Efficient RSS measurement in wireless networks based on compressive sensing

Collecting the RSS between all pair of nodes in the networks is very significant for wireless network optimization, localization, interference management and etc. Aside from its significances, the measurement process could be tedious, time consuming and involving human operations. The state-of-art works usually applied the fashion of “measure a few, predict many”, which use measurement calibrated models to generate the RSS for the whole networks. However, this kind of methods still cannot provide accurate results in a short duration and low measurement cost. In addition, they also require careful scheduling of the measurement which is vulnerable to measurement conflict. In this paper, we propose a compressive sensing (CS)-based RSS measurement solution, which is conflict-tolerant, time-efficient and accuracy-guaranteed without any model-calibrate operation. The CS-based solution takes advantage of compressive sensing theory to enable simultaneous measurement in the same channel, which reduces the time cost to the level of O(logN) (where N is the network size) and works well for sparse networks. Extensive experiments based on real data trace are conducted to show the efficiency of the proposed solutions.