Abstract
In Chap. 4, we focused on the missing data problem in mobile crowdsensing, and saw how the basic compressive sensing improves the reconstruction accuracy. In this chapter, we will discuss another issue in mobile crowdsensing—faulty data. Generally speaking, faulty data can be easily detected via traditional approaches like time series. However, due to the openness of mobile crowdsensing applications, both faulty data and missing values prevail in it.
This chapter is represented with permission from ©[2018] IEEE ref. Wang, B., Kong, L., He, L., Wu, F., Yu, J. and Chen, G., 2018, July. I (TS, CS): Detecting faulty location data in mobile crowdsensing. In 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS) (pp. 808–817).
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Kong, L., Wang, B., Chen, G. (2019). Iterative Compressive Sensing for Fault Detection. In: When Compressive Sensing Meets Mobile Crowdsensing. Springer, Singapore. https://doi.org/10.1007/978-981-13-7776-1_5
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DOI: https://doi.org/10.1007/978-981-13-7776-1_5
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