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

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          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.

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          Locating the nodes: cooperative localization in wireless sensor networks

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            GPS-less low-cost outdoor localization for very small devices

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                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                12 January 2017
                January 2017
                : 17
                : 1
                : 135
                Affiliations
                [1 ]School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China; b20160304@ 123456xs.ustb.edu.cn
                [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; w.zhao1@ 123456csuohio.edu
                Author notes
                [* ]Correspondence: xluo@ 123456ustb.edu.cn (X.L.); weipingwangjt@ 123456ustb.edu.cn (W.W.); Tel.: +86-10-6233-2873 (X.L. & W.W.)
                Article
                sensors-17-00135
                10.3390/s17010135
                5298708
                28085084
                fea65e24-2614-4604-9efd-cdda2254063c
                © 2017 by the authors; licensee MDPI, Basel, Switzerland.

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 30 October 2016
                : 05 January 2017
                Categories
                Article

                Biomedical engineering
                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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