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Revisiting the TTL-based controlled flooding search: optimality and randomization
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Source International Conference on Mobile Computing and Networking archive
Proceedings of the 10th annual international conference on Mobile computing and networking table of contents
Philadelphia, PA, USA
SESSION: Algorithms for multihop networks I table of contents
Pages: 85 - 99  
Year of Publication: 2004
ISBN:1-58113-868-7
Authors
Nicholas Chang  University of Michigan, Ann Arbor, MI
Mingyan Liu  University of Michigan, Ann Arbor, MI
Sponsors
SIGMOBILE: ACM Special Interest Group on Mobility of Systems, Users, Data and Computing
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 5,   Downloads (12 Months): 50,   Citation Count: 13
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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 limit our attention in this study to the class of controlled flooding search strategies where query/search packets are broadcast and propagated in the network until a preset TTL (time-to-live) value carried in the packet expires. Every unsuccessful search attempt results in an increased TTL value (i.e., larger search area) and the same process is repeated. The primary goal of this study is to derive search strategies (i.e., sequences of TTL values) that will minimize the cost of such searches associated with packet transmissions. The main results of this paper are as follows. When the probability distribution of the location of the object is known a priori, we present a dynamic programming formulation with which optimal search strategies can be derived that minimize the expected search cost. We also derive the necessary and sufficient conditions %on the location distribution for two very commonly used search strategies to be optimal. When the probability distribution of the location of the object is not known a priori and the object is to minimize the worst-case search cost, we show that the best strategies are randomized strategies, i.e., successive TTL values are chosen from certain probability distributions rather than deterministic values. We show that given any deterministic TTL sequence, there exists a randomized version that has a lower worst-case expected search cost. We also derive an asymptotically (as the network size increases) optimal strategy within a class of randomized strategies.


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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D. Braginsky and D.Estrin, "Rumor routing algorithm for sensor networks," Proc. International Conference on Distributed Computing Systems (ICDCS-22), 2002.
 
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D. Johnson and D. Maltz, "Dynamic source routing in ad hoc wireless networks," Mobile Computing, pp. 153--181, 1999.
 
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S. Shakkottai, "Asymptotics of query strategies over a sensor network," Proceedings of IEEE Infocom, March 2004, Hong Kong.
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Y. Baryshinikov, E. Coffman, P. Jelenkovic, P. Momcilovic, and D. Rubenstein, "Flood search under the california split rule," Operations Research Letters, vol. 32, no. 3, pp. 199--206, May 2004.
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N. Chang and M. Liu, "Revisiting the TTL-based controlled flooding search: Optimality and randomization," EECS Technical Report CGR 04-06, 2004, University of Michigan, Ann Arbor.
 
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T-J.Kwon Y. Yi, M. Gerla, "Efficient flooding in ad hoc networks using on-demand (passive) cluster formation," Proceedings of Mobihoc, June 2003, Annapolis, USA.

CITED BY  13


REVIEW

"Panamalai R. Parthasarathy : Reviewer"

Chang and Liu have derived search strategies that minimize the expected search cost to locate a node in a large network for a class of time-to-live (TTL)-based controlled flooding search methods.

The authors present a dynamic programming for  more...

Collaborative Colleagues:
Nicholas Chang: colleagues
Mingyan Liu: colleagues