Region Sampling: Continuous Adaptive Sampling on Sensor Networks

摘要: Satisfying energy constraints while meeting performance requirements is a primary concern when sensor network being deployed. Many recent proposed techniques offer error bounding solutions for aggregate approximation but cannot guarantee spending. Inversely, our goal to bound the consumption minimizing error. In this paper, we propose an online algorithm, region sampling, computing approximate aggregates satisfying pre-defined budget. Our algorithm distinguished by segmenting into partitions of non-overlapping regions and performing sampling local aggregation each region. The cost rate statistics are collected analyzed predict optimal plan. Comprehensive experiments on real-world data sets indicate that approach at minimum 10% more accurate compared with previously solutions.