Dual-Source Linear Energy Prediction (LINE-P) Model in the Context of WSNs

Abstract

Energy harvesting technologies such as miniature power solar panels and micro wind turbines are increasingly used to help power wireless sensor network nodes. However, a major drawback of energy harvesting is its varying and intermittent characteristic, which can negatively affect the quality of service. This calls for careful design and operation of the nodes, possibly by means of, e.g., dynamic duty cycling and/or dynamic frequency and voltage scaling. In this context, various energy prediction models have been proposed in the literature; however, they are typically compute-intensive or only suitable for a single type of energy source. In this paper, we propose Linear Energy Prediction “LINE-P”, a lightweight, yet relatively accurate model based on approximation and sampling theory; LINE-P is suitable for dual-source energy harvesting. Simulations and comparisons against existing similar models have been conducted with low and medium resolutions (i.e., 60 and 22 min intervals/24 h) for the solar energy source (low variations) and with high resolutions (15 min intervals/24 h) for the wind energy source. The results show that the accuracy of the solar-based and wind-based predictions is up to approximately 98% and 96%, respectively, while requiring a lower complexity and memory than the other models. For the cases where LINE-P’s accuracy is lower than that of other approaches, it still has the advantage of lower computing requirements, making it more suitable for embedded implementation, e.g., in wireless sensor network coordinator nodes or gateways.

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

“…In [ 5 ] the authors presented three cases of LINE-P (linear energy prediction model) that are based on the sampling and approximation theory. The authors showed that LINE-P (all cases) is more accurate, has a lower complexity, and is energy-efficient in terms of computation as compared to other non-adaptive EP models.…”

“…In this article, we first briefly recall the limitations of most existing EP models due to the fixed-weighting parameter issue; thereafter, we suggest a solution with an adaptive weighting factor based on the energy profiles. In [ 5 ], we discussed how most of the energy prediction models such as EWMA, WCMA, ASEA, PRO-Energy, QL-SEP, and LINE-P (all cases) are dependent on a fixed weighting parameter; however, these solutions are not always suitable for real implementations with many various types (and hence characteristics) of e.g., solar energy harvesters.…”

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.…”

“…We will provide a more detailed description of Pro-Energy and UD-WCMA in the following subsections. 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.…”

“…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.…”