles. Furthermore, we also present a profile compression method to reduce the memory requirements. To determine the performance of our proposed model, we have used real data for the solar and wind energy profiles. The simulation results show that our model achieves 90–94% accuracy and that the compressed method reduces memory overheads by 50% as compared to state-of-the-art models.

A Solar Altitude Angle Model for Efficient Solar Energy Predictions

“…Many different prediction models have been proposed in recent years. Most of them use energy observations in prior days to predict future energy availability: QL-SEP [6], EWMA [7], WCMA [8], ASEA [9], Pro-Energy [10,11], IPro-Energy [12], SEPCS [13], UD-WCMA [14], LINE-P [15], and Adaptive LINE-P [16]. This class of models requires maintaining locally collected data about the energy harvested during a number of prior days.…”

“…SEPCS [13] and LINE-P [15] employ complex linear prediction models that take into account both previous energy samples from the same day and energy samples from previous days. Finally, Adaptive LINE-P [16] improves LINE-P with adaptive weighting parameters.…”

“…Generally, the weighting factors are tuned a priori for a particular set of data, so most of these techniques cannot dynamically adapt to varying weather conditions. To the best of our knowledge, only UD-WCMA [14] and Adaptive LINE-P [16] predictors provide effective models able to dynamically adjust the weighting factors to current weather conditions.…”

“…Different estimation techniques have been proposed in the last few years. Most of them use past energy patterns to predict future energy availability, thus requiring one to maintain several profiles of the energy harvested by the EH node for a number of prior days [6][7][8][9][10][11][12][13][14][15][16]. In this paper, we present a novel solar energy model, named the Solar Altitude Angle (SAA) model, that adopts a completely different approach.…”

An Energy Conserving and Transmission Radius Adaptive Scheme to Optimize Performance of Energy Harvesting Sensor Networks

“…With the development of microelectronics manufacture polytechnics, energy harvesting wireless sensor networks (EHWSNs) [ 39 , 40 ]—whose nodes can charge energy themselves by harvesting the energy of the surrounding environment—also develop. In EHWSNs, sensor nodes equipped with equipment that absorbs energy from the surrounding environment can replenish energy from the surrounding environment, so that they can support long-term work in an unmanned environment that does not require an energy supply, making EHWSNs more widely applicable than the usual wireless sensor networks (WSNs) [ 40 , 41 ]. For example, for an EHWSN based on solar energy, its sensor nodes add solar panels that can absorb solar energy, in order to absorb solar energy in time and to charge nodes.…”