Abstract
Integrating wireless sensor network (WSN) into the emerging computing paradigm, e.g., cyber-physical social sensing (CPSS), has witnessed a growing interest, and WSN can serve as a social network while receiving more attention from the social computing research field. Then, the localization of sensor nodes has become an essential requirement for many applications over WSN. Meanwhile, the localization information of unknown nodes has strongly affected the performance of WSN. The received signal strength indication (RSSI) as a typical range-based algorithm for positioning sensor nodes in WSN could achieve accurate location with hardware saving, but is sensitive to environmental noises. Moreover, the original distance vector hop (DV-HOP) as an important range-free localization algorithm is simple, inexpensive and not related to the environment factors, but performs poorly when lacking anchor nodes. Motivated by these, various improved DV-HOP schemes with RSSI have been introduced, and we present a new neural network (NN)-based node localization scheme, named RHOP-ELM-RCC, through the use of DV-HOP, RSSI and a regularized correntropy criterion (RCC)-based extreme learning machine (ELM) algorithm (ELM-RCC). Firstly, the proposed scheme employs both RSSI and DV-HOP to evaluate the distances between nodes to enhance the accuracy of distance estimation at a reasonable cost. Then, with the help of ELM featured with a fast learning speed with a good generalization performance and minimal human intervention, a single hidden layer feedforward network (SLFN) on the basis of ELM-RCC is used to implement the optimization task for obtaining the location of unknown nodes. Since the RSSI may be influenced by the environmental noises and may bring estimation error, the RCC instead of the mean square error (MSE) estimation, which is sensitive to noises, is exploited in ELM. Hence, it may make the estimation more robust against outliers. Additionally, the least square estimation (LSE) in ELM is replaced by the half-quadratic optimization technique. Simulation results show that our proposed scheme outperforms other traditional localization schemes.
Highlights
In recent years, there has been an emerging interest in the field of socially-aware computing through integrating social computing and pervasive computing [1,2]
Motivated by the scheme of neural network (NN)-based node localization with received signal strength indication (RSSI) and hop counts [30], we present a novel distance vector hop (DV-HOP) localization scheme with RSSI and regularized correntropy criterion (RCC)-based extreme learning machine (ELM) (ELM-RCC), named RHOP-ELM-RCC, to improve the performance of wireless sensor network (WSN) in the cyber-physical social sensing (CPSS)
Is constructed and trained by using these N × N training samples and learning algorithm ELM-RCC, the coordinate of the unknown node j could be estimated by exploiting q the trained single hidden layer feedforward network (SLFN) on the basis of input vector d j = (d jl )l =1, where q is the number of anchors, d jl is the distance between unknown node j and anchor l, which could be obtained using
Summary
There has been an emerging interest in the field of socially-aware computing through integrating social computing and pervasive computing [1,2]. Some novel methods have been proposed on the basis of DV-HOP to enhance the accuracy of localization [22,23,24] Taking advantage of both the range-free method and the range-based method, some algorithms were proposed by incorporating RSSI and DV-HOP to execute the localization for unknown nodes [25,26,27,28]. In this way, the localization error of the unknown and anchor nodes could be reduced effectively.
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