In this paper, an energy efficient adaptive back forwarding method is proposed for spatial range query processing in ubiquitous sensor networks. In order to execute spatial range query efficiently in sensor networks, many rectangle based index methods are researched, such as SPIX method. This kind of method is efficient to transmit user queries to the queried range, but it defines the sensor nodes' results return path as the opposite path of its query transmit path. As a result, the energy used in query results return path is as same as the queries transmit process. In this paper, query result back forwarding method is proposed by rearranging the query results transmit route to aggregate the result data in queried range adaptively. By using the proposed method the energy cost in the query result transmits process is reduced and the spatial range queries are processed energy efficiently. In the performance evaluation, the proposed method decreased the energy cost efficiently while processing the different kinds of spatial range queries.
Abstract. According to increase of spatial data, many decision support systems require the fast spatio-temporal analysis. This paper proposes the improved index for efficient OLAP in a spatial data warehouse. The main idea is to use the hybrid index of the extended aggregation R-tree and the sorted hash table. The extended R-tree supports the spatial hierarchy with the level of R-tree. Also, it provides pre-aggregation for fast retrieval of the aggregated value. The sorted hash table is the transformed hash table for supporting the temporal hierarchy. So, it provides pre-aggregation of each temporal unit, year, month and etc. By the proposed hybrid index, an efficient spatio-temporal analysis can be supported since it provides the spatio-temporal hierarchy and the pre-aggregated value.
Abstract.Since the context plays a significant role in ubiquitous computing environment, many researches have studied about context-awareness system to improve the performance. An efficient learning mechanism is in importance of context-aware system, but there are seldom algorithms focused on convenience of systems by elaborating the learning mechanism with user's context information. As one of the most adaptable algorithm, Back Propagation provides us favorable inference capability. In this paper, we concentrate on improving the predict ability and reducing the system workload by proposing improved selfadaptive back propagation algorithm. The middleware we proposed improves the predicate capability. Thus, the overall performance becomes better than other systems. By adding system cache to middleware, it is possible for the context-aware system to act faster and improve the workload efficiency. Experiments show that there is an obvious improvement in overall performance of the context-awareness systems.
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