Range-based localization in mobile sensor networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3868 LNCS, Issue , 2006, Pages 164-179

  Dil, Bram ^a   Dulman, Stefan ^b   Havinga, Paul ^a,b  

^a UNIVERSITY OF TWENTE   (Netherlands)

^b Ambient Systems   (Netherlands)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; INFORMATION USE; MONTE CARLO METHODS; POSITION MEASUREMENT; SENSOR DATA FUSION; SYSTEMATIC ERRORS;

LOCALIZATION SCHEMES; MOBILE SENSOR NETWORKS; MOBILITY; RANGE MEASUREMENTS;

MOBILE TELECOMMUNICATION SYSTEMS;

EID: 33745563444     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11669463_14     Document Type: Conference Paper

References (19)

1. A. Savvides, H. Park and M. Srivastava: The Bits and Flops of the N-Hop Multilateration Primitive for Node Localization Problems. In First ACM International Workshop on Wireless Sensor Networks and Application, Atlanta, GA, September 2002.
2. C. Savarese, J. Rabay and K. Langendoen: Robust Positioning Algorithms for Distributed Ad-Hoc Wireless Sensor Networks. USENIX Technical Annual Conference, Monterey, CA, June 2002.
3. Koen Langendoen and Niels Reijers: Distributed localization in wireless sensor networks: A quantitative comparison. In Computer Networks (Elsevier), special issue on Wireless Sensor Networks, 2003.
4. Y. Shang, W. Ruml, Y. Zhang and M. Fromherz: Localization From Mere Connectivity. MobiHoc'03, Annapolis, Maryland, June 2003.
5. Yi Shang and Wheeler Ruml: Improved MDS-based localization. In Infocom 2004
6. L.Evers,W.Bach, D.Dam,M.Jonker, H.Scholten, and P.Havinga: An iterative quality based localization algorithm for adhoc networks. In Department of Computer Science, University of Twente, 2002.
7. L.Evers, S.Dulman, P.Havinga: A distributed precision based localization algorithm for ad-hoc networks. Proceedings of Pervasive Computing (PERVASIVE 2004).
8. Jan Beutel: Geolocation in a picoradio environment. In MS Thesis, ETH Zurich, Electronics Lab, 1999.
9. J.E.Handschin: Monte Carlo Techniques for Prediction and Filtering of Non-Linear Stochastic Processes. Automatica 6. pp. 555-563. 1970.
10. V.S.Zaritskii, V.S.Svetnik, L.I.Shimelevich: Monte Carlo technique in problems of optimal data processing. Automation and Remote Control 12: 95-103. 1974.
11. F.Dellaert, D.Fox, W.Burgard, S.Thrun: Monte Carlo Localization for Mobile Robots. IEEE International Conference on Robotics and Automation (ICRA). May 1999.
12. S.Thrun, D.Fox, W.Burgard, F.Dellaert: Robust Monte Carlo Localization for Mobile Robots. Artificial Intelligence Journal. 2001.
13. L.Hu, D.Evans: Localization for Mobile Sensor Networks. Tenth Annual International Conference on Mobile Computing and Networking (MobiCom 2004), USA. 2004.
14. A.Kong, J.S.Liu, W.H.Wong: Sequential Imputations and Bayesian Missing Data Problems. Journal of the American Statistical Association. Volume 89, pp. 278-288. 1994.
15. A.Doucet, S.Godsill, C.Andrieu: On Sequential Monte Carlo Sampling Methods for Bayesian Filtering. Statistics and Computing. Volum 10, pp. 197-208. 2000.
16. T.Camp, J.Boleng, V.Davies: A survey of Mobility Models for Ad Hoc Networks Research. Wireless Communications and Mobile Computing. Volume 2, Number 5. 2002.
17. H.Tanizaki, R.S.Mariano: Nonlinear and non-Gaussian statespace modeling with Monte-Carlo simulations. Journal of Econometrics 83: 263-290. 1998.
18. D.Niculescu, B.Nath: Ad hoc positioning systems. In: IEEE Globecom 2001, San Antonio. 2001.
19. S.Dulman, P.Havinga: Statistically enhanced localization schemes for randomly deployed wireless sensor networks. DEST International Workshop on Signal Processing for Sensor Networks, Australia. 2004.