Journal
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Volume 25, Issue 11, Pages 2818-2828Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2013.269
Keywords
Wireless sensor networks; data loss and reconstruction; compressive sensing
Funding
- NSFC [61303202, 61073158, 61100210]
- STCSM Project [12dz1507400]
- Doctoral Program Foundation [20110073120021]
- FQRNT grant [131844]
- Singapore-MIT IDC [IDD61000102a]
- [SUTD-ZJU/RES/03/2011]
- [NRF2012EWT-EIRP002-045]
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Reconstructing the environment by sensory data is a fundamental operation for understanding the physical world in depth. A lot of basic scientific work (e. g., nature discovery, organic evolution) heavily relies on the accuracy of environment reconstruction. However, data loss in wireless sensor networks is common and has its special patterns due to noise, collision, unreliable link, and unexpected damage, which greatly reduces the reconstruction accuracy. Existing interpolation methods do not consider these patterns and thus fail to provide a satisfactory accuracy when the missing data rate becomes large. To address this problem, this paper proposes a novel approach based on compressive sensing to reconstruct the massive missing data. Firstly, we analyze the real sensory data from Intel Indoor, GreenOrbs, and Ocean Sense projects. They all exhibit the features of low-rank structure, spatial similarity, temporal stability and multi-attribute correlation. Motivated by these observations, we then develop an environmental space time improved compressive sensing (ESTI-CS) algorithm with a multi-attribute assistant (MAA) component for data reconstruction. Finally, extensive simulation results on real sensory datasets show that the proposed approach significantly outperforms existing solutions in terms of reconstruction accuracy.
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