Your Search Results

Use this resource - and many more! - in your textbook!

AcademicPub holds over eight million pieces of educational content for you to mix-and-match your way.

Experience the freedom of customizing your course pack with AcademicPub!
Not an educator but still interested in using this content? No problem! Visit our provider's page to contact the publisher and get permission directly.

Controlled Flooding Search in a Large Network

By: Mingyan Liu; Chang, N.B.;

2007 / IEEE


This item was taken from the IEEE Periodical ' Controlled Flooding Search in a Large Network ' 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 of 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