Exploring artificial intelligence in the new millennium
Active learning for adaptive mobile sensing networks
Proceedings of the 5th international conference on Information processing in sensor networks
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
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This paper presents a novel approach, compressive mobile sensing, to use mobile sensors to sample and reconstruct sensing fields based on compressive sensing. Compressive sensing is an emerging research field based on the fact that a small number of linear measurements can recover a sparse signal without losing any useful information. Using compressive sensing, the signal can be recovered by a sampling rate that is much lower than the requirements from the well-known Shannon sampling theory. The proposed compressive mobile sensing approach has not only the merits of compressive sensing, but also the flexibility of different sampling densities for areas of different interests. A special measurement process makes it different from normal compressive sensing. Adopting importance sampling, compressive mobile sensing enables mobile sensors to move adaptively and acquire more samples from more important areas. A motion planning algorithm is designed based on the result of sparsity analysis to locate areas of more interests. At last, experimental results of 2-D mapping are presented as an implementation compressive mobile sensing.