Page 2 Lecture Notes in Computer Science 2865 Edited by G. Goos ...

200 S. PatilIDEA uses the concept of iterative deepening by ‘iteratively’ sending thequery messages to the increasing 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 certain ‘check-points’ i.e. end of each iteration.This technique is also applied to a self-organizing, token-based search, T-IDEAwhich adaptively takes into account the constraints of each node. Each sensornode identifies itself as ‘available’ or ‘unavailable’ for search operations, basedon local decisions made towards overall network performance improvement.The remainder 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 iterativedeepening algorithm in details, followed by an active token based version of theiterative deepening algorithm. Section 5 presents the experiments and the resultsof this protocol. Finally, Section 6 presents some related work and the followingsection concludes the paper.2 Problem OverviewInformation dissemination and gathering is one of the most important tasks froma mobile ad hoc network. Constraints like limited energy, dynamic network, lowbandwidth etc. make it more challenging. Dynamic searching techniques arecrucial for energy-efficient operations of mobile ad hoc networks.This paper introduces a novel approach called IDEA to search in ad-hocnetworks, using iterative-deepening, a known search technique to search overstate space in artificial intelligence applications [14]. Iterative deepening searchesare a combination of breadth-first searches with series of depth-first searcheswith increasing bounds of depth. This is further extended to use in conjunctionwith local information based on the interaction with neighboring nodes, calledas token-based algorithm, T-IDEA. Nodes make local decisions, based on theirenergy constraints and importance in the search operation, to make themselvesavailable for searching.3 Iterative-DeepeningIn applications where relevance of the search result is an important measure,iterative deepening is a better search technique than many classical algorithms.In the iterative deepening technique, multiple breadth-first searches are initiatedwith increasing depth limits, until the appropriate result has been found, orin case of routing destination has been reached, or as in the worst-case, themaximum depth limit D has been reached.There are various motivations to support this search technique in sensor networks.First, the cost of querying at smaller depths is less than query-processingcost at larger depths. This is because of the fast growth of the number of nodesto be searched at growing depths. Secondly, this would also lead to reduced numberof resources utilized for query processing in a constrained ad-hoc network.Compared to the traditional graph searching problems like BFS of depth d, this