Two-Phase Stochastic Optimization to Sensor Network Localization

In this paper we describe a novel approach to sensor network localization, i.e., two-phase algorithms based on simulated annealing and genetic algorithm. The numerical results presented and discussed in the final part of the paper show that these novel schemes give accurate and consistent location estimates of the nodes in the network. The performance is better and speed is faster than that of the semidefinite programming (SDP) and one-phase simulated annealing (SA).

[1]  Branka Vucetic,et al.  Simulated annealing based localization in wireless sensor network , 2005, The IEEE Conference on Local Computer Networks 30th Anniversary (LCN'05)l.

[2]  Yinyu Ye,et al.  Semidefinite programming for ad hoc wireless sensor network localization , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[3]  L. El Ghaoui,et al.  Convex position estimation in wireless sensor networks , 2001, Proceedings IEEE INFOCOM 2001. Conference on Computer Communications. Twentieth Annual Joint Conference of the IEEE Computer and Communications Society (Cat. No.01CH37213).

[4]  Branka Vucetic,et al.  Simulated Annealing based Wireless Sensor Network Localization with Flip Ambiguity Mitigation , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[5]  B. R. Badrinath,et al.  Ad hoc positioning system (APS) , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[6]  Brian D. O. Anderson,et al.  Wireless sensor network localization techniques , 2007, Comput. Networks.

[7]  Ying Zhang,et al.  Localization from connectivity in sensor networks , 2004, IEEE Transactions on Parallel and Distributed Systems.

[8]  Stephen P. Boyd,et al.  Further Relaxations of the SDP Approach to Sensor Network Localization , 2007 .

[9]  Xiang Ji,et al.  Sensor positioning in wireless ad-hoc sensor networks using multidimensional scaling , 2004, IEEE INFOCOM 2004.