Abstract
Wireless sensor networks (WSN) take on an invaluable technology in many applications. Their prevalence, however, is threatened by a number of technical difficulties, especially the shortage of energy in sensors. To mitigate this problem, we propose a smart reduction in data communication by sensors. Indeed, in case we have a solution to this end, the components of a sensor, including its radio, can be turned off most of the time without noticeable influence on network operation. Thus, reducing the acquired data, the sensors can be idle for longer and power can be saved. The main idea in devising such a solution is to minimize the correlation between the data communicated. In order to reduce the measurements, we present a data prediction method based on neural networks which performs an adaptive, data-driven, and non-uniform sampling. Evidently, the amount of possible reduction in required samples is bounded by the extent to which the sensed data is stationary. The proposed method is validated on simulated and experimental data. The results show that it leads to a considerable reduction of the number of samples required (and hence also a power saving) while still providing a good approximation of the data.
Highlights
Wireless sensor networks (WSN) have received a great attention in recent years
Energy saving is crucial to the operation of WSNs, and devising methods for efficient power consumption is central to the research in this area
A multilayer perceptron (MLP) was used to perform periodically a forward prediction on 100 realizations of stochastic inputs extracted from a uniform probability distribution with mean and range given by the available data and their uncertainty, respectively
Summary
Wireless sensor networks (WSN) have received a great attention in recent years. They have a wide variety of applications such as event detection, target tracking, environment sensing, elder people monitoring, and security [1,2,3,4,5,6,7,8]. Energy saving is crucial to the operation of WSNs, and devising methods for efficient power consumption is central to the research in this area. Many approaches were proposed to reduce the power consumption of a sensor network, but the following three main techniques are the most important among them [9,10]: duty cycling, data-driven approaches, and mobility. Reducing extra communications is a way to save energy which can be followed by data-driven techniques. Two acquisitions, there are no transmissions in order to save energy
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