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In this paper, we study the problem of processing continuous range queries ina hierarchical wireless sensor network. Contrasted with the traditionalapproach of building networks in a "flat" structure using sensor devices of thesame capability, the hierarchical approach deploys devices of higher capabilityin a higher tier, i.e., a tier closer to the server. While query processing inflat sensor networks has been widely studied, the study on query processing inhierarchical sensor networks has been inadequate. In wireless sensor networks,the main costs that should be considered are the energy for sending data andthe storage for storing queries. There is a trade-off between these two costs.Based on this, we first propose a progressive processing method thateffectively processes a large number of continuous range queries inhierarchical sensor networks. The proposed method uses the query mergingtechnique proposed by Xiang et al. as the basis and additionally considers thetrade-off between the two costs. More specifically, it works toward reducingthe storage cost at lower-tier nodes by merging more queries, and towardreducing the energy cost at higher-tier nodes by merging fewer queries (therebyreducing "false alarms"). We then present how to build a hierarchical sensornetwork that is optimal with respect to the weighted sum of the two costs. Itallows for a cost-based systematic control of the trade-off based on therelative importance between the storage and energy in a given networkenvironment and application. Experimental results show that the proposed methodachieves a near-optimal control between the storage and energy and reduces thecost by 0.989~84.995 times compared with the cost achieved using the flat(i.e., non-hierarchical) setup as in the work by Xiang et al.

Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks