An Adaptive Algorithm for Distributed Processing in Multidimensional Data Sets
The distributed processing of probabilistic topk queries in cluster based wireless sensor networks is done by sufficient set and necessary set. System issues such as indexing techniques and query processing have been examined. Due to the centralized system setting, there is a failure occur in individual tuple means it will affects the neighboring tuples. The transmission cost is high in centralized system settings and more rounds of communication takes place. To overcome that, three algorithms are proposed for intercluster query processing with bounded rounds of communication. The proposed algorithms reduce data transmissions significantly. The algorithms namely Sufficient Set Based (SSB), Necessary Set Based (NSB), and Boundary Based (BB) are used. Therefore developing an Adaptive algorithm that switches among three algorithms to minimize the transmission cost. The concepts include data pruning and data aggregation are introduced. These two concepts have properties that can facilitate localized data pruning in clusters. These algorithms reduce data transmissions and incur only small constant rounds of communication. Hence to analyze the cost during data transmission a cost based adaptive algorithm is used. The least transmission cost is achieved by using the cost based adaptive algorithm.
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