Adaptive processing of historical spatial range queries in peer-to-peer sensor networks
Distributed and Parallel Databases
Processing continuous join queries in sensor networks: a filtering approach
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
MG-join: detecting phenomena and their correlation in high dimensional data streams
Distributed and Parallel Databases
Dynamic join optimization in multi-hop wireless sensor networks
Proceedings of the VLDB Endowment
Power efficiency through tuple ranking in wireless sensor network monitoring
Distributed and Parallel Databases
On clustering large number of data streams
Intelligent Data Analysis
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Given their autonomy, flexibility and large range of functionality, wireless sensor networks can be used as an effective and discrete means for monitoring data in many domains. Typical sensor nodes are very constrained, in particular regarding their energy and memory resources. Thus, any query processing solution over these devices should consider their limitations. We investigate the problem of processing join queries within a sensor network. Due to the limited memory at nodes, joins are typically processed in a distributed manner over a set of nodes. Previous approaches have either assumed that the join processing nodes have sufficient memory to buffer the subset of the join relations assigned to them, or that the amount of available memory at nodes is known in advance. These assumptions are not realistic for most scenarios. In this context we propose and investigate DIJ, a distributed algorithm for join processing that considers the memory limitations at nodes and does not make a priori assumptions on the available memory at the processing nodes. At the same time, our algorithm still aims at minimizing the energy cost of query processing.