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Controlled flooding search in a large network
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Source IEEE/ACM Transactions on Networking (TON) archive
Volume 15 ,  Issue 2  (April 2007) table of contents
Pages: 436 - 449  
Year of Publication: 2007
ISSN:1063-6692
Authors
Nicholas B. Chang  Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
Mingyan Liu  Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI
Publisher
IEEE Press  Piscataway, NJ, USA
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Downloads (6 Weeks): 8,   Downloads (12 Months): 54,   Citation Count: 4
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DOI Bookmark: 10.1109/TNET.2007.892880

ABSTRACT

In this paper, we consider the problem of searching for a node or an object (i.e., piece of data, file, etc.) in a large network. Applications of this problem include searching for a destination node in a mobile ad hoc network, querying for a piece of desired data in a wireless sensor network, and searching for a shared file in an unstructured peer-to-peer network. We consider the class of controlled flooding search strategies where query/search packets are broadcast and propagated in the network until a preset time-to-live (TTL) value carried in the packet expires. Every unsuccessful search attempt, signified by a timeout at the origin of the search, results in an increased TTL value (i.e., larger search area) and the same process is repeated until the object is found. The primary goal of this study is to find search strategies (i.e., sequences of TTL values) that will minimize the cost of such searches associated with packet transmissions. Assuming that the probability distribution the object location is not known a priori, we derive search strategies that minimize the search cost in the worst-case, via a performance measure in the form of the competitive ratio between the average search cost of a strategy and that of an omniscient observer. This ratio is shown in prior work to be asymptotically (as the network size grows to infinity) lower bounded by 4 among all deterministic search strategies. In this paper, we show that by using randomized strategies (i.e., successive TTL values are chosen from certain probability distributions rather than deterministic values), this ratio is asymptotically lower bounded by e. We derive an optimal strategy that achieves this lower bound, and discuss its performance under other criteria. We further introduce a class of randomized strategies that are sub-optimal but potentially more useful in practice.


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.

 
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[1] D. Braginsky and D. Estrin, "Rumor routing algorithm for sensor networks," in Proc. Int. Conf. Distributed Computing Systems (ICDCS- 22), Vienna, Austria, 2002, pp. 563-570.
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[6] Y. Baryshinikov, E. Coffman, P. Jelenkovic, P. Momcilovic, and D. Rubenstein, "Flood search under the california split rule," Oper. Res. Lett., vol. 32, no. 3, pp. 199-206, May 2004.
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[9] S. Nash and A. Sofer, Linear and Nonlinear Programming. New York: McGraw-Hill, 1996.


Collaborative Colleagues:
Nicholas B. Chang: colleagues
Mingyan Liu: colleagues