Abstract-Join processing in wireless sensor networks is difficult: As the tuples can be arbitrarily distributed within the network, matching pairs of tuples is communication intensive and costly in terms of energy. Current solutions only work well with specific placements of the nodes and/or make restrictive assumptions. In this paper, we present SENS-Join, an efficient general-purpose join method for sensor networks. To obtain efficiency, SENS-Join does not ship tuples that do not join, based on a filtering step. Our main contribution is the design of this filtering step which is highly efficient in order not to exhaust the potential savings. We demonstrate the performance of SENS-Join experimentally: The overall energy consumption can be reduced by more than 80%, as compared to the state-of-the-art approach. The per node energy consumption of the most loaded nodes can be reduced by more than an order of magnitude.
Automated matching of semantic service descriptions is the key to automatic service discovery and binding. But when trying to find a match for a certain request it may often happen, that the request cannot be serviced by a single offer but could be handled by combining existing offers. In this case automatic service composition is needed. Although automatic composition is an active field of research it is mainly viewed as a planning problem and treated separatedly from service discovery. In this paper we argue that an integrated approach to the problem is better than seperating these issues as is usually done. We propose an approach that integrates service composition into service discovery and matchmaking to match service requests that ask for multiple connected effects, discuss general issues involved in describing and matching such services and present an efficient algorithm implementing our ideas.
Answering queries with a low selectivity in wireless sensor networks is a challenging problem. A simple tree-based data collection is communication-intensive and costly in terms of energy. Prior work has addressed the problem by approximating query results based on models of sensor readings. This cuts communication effort if the accuracy requirements are loose, e.g., if the temperature is required within ±0.5• C. For more accuracy, the models need frequent updates, and the communication costs quickly increase. In addition, sophisticated models incur substantial training costs. We propose a query-processing scheme that efficiently consolidates sensor data based on wavelet synopses. The difficulty is that the synopsis has to be constructed incrementally during data collection to ensure efficiency. Our core contribution is to show how to distribute the construction of wavelet synopses in sensor networks. In addition, our approach provides strict error guarantees. We evaluate our distributed wavelet compaction on real-world and on synthetic sensor data. Our solution reduces communication costs by more than a factor of five compared to state-of-the-art approaches. Further, our error guarantees for which efficient data consolidation is possible are better than theirs by more than an order of magnitude.
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