Enabling ε-approximate querying in sensor networks

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