Using laplacian eigenmap as heuristic information to solve nonlinear constraints defined on a graph and its application in distributed range-free localization of wireless sensor networks

Neural Processing Letters

Volumn 37, Issue 3, 2013, Pages 411-424

  Li, Shuai ^a   Wang, Zheng ^b   Li, Yangming ^c  

^a Stevens Institute of Technology   (United States)

^b UNIVERSITY OF TORONTO   (Canada)

^c INSTITUTE OF INTELLIGENT MACHINES   (China)

Author keywords

Constrained optimization; Laplacian eigenmap; Projected dynamic neural network; Wireless sensor networks

Indexed keywords

BETTER PERFORMANCE; FEASIBLE SOLUTION SET; HEURISTIC INFORMATION; LAPLACIAN EIGENMAP; NON-LINEAR CONSTRAINTS; NONLINEAR INEQUALITY; PROJECTED DYNAMICS; RANGE-FREE LOCALIZATIONS;

CONSTRAINED OPTIMIZATION; ITERATIVE METHODS; LAPLACE TRANSFORMS; WIRELESS SENSOR NETWORKS;

NEURAL NETWORKS;

EID: 84878160865     PISSN: 13704621     EISSN: 1573773X     Source Type: Journal    
DOI: 10.1007/s11063-012-9255-8     Document Type: Article

References (41)

1. Jeong H, Tombor B, Albert R, Oltvai ZN, Barabasi AL (2000) The large-scale organization of metabolic networks. Nature 407: 651
2. Buldyrev SV, Parshani R, Paul G, Stanley HE, Havlin S (2009) Catastrophic cascade of failures in interdependent networks. Nature 464(7291): 1025-1028
3. Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E (2002) Wireless sensor networks: a survey. Comput Netw 38: 393-422
4. Mohammed JZ, Vinay N, Deb B, Chris B (2004) Predicting protein folding pathways. Bioinformatics 20(1): 386-393
5. Cao YU, Fukunaga AS, Kahng AB (1997) Cooperative mobile robotics: antecedents and directions. Auton Robots 4: 226-234
6. Bullo F, Cortés J, Martínez S (2009) Distributed control of robotic networks. Applied Mathematics Series. Princeton University Press. Electronically available at http://coordinationbook.info
7. Kulaib AR, Shubair RM, Al-Qutayri MA, Ng JWP (2011) An overview of localization techniques for wireless sensor networks. Innovations in Information Technology (IIT), 2011 International Conference on, pp 167-172, April 2011
8. Reich J, Misra V, Rubenstein D, Zussman G (2011) Connectivity maintenance in mobile wireless networks via constrained mobility. In INFOCOM, 2011 Proceedings IEEE, pp 927-935, April 2011
9. Mathar R, Niessen T (2000) Optimum positioning of base stations for cellular radio networks. Wirel. Netw 6(6): 421-428
10. Ghrist R, Muhammad A (2005) Coverage and hole-detection in sensor networks via homology. In IPSN '05: Proceedings of the 4th international symposium on information processing in sensor networks. IEEE Press, Piscataway
11. Li S, Wang Y, Yu J, Liu B (2013) A nonlinear model to generate the winner-take-all competition. Commun Nonlinear Sci Numer Simul 18(3): 435-442
12. Xia Y (2009) A compact cooperative recurrent neural network for computing general constrained l1 norm estimators. IEEE Trans Signal Process 57(9): 3693-3697
13. Niranjan B, Burrows TL, Niranjan M (1994) The use of recurrent neural networks for classification. In IEEE workshop on neural networks for signal processing IV pp 117-125
14. Husken M, Stagge P (2003) Recurrent neural networks for time series classification. Neurocomputing 50: 223-235
15. Li S, Cui H, Li Y, Liu B, Lou Y (2012) Decentralized control of collaborative redundant manipulators with partial command coverage via locally connected recurrent neural networks. Neural Comput Appl 1-10
16. Li S, Chen S, Liu B, Li Y, Liang Y (2012) Decentralized kinematic control of a class of collaborative redundant manipulators via recurrent neural networks. Neurocomputing 91(0): 1-10
17. Tong M, Bickett A, Christiansen E, Cottrell G (2007) Learning grammatical structure with Echo State Networks. Neural Netw 20(3): 424-432
18. Li S, Chen S, Liu B (2012) Accelerating a recurrent neural network to finite-time convergence for solving time-varying sylvester equation by using a sign-bi-power activation function. Neural Process Lett 1-17. doi: 10.1007/s11063-012-9241-1
19. Doherty L, pister KSJ, Ghaoui EL (2001) Convex position estimation in wireless sensor networks. In INFOCOM 2001. Twentieth annual joint conference of the IEEE computer and communications societies. Proceedings. IEEE,vol 3:1655-1663
20. Niculescu D, Nath B (2003) Dv based positioning in ad hoc networks. Telecommun Syst 22(1): 267-280
21. He T, Huang C, Blum Brian M, Stankovic JA, Abdelzaher T (2003) Range-free localization schemes for large scale sensor networks. In Proceedings of the 9th annual international conference on Mobile computing and networking, MobiCom '03, ACM, New York, pp 81-95
22. Hopfield JJ (1984) Neurons with graded response have collective computational properties like those of two-state neurons. Proc Natl Acad Sci 81(10): 3088-3092
23. Kennedy MP, Chua LO (1988) Neural networks for nonlinear programming. Circuits Syst IEEE Trans 35(5): 554-562
24. Xia Y, Ye D (2008) On exponential convergence conditions of an extended projection neural network. Neural Comput 20(9): 2227-2237
25. Jiang S, Han D, Yuan X (2012) Efficient neural networks for solving variational inequalities. Neurocomputing 86(0): 97-106
26. Liu L, Ge R, Gao P (2011) A novel neural network for solving singular nonlinear convex optimization problems. In neural information processing, vol 7063 of Lecture Notes in Computer Science, pp 554-561
27. Li S, Chen S, Lou Y, Lu B, Liang Y (2012) A recurrent neural network for inter-localization of mobile phones. In neural networks (IJCNN), The 2012 International Joint Conference on June 2012, pp 1-5
28. Li S, Lou Y, Liu B Bluetooth aided mobile phone localization: a nonlinear neural circuit approach. ACM Transac Embed Comput Syst. Accepted
29. Li S, Qin F A dynamic neural network approach for solving nonlinear inequalities defined on a graph and its application to distributed, routing-free, range-free localization of WSNS. Neurocomputing. Accepted
30. Li S, Liu B, Chen B, Lou Y (2012) Neural network based mobile phone localization using bluetooth connectivity. Neural Comput Appl, 1-9
31. Zhang Y, Wang J (2002) A dual neural network for convex quadratic programming subject to linear equality and inequality constraints. Phys Lett A 298(4): 271-278
32. Xia Y, Feng G, Wang J (2004) A recurrent neural network with exponential convergence for solving convex quadratic program and related linear piecewise equations. Neural Netw 17(7): 1003-1015
33. Wu N (1997) The maximum entropy method (Springer series in information sciences). Springer, Berlin
34. Belkin M, Niyogi P (2001) Laplacian eigenmaps and spectral techniques for embedding and clustering. In advances in neural information processing systems 14, MIT Press, Cambridge, pp 585-591
35. Boyd S, Vandenberghe L (2004) Convex optimization. Cambridge University Press, New York
36. Xia Y (2004) An extended projection neural network for constrained optimization. Neural Comput 16: 863-883
37. Nedic A, Ozdaglar A (2009) Subgradient methods for saddle-point problems. J Optim Theory Appl 142: 205-228
38. Mao G, Fidan B, Anderson BDO (2007) Wireless sensor network localization techniques. Computer Netw 51(10): 2529-2553
39. Biswas P, Liang TC, Wang TC, Ye Y (2006) Semidefinite programming based algorithms for sensor network localization. ACM Trans Sens Netw 2: 2006
40. Shang Y, Ruml W, Zhang Y, Fromherz MPJ (2003) Localization from mere connectivity. In Proceedings of the 4th ACM international symposium on Mobile ad hoc networking & computing, MobiHoc '03. ACM, NY, pp 201-212
41. Stankovic JA, Stoleru R, He T (2007) Secure Localization and Time Synchronization for Wireless Sensor and Ad Hoc Networks, vol 30, chapter range-free localization. Springer