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.
Highlights
Improvements have been made in the domain of energy storage, those storage devices still have numerous shortcomings such as size, installation, maintenance and cost, especially for wireless sensor network (WSN) nodes [1]
Energy sources such as solar and wind are characterized by significant variations and intermittence; it is challenging to guarantee that the WSN nodes always have the necessary energy to operate
This section comprises two parts: in the first one, the state of the art related to solar and wind based energy prediction models is discussed in detail; and, in the second part, we describe and discuss the proposed LINE-P model
Summary
Improvements have been made in the domain of energy storage (e.g., supercapacitor and lithium battery), those storage devices still have numerous shortcomings such as size, installation, maintenance and cost, especially for WSNs nodes [1]. Various energy harvesting methods and techniques have been proposed and developed over the last decade [2]; such approaches are typically used to complement more traditional energy storage devices such as rechargeable batteries and supercapacitors, or even to replace them all together as in battery-less nodes that operate according to the principles of transient computing [3]. Energy sources such as solar and wind are characterized by significant variations and intermittence; it is challenging to guarantee that the WSN nodes always have the necessary energy to operate. On-line mechanisms such as dynamic duty cycling and/or dynamic frequency and voltage scaling can be used to modulate the energy consumption of the WSN node according to the available energy
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