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

208 S. PatilTable 1. Probability of correct search results using IDEA for 4 and 8 neighbor nodesNo. of Search Results Pr(4-neighbors per node) Pr(8-neighbors per node)50 0.698 0.756100 0.597 0.673150 0.534 0.572200 0.458 0.5026.2 Query Results CorrectnessPerformance of the iterative deepening technique and its rules, also depends onthe value chosen for number of results expected, R. In addition, performancedepends on the number of nodes that process each query, which in turn is determinedlocally by the number of neighbors (degree) that a node maintains. Tosee the effect of these two factors on the IDEA algorithm, the first simulationassumed 8 neighbors per node (approximately), then for 4 neighbors per node.Over each data set, we then ran analysis for four different values of search results,first with R = 50, then R = 100, R = 150 and R =200Table 1 shows the performance of each variation in terms of query satisfactionprobability, Pr(). It represents all iterative deepening policies as it is independentof the inter-iteration interval, T. As expected, when the definition of querysatisfaction, Q def , increases, the level of satisfaction decreases. However, it isencouraging to note that satisfaction does not drop very quickly as number ofsearch results increase.Also as expected, when the number of neighbors per node decreases, theprobability that a query is satisfied also decreases, since fewer neighbors generallytranslates to fewer number of results. However, we find it interesting thatsatisfaction probability with 4 neighbors per node is not much lower than satisfactionprobability with 8 neighbors per node. The reason being that with 8neighbors per node, the source node usually receives more results than neededto satisfy the query. When the number of neighbors per node decreased to 4,the source received significantly fewer results, but in most cases it was able toproduce the query results.7 Related WorkVarious research groups have worked on optimization of searching algorithmsin ad-hoc sensor networks. Earlier research was focused on optimal flooding orbroadcasting in ad hoc networks. [11] proposes a heuristics based algorithm,which decides to forward the packet based on various factors like probabilisticneighbor node selection. On the other hand, this paper gives a deterministicselection policy based on the tokens or the policy for iterative deepening. [8]uses the concept of elimination scheme based on the information of nodes towhich the packet was broadcast from the transmitter. Node(s) do not broadcastto nodes which have already received the packet. This ‘broadcast cover state’ is