Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...
Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...
Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...
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200 S. PatilIDEA uses the concept of iterative deepen<strong>in</strong>g <strong>by</strong> ‘iteratively’ send<strong>in</strong>g thequery messages to the <strong>in</strong>creas<strong>in</strong>g number of nodes until the query is answered (ornot matched). This floods the query throughout the network only if the searchdoes not yield a result at the certa<strong>in</strong> ‘check-po<strong>in</strong>ts’ i.e. end of each iteration.This technique is also applied to a self-organiz<strong>in</strong>g, token-based search, T-IDEAwhich adaptively takes <strong>in</strong>to account the constra<strong>in</strong>ts of each node. Each sensornode identifies itself as ‘available’ or ‘unavailable’ for search operations, basedon local decisions made towards overall network performance improvement.The rema<strong>in</strong>der of the paper is organized as follows. First, a brief summaryof the problem is presented. Section 2 presents a brief description of the problemwith the assumptions for that algorithm. Section 3 describes the iterativedeepen<strong>in</strong>g algorithm <strong>in</strong> details, followed <strong>by</strong> an active token based version of theiterative deepen<strong>in</strong>g algorithm. Section 5 presents the experiments and the resultsof this protocol. F<strong>in</strong>ally, Section 6 presents some related work and the follow<strong>in</strong>gsection concludes the paper.2 Problem OverviewInformation dissem<strong>in</strong>ation and gather<strong>in</strong>g is one of the most important tasks froma mobile ad hoc network. Constra<strong>in</strong>ts like limited energy, dynamic network, lowbandwidth etc. make it more challeng<strong>in</strong>g. Dynamic search<strong>in</strong>g techniques arecrucial for energy-efficient operations of mobile ad hoc networks.This paper <strong>in</strong>troduces a novel approach called IDEA to search <strong>in</strong> ad-hocnetworks, us<strong>in</strong>g iterative-deepen<strong>in</strong>g, a known search technique to search overstate space <strong>in</strong> artificial <strong>in</strong>telligence applications [14]. Iterative deepen<strong>in</strong>g searchesare a comb<strong>in</strong>ation of breadth-first searches with series of depth-first searcheswith <strong>in</strong>creas<strong>in</strong>g bounds of depth. This is further extended to use <strong>in</strong> conjunctionwith local <strong>in</strong>formation based on the <strong>in</strong>teraction with neighbor<strong>in</strong>g nodes, calledas token-based algorithm, T-IDEA. Nodes make local decisions, based on theirenergy constra<strong>in</strong>ts and importance <strong>in</strong> the search operation, to make themselvesavailable for search<strong>in</strong>g.3 Iterative-Deepen<strong>in</strong>gIn applications where relevance of the search result is an important measure,iterative deepen<strong>in</strong>g is a better search technique than many classical algorithms.In the iterative deepen<strong>in</strong>g technique, multiple breadth-first searches are <strong>in</strong>itiatedwith <strong>in</strong>creas<strong>in</strong>g depth limits, until the appropriate result has been found, or<strong>in</strong> case of rout<strong>in</strong>g dest<strong>in</strong>ation has been reached, or as <strong>in</strong> the worst-case, themaximum depth limit D has been reached.There are various motivations to support this search technique <strong>in</strong> sensor networks.First, the cost of query<strong>in</strong>g at smaller depths is less than query-process<strong>in</strong>gcost at larger depths. This is because of the fast growth of the number of nodesto be searched at grow<strong>in</strong>g depths. Secondly, this would also lead to reduced numberof resources utilized for query process<strong>in</strong>g <strong>in</strong> a constra<strong>in</strong>ed ad-hoc network.Compared to the traditional graph search<strong>in</strong>g problems like BFS of depth d, this