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Sensors 2017, 17(1), 135; doi:10.3390/s17010135

Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme

1,2
,
1,2,* , 1,2,* and 3
1
School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China
2
Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China
3
Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA
*
Authors to whom correspondence should be addressed.
Academic Editors: Mianxiong Dong, Zhi Liu, Anfeng Liu and Didier El Baz
Received: 30 October 2016 / Revised: 3 January 2017 / Accepted: 5 January 2017 / Published: 12 January 2017
(This article belongs to the Special Issue New Paradigms in Cyber-Physical Social Sensing)
View Full-Text   |   Download PDF [569 KB, uploaded 12 January 2017]   |  

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. View Full-Text
Keywords: wireless sensor network (WSN); received signal strength indication (RSSI); distance vector hop (DV-HOP); regularized correntropy criterion (RCC); extreme learning machine (ELM) wireless sensor network (WSN); received signal strength indication (RSSI); distance vector hop (DV-HOP); regularized correntropy criterion (RCC); extreme learning machine (ELM)
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Xu, Y.; Luo, X.; Wang, W.; Zhao, W. Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme. Sensors 2017, 17, 135.

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