Coping with irregular spatio-temporal sampling in sensor networks

摘要: Wireless sensor networks have attracted attention from a diverse set of researchers, due to the unique combination distributed, resource and data processing constraints. However, until now, lack real network deployments resulted in ad-hoc assumptions on wide range issues including topology characteristics distribution. As become more widespread [1, 2], many these need be revisited.This paper deals with fundamental issue spatio-temporal irregularity We make case for existence such irregular sampling, show that it impacts performance networks. For instance, aggregation schemes provide inaccurate results, compression efficiency is dramatically reduced, storage skews load among nodes incurs significantly greater routing overhead. To mitigate impact irregularity, we outline spectrum solutions. compression, propose use spatial interpolation (first suggested by Ganeriwal et al [3] temporal signal segmentation followed alignment. reduce cost data-centric routing, virtualization, boundary detection.