Abstract
The paper introduces a method which improves localization accuracy of the signal strength fingerprinting approach. According to the proposed method, entire localization area is divided into regions by clustering the fingerprint database. For each region a prototype of the received signal strength is determined and a dedicated artificial neural network (ANN) is trained by using only those fingerprints that belong to this region (cluster). Final estimation of the location is obtained by fusion of the coordinates delivered by selected ANNs. Sensor nodes have to store only the signal strength prototypes and synaptic weights of the ANNs in order to estimate their locations. This approach significantly reduces the amount of memory required to store a received signal strength map. Various ANN topologies were considered in this study. Improvement of the localization accuracy as well as speedup of learning process was achieved by employing fully connected neural networks. The proposed method was verified and compared against state-of-the-art localization approaches in real world indoor environment by using both stationary and mobile sensor nodes.
Highlights
Localization of sensor nodes is a necessary function for various emerging applications of wireless sensor networks (WSNs), such as road traffic control [1] and target tracking [2]
The experimental results presented in this paper confirm that, for indoor environment, the received signal strength indicators (RSSI) values obtained from propagation models and measurements differ significantly
The range-free localization methods that use ANNbased fingerprinting were considered in this study
Summary
Localization of sensor nodes is a necessary function for various emerging applications of wireless sensor networks (WSNs), such as road traffic control [1] and target tracking [2]. For range-based methods, the distance information can be obtained by analyzing time of arrival (TOA), time difference of arrival (TDOA), angle of arrival (AOA), or received signal strength indicators (RSSI) [8]. TDOA uses two transmission signals of different propagation speeds It requires two different transmitters and receivers on each node. There is a considerable research interest in developing fingerprint localization methods based on artificial neural networks (ANNs) [10]. In this paper a method is proposed that improves localization accuracy of the ANN-based fingerprinting. Final estimation of the location is obtained by fusion of the coordinates delivered by ANNs. Further improvement of the localization accuracy as well as speedup of learning process was achieved by employing fully connected neural networks [11].
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