Efficient DV-HOP localization forwireless cyber-physical social sensing system: A correntropy-based neural network learning scheme

Sensors (Switzerland)

Volumn 17, Issue 1, 2017, Pages

  Xu, Yang ^a   Luo, Xiong ^a   Wang, Weiping ^a   Zhao, Wenbing ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING   (China)

^b Cleveland State University   (United States)

Author keywords

Distance vector hop (DV HOP); Extreme learning machine (ELM); Received signal strength indication (RSSI); Regularized correntropy criterion (RCC); Wireless sensor network (WSN)

Indexed keywords

KNOWLEDGE ACQUISITION; LEARNING SYSTEMS; MEAN SQUARE ERROR; NETWORK LAYERS; OPTIMIZATION; QUADRATIC PROGRAMMING; SENSOR NODES; SOCIAL SCIENCES COMPUTING;

CORRENTROPY; DISTANCE VECTOR; EMERGING COMPUTING PARADIGM; EXTREME LEARNING MACHINE; GENERALIZATION PERFORMANCE; HALF-QUADRATIC OPTIMIZATIONS; LOCALIZATION OF SENSOR NODES; RECEIVED SIGNAL STRENGTH INDICATION;

WIRELESS SENSOR NETWORKS;

EID: 85010015610     PISSN: 14248220     EISSN: None     Source Type: Journal    
DOI: 10.3390/s17010135     Document Type: Article

References (56)

1. Pentland, A. Socially aware computation and communication. Computer 2005, 38, 33-40.
2. Lazer, D.; Pentland, A.; Adamic, L. Computational social science. Science 2009, 323, 721-723.
3. Zeng, J.; Yang, L.T.; Ma, J.H. A system-level modeling and design for cyber-physical-social systems. ACM Trans. Embed. Comput. Syst. 2016, 15, 1-26.
4. Abbas, W.; Laszka, A.; Koutsoukos, X. Resilient wireless sensor networks for cyber-physical systems. In Cyber-Physical System Design with Sensor Networking Technologies; Zeadally, S., Jabeur, N., Eds.; The Institution of Engineering and Technology: London, UK, 2016; pp. 239-267.
5. Luo, X.; Zhang, D.D.; Yang, L.T.; Liu, J.; Chang, X.H.; Ning, H.S. A kernel machine-based secure data sensing and fusion scheme in wireless sensor networks for the cyber-physical systems. Future Gener. Comput. Syst. 2016, 61, 85-96.
6. Zhao, H.; Ma, X.; Shi, C. Information interaction in wireless sensor networks based on socially aware computing. Commun. Comput. Inf. Sci. 2014, 418, 71-81.
7. Ammar, A.B.; Dziri, A.; Terre, M.; Youssef, H. Multi-hop LEACH based cross-layer design for large scale wireless sensor networks. In Proceedings of the InternationalWireless Communications and Mobile Computing Conferenc, Paphos, Cyprus, 5-9 September 2016; pp. 763-768.
8. Dong, M.X.; Ota, K.; Liu, A.F.; Guo, M. Joint optimization of lifetime and transport delay under reliability constraint wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 2016, 27, 225-236.
9. Liu, A.F.; Liu, X.; Long, J. A trust-based adaptive probability marking and storage traceback scheme for WSNs. Sensors 2016, 16, 451.
10. Liu, A.; Liu, X.; Li, H.; Long, J. MDMA: A multi-data and multi-ACK verified selective forwarding attack detection. IEICE Trans. Inf. Syst. 2016, 99, 2010-2018.
11. Liu, A.F.; Dong, M.X.; Ota, K.; Long, J. An efficient scheme for selective forwarding attack detecting in WSNs. Sensors 2015, 15, 30942-30963.
12. Niewiadomska-Szynkiewicz, H. Localization in wireless sensor networks: Classification and evaluation of techniques. Comput. Methods Appl. Mech. Eng. 2012, 22, 281-297.
13. Bulusu, N.; Heidemann, J.; Estrin, D. GPS-less low-cost ourdoor localization for very small devices. IEEE Pers. Commun. 2000, 7, 28-34.
14. Leung, C.S.; Sum, J.; So, H.C.; Constrantinides, A.G.; Chan, K.W. Lagrange programming neural networks for time-of-arrival-based source localization. Neural Comput. Appl. 2014, 24, 109-116.
15. Patwari, N.; Ash, J.N.; Kyperountas, S.; Hero, A.O.; Moses, R.L.; Correal, N.S. Locating the Nodes:Cooperative Localization in Wireless Sensor Networks. IEEE Signal Process. Mag. 2005, 22, 54-69.
16. Wang, J.; Gao, Q.H.; Yu, Y.; Zhang, X.; Feng, X.Y. Time and energy efficient TOF-based device-free wireless localization. IEEE Trans. Ind. Inf. 2016, 12, 158-168.
17. Wang, J.; Gao, Q.H.; Pan, M.; Zhang, X.; Yu, Y.; Wang, H.Y. Towards accurate device-free wireless localization with a saddle surface model. IEEE Trans. Veh. Technol. 2016, 65, 6665-6677.
18. Wang, J.; Gao, Q.H.; Wang, H.Y.; Cheng, P.; Xin, K.F. Device-free localization with multi-dimensional wireless link information. IEEE Trans. Veh. Technol. 2015, 64, 356-366.
19. Wang, J.; Gao, Q.H.; Yu, Y.; Cheng, P.; Wu, L.F.; Wang, H.Y. Robust device-free wireless localization based on differential RSS measurements. IEEE Trans. Ind. Electron. 2013, 60, 5943-5952.
20. Golestanian, M.; Poellabauer, C. Localization in heterogeneous wireless sensor networks using elliptical range estimation. In Proceedings of the International Conference on Computin, Networking and Communications, Kauai, HI, USA, 15-18 February 2016; pp. 1-7.
21. Zhang, P.; Sun, Y. A new DV-Hop algorithm for wireless sensor networks. Chin. J. Electron Dev. 2010, 33, 1-7.
22. Liu, P.X.; Zhang, X.M.; Tian, S.; Zhao, Z.W.; Sun, P. A novel virtual anchor node-based localization algorithm for wireless sensor networks. In Proceedings of the Sixth International Conference on Networkin, Sainte-Luce, France, 22-28 April 2007; pp. 1-6.
23. Yi, T.T.; Fang, Z.Y.; Li, R.X. RMADV-Hop: An improved DV-Hop localization algorithm. In Proceedings of the Seventh International Conference on Information Technolog, Las Vegas, NV, USA, 12-14 April 2010; pp. 939-943.
24. Fang, H.; Lei, J.; Hu, K. An improved DV-Hop algorithm based on RSSI revising. Commun. Technol. 2012, 45, 16-18.
25. Zhang, W.; Yang, X.Y.; Song, Q.X. DV-Hop localization algorithm based on RSSI correction. J. Softw. Eng. 2015, 9, 188-194.
26. Xie, H.; Li, W.S.; Li, S.B.; Xu, B.G. An improved DV-Hop localization algorithm based on RSSI auxiliary ranging. In Proceedings of the Chinese Control Conferenc, Chengdu, China, 27-29 July 2016; pp. 8319-8324.
27. Peyvandi, M.; Pouyan, A.A. An improved DV-Hop localization algorithm in wireless sensor networks. In Proceedings of the Signal Processing and Intelligent Systems Conferenc, Tehran, Iran, 16-17 December 2015; pp. 153-158.
28. Shi, H.Y.; Peng, L. An improved DV-Hop node localization algorithm combined with RSSI ranging technology. Lect. Notes Electr. Eng. 2016, 367, 269-276.
29. Chen, H.; Sezaki, K.; Deng, P.; So, H.C. An improved DV-Hop localization algorithm for wireless sensor networks. In Proceedings of the 3rd IEEE Conference on Industrial Electronics and Application, Singapore, 3-5 June 2008; pp. 1557-1561.
30. Chuang, P.J.; Jiang, Y.J. Effective neural network-based node localisation scheme for wireless sensor networks. IET Wirel. Sens. Syst. 2014, 4, 97-103.
31. Borenovic, M.N.; Neskovic, A.M. Comparative analysis of RSSI, SNR and noise level parameters applicability for WLAN positioning purposes. In Proceedings of the IEEE EUROCO, St. Petersburg, Russia, 18-23 May 2009; pp. 1895-1900.
32. Mahfouz, S.; Mourad-Chehade, F.; Honeine, P.; Farah, J. Non-parametric and semi-parametric RSSI/distance modeling for target tracking in wireless sensor networks. IEEE Sens. J. 2016, 16, 2115-2126.
33. Huang, G.B.; Zhou, H.M.; Ding, X.J.; Zhang, R. Extreme learning machine for regression and multiclass classification. IEEE Trans. Syst. Man Cybern. Part B Cybern. 2012, 42, 513-529.
34. Luo, X.; Chang, X.H.; Ban, X.J. Regression and classification using extreme learning machine based on L1-norm and L2-norm. Neurocomputing 2016, 174, 179-186.
35. Luo, X.; Chang, X.H.; Liu, H. A Taylor based localization algorithm for wireless sensor network using extreme learning machine. IEICE Trans. Inf. Syst. 2014, 97, 2652-2659.
36. Luo, X.; Chang, X.H. A novel data fusion scheme using grey model and extreme learning machine in wireless sensor networks. Int. J. Control Autom. Syst. 2015, 13, 539-546.
37. Chang, X.H.; Luo, X. An improved self-localization algorithm for Ad hoc network based on extreme learning machine. In Proceedings of the 11th World Congress on Intelligent Control and Automation, Shenyang, China, 29 June-4 July 2014; pp. 564-569.
38. Xing, H.J.; Wang, X.M. Training extreme learning machine via regularized correntropy criterion. Neural Comput. Appl. 2013, 23, 1977-1986.
39. Tomic, S.; Beko, M.; Dinis, R. Distributed RSS-Based localization in wireless sensor networks based on second-order cone programming. Sensors 2014, 14, 18410-18432.
40. Man, D.P.; Qin, G.D.; Yang, W.; Xuan, S.C. Improved DV-Hop algorithm for enhancing localization accuracy in WSN. Appl. Mech. Mater. 2014, 3256-3259.
41. Tian, S.; Zhang, X.; Liu, P.; Wang, X. A RSSI-based DV-hop algorithm for wireless sensor networks. In Proceedings of the 3rd International Conference onWireless Communication, Networking and Mobile Computing, Shanghai, China, 21-25 September 2007; pp. 2555-2558.
42. Benkic, K.; Malajner, M.; Planinsic, P.; Cucej, Z. Using RSSI value for distance estimation in wireless sensor networks based on ZigBee. In Proceedings of the 15th International Conference on System, Signals and Image Processing, Bratislava, Slovakia, 25-28 June 2008; pp. 303-306.
43. Santi, P. Topology control in wireless ad hoc and sensor networks. ACM Comput. Surv. 2005, 37, 164-194.
44. Yang, X.; Pan,W. DV-Hop localization algorithm for wireless sensor networks based on RSSI ratio improving. Transducer Microsyst. Technol. 2012, 32, 126-128.
45. Huang, G.B.; Zhu, Q.Y.; Siew, C.K. Extreme learning machine: A new learning scheme of feedforward neural networks. In Proceedings of the IEEE International Joint Conference on Neural Network, Budapest, Hungary, 25-29 July 2004; pp. 985-990.
46. Erdogmus, D.; Principe, J.C. An error-entropy minimization algorithm for supervised training of nonlinear adaptive systems. IEEE Trans. Signal Process. 2002, 50, 1780-1786.
47. Santamaria, I.; Pokharel, P.P.; Principe, J.C. Generalized correlation function: Definition, properties, and application to blind equalization. IEEE Trans. Signal Process. 2006, 54, 2187-2197.
48. Liu, W.F.; Pokharel, P.P.; Principe, J.C. Correntropy: Properties and applications in non-Gaussian signal processing. IEEE Trans. Signal Process. 2007, 55, 5286-5297.
49. Vapnik, V. The Nature of Statistical Learning Theory; Springer: New York, NY, USA, 1995.
50. Chen, B.D.; Principe, J.C. Maximum correntropy estimation is a smoothed MAP estimation. IEEE Signal Process. Lett. 2012, 19, 491-494.
51. He, R.; Zheng,W.S.; Hu, B.G. Maximum correntropy criterion for robust face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 2011, 33, 1561-1576.
52. Yuan, X.; Hu, B.G. Robust feature extraction via information theoretic learning. In Proceedings of the 26th International Conference on Machine Learnin, Montreal, QC, Canada, 14-18 June 2009; pp. 1193-1200.
53. Rockfellar, R. Convex Analysis; Princeton University Press: Princeton, NY, USA, 1970.
54. Boyd, S.; Vandenberghe, L. Convex Optimization; Cambridge University Press: Cambridge, MA, USA, 2004.
55. Wei, Q.R.; Liu, J.; Han, J.Q. An improved DV-hop node localization algorithm based on unbiased estimation for wireless sensor networks. Hsi An Chiao Tung Ta Hsueh 2014, 48, 1-6.
56. Ren, H.; Zhu, L.; Yang, A. Localization algorithm for sensor node based on RSSI ranging and DV-Hop error correcting. Comput. Meas. Control 2012, 20, 2863-2866.