Enabling ε-approximate querying in sensor networks

Abstract

Data approximation is a popular means to support energy-efficient query processing in sensor networks. Conventional data approximation methods require users to specify fixed error bounds a prior to address the trade-off between result accuracy and energy efficiency of queries. We argue that this can be infeasible and inefficient when, as in many real-world scenarios, users are unable to determine in advance what error bounds can lead to affordable cost in query processing. We envision ε- approximate querying (EAQ) to bridge the gap. EAQ is a uniform data access scheme underlying various queries in sensor networks. It allows users or query executors to incrementally 'refine' previously obtained approximate data to reach arbitrary accuracy. EAQ not only grants more flexibility to in-network query processing, but also minimizes energy consumption through communicating data upto a just-sufficient level. To enable the EAQ scheme, we propose a novel data shuffling algorithm. The algorithm converts sensed datasets into special representations called multi-version array (MVA) . From prefixes of MVA , we can recover approximate versions of the entire dataset, where all individual data items have guaranteed error bounds. The EAQ scheme supports efficient and flexible processing of various queries including spatial window query, value range query, and queries with QoS constraints. The effectiveness and efficiency of the EAQ scheme are evaluated in a real sensor network testbed.