Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

Abstract

SUMMARYIn this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. Recently, as the size of sensor networks increases due to the growth of ubiquitous computing environments and wireless networks, building wireless sensor networks in a hierarchical configuration is put forth as a practical approach. Contrasted with the traditional approach of building networks in a "flat" structure using sensor devices of the same capability, the hierarchical approach deploys devices of higher-capability in a higher tier, i.e., a tier closer to the server. While query processing in flat sensor networks has been widely studied, the study on query processing in hierarchical sensor networks has been inadequate. In wireless sensor networks, the main costs that should be considered are the energy for sending data and the storage for storing queries. There is a trade-off between these two costs. Based on this, we first propose a progressive processing method that effectively processes a large number of continuous range queries in hierarchical sensor networks. The proposed method uses the query merging technique proposed by Xiang et al. as the basis. In addition, the method considers the trade-off between the two costs. More specifically, it works toward reducing the storage cost at lower-tier nodes by merging more queries and toward reducing the energy cost at higher-tier nodes by merging fewer queries (thereby reducing "false alarms"). We then present how to build a hierarchical sensor network that is optimal with respect to the weighted sum of the two costs. This allows for a cost-based systematic control of the trade-off based on the relative importance between the storage and energy in a given network environment and application. Experimental results show that the proposed method achieves a near-optimal control between the storage and energy and reduces the cost by 1.002 − 3.210 times compared with the cost achieved using the flat (i.e., non-hierarchical) setup as in the work by Xiang et al. key words: hierarchical sensor network, progressive processing, continuous range queries

Optimization of generic progressive queries based on dependency analysis and materialized views

“…Other work related to the progressive queries includes the query processing for continuous queries (Agarwal et al 2006;Babu 2005;Lee et al 2010;Lim et al 2006;Mokbel 2006), the adaptive query optimization (Antoshenkov 1993;Babu and Bizarro 2005;Kabra and DeWitt 1998;Liu and Pu 1997;Lu et al 1995;Markl et al 2004), and the Extraction-Transformation-Loading (ETL) processing (Jorg and DeBloch 2008;Simitsis et al 2005;Vassiliadis et al 2001;Vassiliadis et al 2005). Continuous queries require a query to repeatedly execute over a continuous stream of data; the main idea of the adaptive query optimization is to adapt the environments of a query for optimizing the query processing during the query execution time.…”