ACM Home Page
Please provide us with feedback. Feedback
Localized algorithms in wireless ad-hoc networks: location discovery and sensor exposure
Full text PdfPdf (230 KB)
Source International Symposium on Mobile Ad Hoc Networking & Computing archive
Proceedings of the 2nd ACM international symposium on Mobile ad hoc networking & computing table of contents
Long Beach, CA, USA
Session: Sensor networks and energy management table of contents
Pages: 106 - 116  
Year of Publication: 2001
ISBN:1-58113-428-2
Authors
Seapahn Meguerdichian  Computer Science Department, University of California Los Angeles
Sasa Slijepcevic  Computer Science Department, University of California Los Angeles
Vahag Karayan  Electrical Engineering Department, University of California Los Angeles
Miodrag Potkonjak  Computer Science Department, University of California Los Angeles
Sponsor
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 57,   Downloads (12 Months): 696,   Citation Count: 24
Additional Information:

abstract   references   cited by   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: 10.1145/501431.501432

ABSTRACT

The development of practical, localization algorithms is probably the most needed and most challenging task in wireless ad-hoc sensor networks (WASNs). Localized algorithms are a special type of distributed algorithms where only a subset of nodes in the WASN participate in sensing, communication, and computation. We have developed a generic localized algorithm for solving optimization problems in wireless ad-hoc networks that has five components: (i) data acquisition mechanism, (ii) optimization mechanism, (iii) search expansion rules, (iv) bounding conditions and (v) termination rules. the main idea is to request and process data only locally and only from nodes who are likely to contribute to rapid formation of the final solution. The approach enables two types of optimization: The first, guarantees the fraction of nodes that are contacted while optimizing for solution quality. The second, provides guarantees on solution qualities while minimizing the number of nodes that are contacted and/or amount of communication. The localized optimization approach is applied to two fundamental problems in sensor networks: location discovery and exposure-based coverage. We demonstrate its effective-ness on a number of examples


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

Abe00
 
Abi00
A.A. Abidi, G.J. Pottie, W.J. Kaiser, "Power-Conscious Design Of Wireless Circuits And Systems.' Proceedings of the IEEE, vol.88, (no.10), pp. 1528-45, Oct. 2000.
Adj99
Bad93
Aur91
 
Ben93
M. Bender et. al. "Unix For Nomads: Making Unix Support Mobile Computing." In Proceedings of the USENIX Symposium on Mobile & Location Independent Computing, August 1993.
 
Beu99
J. Beutel, "Geolocation In A PicoRadio Environment." M.S. Thesis, ETH Z~rich, Electronics Lab, 1999.
 
Bul00
N. Bulusu, J. Heidemann, D. Estrin, ""GPS-less Low Cost Outdoor Localization For Very Small Devices." IEEE Personal Communications, Special Issue on "Smart Spaces and Environments", Vol. 7, No. 5, pp. 28-34, October 2000.
 
Cor90
 
Dow98
 
Dur89
Est00
 
Has97
Z.J. Haas, "On The Relaying Capability Of The Reconfigurable Wireless Networks." IEEE 47th Vehicular Technology Conference, Vol.2, pp. 1148-52, May 1997.
Int00
Gal83
 
Gir01
L. Girod, D. Estrin, "Robust Range Estimation Using Acoustic And Multimodal Sensing." IEEE/RSI Int. Conf. on Intelligent Robots and Systems (IROS 2001), Maui, Hawaii, October 2001 (To Appear).
 
Hoa85
 
Kan00
C.W. Kang, M.W. Golay, "An Integrated Method For Comprehensive Sensor Network Developement In Complex Power Plant Systems." Reliability Engineering & System Safety, vol.67, pp. 17-27, Jan. 2000.
Kis92
Lam78
 
Lam90
 
Lie98
K. Lieska, E. Laitinen, J. Lahteenmaki, "Radio Coverage Optimization With Genetic Algorithms." IEEE Int. Symp. on Personal, Indoor and Mobile Radio Communications, pp. 318-22, Sept. 1998.
 
Lyn96
 
Meg01a
S. Meguerdichian, F. Koushanfar, M. Potkonjak, M. Srivastava, "Coverage Problems in Wireless Ad-Hoc Sensor Networks." IEEE Infocom 2001, Vol. 3, pp. 1380-1387, April 2001.
Meg01b
 
Mil89
 
MIT82
MIT Lincoln Laboratories. Workshop on Distributed Sensor Networks. 1982.
 
Glo2
Glomosim 2.0. http://pcl.cs.ucla.edu/projects/glomosim/
 
ORo92
J. O'Rourke, Computational geometry column 15. (Open problem from art gallery solved). Int. Journal of Computational Geometry & Applications, vol.2, pp.215-17, June 1992.
Pot00
 
Ray88
 
Rum86
 
Sav01a
C. Savarese, J. Rabaey, J. Beutel, "Locationing in Distributed Ad-Hoc Wireless Sensor Networks." Proceedings of the ICASSP, May 2001.
Sav01b
 
Tel94
Ten00

CITED BY  24

Collaborative Colleagues:
Seapahn Meguerdichian: colleagues
Sasa Slijepcevic: colleagues
Vahag Karayan: colleagues
Miodrag Potkonjak: colleagues