Progressive Processing of Continuous Range Queries in Hierarchical Wireless Sensor Networks

In this paper, we study the problem of processing continuous range queries in a hierarchical wireless sensor network. 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 and additionally 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. It 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 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.

💡 Research Summary

The paper addresses the challenge of processing a large number of continuous range queries in wireless sensor networks (WSNs) that are organized hierarchically rather than in a flat topology. In a hierarchical WSN, sensor nodes with limited resources occupy the lower tiers, while more capable devices (e.g., cluster heads or gateways) reside in higher tiers closer to the base station. This architectural difference creates a trade‑off between two dominant costs: (1) storage cost, i.e., the memory required at each node to keep query descriptors, and (2) transmission‑energy cost, i.e., the energy spent forwarding data that matches a query toward the sink. The authors formalize this trade‑off as a weighted sum C = ∑ₗ (wₛ·Sₗ + wₑ·Eₗ), where Sₗ and Eₗ denote storage and energy consumption at tier l, and wₛ, wₑ reflect the relative importance of each cost in a given application scenario.

Building on the query‑merging technique introduced by Xiang et al., the authors propose a “progressive processing” framework that adapts the degree of query merging per tier. They introduce a merging ratio αₗ ∈