{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T21:53:33Z","timestamp":1773784413222,"version":"3.50.1"},"reference-count":56,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2017,1,12]],"date-time":"2017-01-12T00:00:00Z","timestamp":1484179200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61603032"],"award-info":[{"award-number":["61603032"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61300074"],"award-info":[{"award-number":["61300074"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Key Technologies R&amp;D Program of China","award":["2015BAK38B01"],"award-info":[{"award-number":["2015BAK38B01"]}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2016M590048"],"award-info":[{"award-number":["2016M590048"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["06500025"],"award-info":[{"award-number":["06500025"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s17010135","type":"journal-article","created":{"date-parts":[[2017,1,12]],"date-time":"2017-01-12T10:02:08Z","timestamp":1484215328000},"page":"135","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Efficient DV-HOP Localization for Wireless Cyber-Physical Social Sensing System: A Correntropy-Based Neural Network Learning Scheme"],"prefix":"10.3390","volume":"17","author":[{"given":"Yang","family":"Xu","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1929-8447","authenticated-orcid":false,"given":"Xiong","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}]},{"given":"Weiping","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3202-1127","authenticated-orcid":false,"given":"Wenbing","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Science, Cleveland State University, Cleveland, OH 44115, USA"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1109\/MC.2005.104","article-title":"Socially aware computation and communication","volume":"38","author":"Pentland","year":"2005","journal-title":"Computer"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"721","DOI":"10.1126\/science.1167742","article-title":"Computational social science","volume":"323","author":"Lazer","year":"2009","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/2834119","article-title":"A system-level modeling and design for cyber-physical-social systems","volume":"15","author":"Zeng","year":"2016","journal-title":"ACM Trans. Embed. Comput. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zeadally, S., and Jabeur, N. (2016). Cyber-Physical System Design with Sensor Networking Technologies, The Institution of Engineering and Technology.","DOI":"10.1049\/PBCE096E"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.future.2015.10.022","article-title":"A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems","volume":"61","author":"Luo","year":"2016","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_6","first-page":"71","article-title":"Information interaction in wireless sensor networks based on socially aware computing","volume":"418","author":"Zhao","year":"2014","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_7","unstructured":"Ammar, A.B., Dziri, A., Terre, M., and Youssef, H. (2016, January 5\u20139). Multi-hop LEACH based cross-layer design for large scale wireless sensor networks. Proceedings of the International Wireless Communications and Mobile Computing Conference, Paphos, Cyprus."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1109\/TPDS.2015.2388482","article-title":"Joint optimization of lifetime and transport delay under reliability constraint wireless sensor networks","volume":"27","author":"Dong","year":"2016","journal-title":"IEEE Trans. Parallel Distrib. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, A.F., Liu, X., and Long, J. (2016). A trust-based adaptive probability marking and storage traceback scheme for WSNs. Sensors, 16.","DOI":"10.3390\/s16040451"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2010","DOI":"10.1587\/transinf.2015INP0005","article-title":"MDMA: A multi-data and multi-ACK verified selective forwarding attack detection","volume":"99","author":"Liu","year":"2016","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"30942","DOI":"10.3390\/s151229835","article-title":"An efficient scheme for selective forwarding attack detecting in WSNs","volume":"15","author":"Liu","year":"2015","journal-title":"Sensors"},{"key":"ref_12","first-page":"281","article-title":"Localization in wireless sensor networks: Classification and evaluation of techniques","volume":"22","year":"2012","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/98.878533","article-title":"GPS-less low-cost ourdoor localization for very small devices","volume":"7","author":"Bulusu","year":"2000","journal-title":"IEEE Pers. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s00521-013-1466-z","article-title":"Lagrange programming neural networks for time-of-arrival-based source localization","volume":"24","author":"Leung","year":"2014","journal-title":"Neural Comput. Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1109\/MSP.2005.1458287","article-title":"Locating the Nodes: Cooperative Localization in Wireless Sensor Networks","volume":"22","author":"Patwari","year":"2005","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1109\/TII.2015.2501225","article-title":"Time and energy efficient TOF-based device-free wireless localization","volume":"12","author":"Wang","year":"2016","journal-title":"IEEE Trans. Ind. Inf."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"6665","DOI":"10.1109\/TVT.2015.2476495","article-title":"Towards accurate device-free wireless localization with a saddle surface model","volume":"65","author":"Wang","year":"2016","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1109\/TVT.2014.2318084","article-title":"Device-free localization with multi-dimensional wireless link information","volume":"64","author":"Wang","year":"2015","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5943","DOI":"10.1109\/TIE.2012.2228145","article-title":"Robust device-free wireless localization based on differential RSS measurements","volume":"60","author":"Wang","year":"2013","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Golestanian, M., and Poellabauer, C. (2016, January 15\u201318). Localization in heterogeneous wireless sensor networks using elliptical range estimation. Proceedings of the International Conference on Computing, Networking and Communications, Kauai, HI, USA.","DOI":"10.1109\/ICCNC.2016.7440701"},{"key":"ref_21","first-page":"1","article-title":"A new DV-Hop algorithm for wireless sensor networks","volume":"33","author":"Zhang","year":"2010","journal-title":"Chin. J. Electron Dev."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Liu, P.X., Zhang, X.M., Tian, S., Zhao, Z.W., and Sun, P. (2007, January 22\u201328). A novel virtual anchor node-based localization algorithm for wireless sensor networks. Proceedings of the Sixth International Conference on Networking, Sainte-Luce, France.","DOI":"10.1109\/ICN.2007.8"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yi, T.T., Fang, Z.Y., and Li, R.X. (2010, January 12\u201314). RMADV-Hop: An improved DV-Hop localization algorithm. Proceedings of the Seventh International Conference on Information Technology, Las Vegas, NV, USA.","DOI":"10.1109\/ITNG.2010.66"},{"key":"ref_24","first-page":"16","article-title":"An improved DV-Hop algorithm based on RSSI revising","volume":"45","author":"Fang","year":"2012","journal-title":"Commun. Technol."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"188","DOI":"10.3923\/jse.2015.188.194","article-title":"DV-Hop localization algorithm based on RSSI correction","volume":"9","author":"Zhang","year":"2015","journal-title":"J. Softw. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Xie, H., Li, W.S., Li, S.B., and Xu, B.G. (2016, January 27\u201329). An improved DV-Hop localization algorithm based on RSSI auxiliary ranging. Proceedings of the Chinese Control Conference, Chengdu, China.","DOI":"10.1109\/ChiCC.2016.7554681"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Peyvandi, M., and Pouyan, A.A. (2015, January 16\u201317). An improved DV-Hop localization algorithm in wireless sensor networks. Proceedings of the Signal Processing and Intelligent Systems Conference, Tehran, Iran.","DOI":"10.1109\/SPIS.2015.7422331"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1007\/978-3-662-48768-6_31","article-title":"An improved DV-Hop node localization algorithm combined with RSSI ranging technology","volume":"367","author":"Shi","year":"2016","journal-title":"Lect. Notes Electr. Eng."},{"key":"ref_29","unstructured":"Chen, H., Sezaki, K., Deng, P., and So, H.C. (2008, January 3\u20135). An improved DV-Hop localization algorithm for wireless sensor networks. Proceedings of the 3rd IEEE Conference on Industrial Electronics and Applications, Singapore."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1049\/iet-wss.2013.0055","article-title":"Effective neural network-based node localisation scheme for wireless sensor networks","volume":"4","author":"Chuang","year":"2014","journal-title":"IET Wirel. Sens. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Borenovic, M.N., and Neskovic, A.M. (2009, January 18\u201323). Comparative analysis of RSSI, SNR and noise level parameters applicability for WLAN positioning purposes. Proceedings of the IEEE EUROCON, St. Petersburg, Russia.","DOI":"10.1109\/EURCON.2009.5167905"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/JSEN.2015.2510020","article-title":"Non-parametric and semi-parametric RSSI\/distance modeling for target tracking in wireless sensor networks","volume":"16","author":"Mahfouz","year":"2016","journal-title":"IEEE Sens. J."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme learning machine for regression and multiclass classification","volume":"42","author":"Huang","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B Cybern."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1016\/j.neucom.2015.03.112","article-title":"Regression and classification using extreme learning machine based on L1-norm and L2-norm","volume":"174","author":"Luo","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2652","DOI":"10.1587\/transinf.2013THP0019","article-title":"A Taylor based localization algorithm for wireless sensor network using extreme learning machine","volume":"97","author":"Luo","year":"2014","journal-title":"IEICE Trans. Inf. Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1007\/s12555-014-0309-8","article-title":"A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks","volume":"13","author":"Luo","year":"2015","journal-title":"Int. J. Control Autom. Syst."},{"key":"ref_37","unstructured":"Chang, X.H., and Luo, X. (July, January 29). An improved self-localization algorithm for Ad hoc network based on extreme learning machine. Proceedings of the 11th World Congress on Intelligent Control and Automation, Shenyang, China."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1977","DOI":"10.1007\/s00521-012-1184-y","article-title":"Training extreme learning machine via regularized correntropy criterion","volume":"23","author":"Xing","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"18410","DOI":"10.3390\/s141018410","article-title":"Distributed RSS-Based localization in wireless sensor networks based on second-order cone programming","volume":"14","author":"Tomic","year":"2014","journal-title":"Sensors"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Man, D.P., Qin, G.D., Yang, W., and Xuan, S.C. (2014). Improved DV-Hop algorithm for enhancing localization accuracy in WSN. Appl. Mech. Mater., 3256\u20133259.","DOI":"10.4028\/www.scientific.net\/AMM.543-547.3256"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Tian, S., Zhang, X., Liu, P., and Wang, X. (2007, January 21\u201325). A RSSI-based DV-hop algorithm for wireless sensor networks. Proceedings of the 3rd International Conference on Wireless Communications, Networking and Mobile Computing, Shanghai, China.","DOI":"10.1109\/WICOM.2007.636"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Benkic, K., Malajner, M., Planinsic, P., and Cucej, Z. (2008, January 25\u201328). Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. Proceedings of the 15th International Conference on Systems, Signals and Image Processing, Bratislava, Slovakia.","DOI":"10.1109\/IWSSIP.2008.4604427"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"164","DOI":"10.1145\/1089733.1089736","article-title":"Topology control in wireless ad hoc and sensor networks","volume":"37","author":"Santi","year":"2005","journal-title":"ACM Comput. Surv."},{"key":"ref_44","first-page":"126","article-title":"DV-Hop localization algorithm for wireless sensor networks based on RSSI ratio improving","volume":"32","author":"Yang","year":"2012","journal-title":"Transducer Microsyst. Technol."},{"key":"ref_45","unstructured":"Huang, G.B., Zhu, Q.Y., and Siew, C.K. (2004, January 25\u201329). Extreme learning machine: A new learning scheme of feedforward neural networks. Proceedings of the IEEE International Joint Conference on Neural Networks, Budapest, Hungary."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1109\/TSP.2002.1011217","article-title":"An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems","volume":"50","author":"Erdogmus","year":"2002","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"2187","DOI":"10.1109\/TSP.2006.872524","article-title":"Generalized correlation function: Definition, properties, and application to blind equalization","volume":"54","author":"Santamaria","year":"2006","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"5286","DOI":"10.1109\/TSP.2007.896065","article-title":"Correntropy: Properties and applications in non-Gaussian signal processing","volume":"55","author":"Liu","year":"2007","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (1995). The Nature of Statistical Learning Theory, Springer.","DOI":"10.1007\/978-1-4757-2440-0"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/LSP.2012.2204435","article-title":"Maximum correntropy estimation is a smoothed MAP estimation","volume":"19","author":"Chen","year":"2012","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1561","DOI":"10.1109\/TPAMI.2010.220","article-title":"Maximum correntropy criterion for robust face recognition","volume":"33","author":"He","year":"2011","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yuan, X., and Hu, B.G. (2009, January 14\u201318). Robust feature extraction via information theoretic learning. Proceedings of the 26th International Conference on Machine Learning, Montreal, QC, Canada.","DOI":"10.1145\/1553374.1553526"},{"key":"ref_53","unstructured":"Rockfellar, R. (1970). Convex Analysis, Princeton University Press."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Boyd, S., and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press.","DOI":"10.1017\/CBO9780511804441"},{"key":"ref_55","first-page":"1","article-title":"An improved DV-hop node localization algorithm based on unbiased estimation for wireless sensor networks","volume":"48","author":"Wei","year":"2014","journal-title":"Hsi An Chiao Tung Ta Hsueh"},{"key":"ref_56","first-page":"2863","article-title":"Localization algorithm for sensor node based on RSSI ranging and DV-Hop error correcting","volume":"20","author":"Ren","year":"2012","journal-title":"Comput. Meas. Control"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/1\/135\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:26:01Z","timestamp":1760207161000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/1\/135"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,1,12]]},"references-count":56,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2017,1]]}},"alternative-id":["s17010135"],"URL":"https:\/\/doi.org\/10.3390\/s17010135","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,12]]}}}