Edge computing recently is increasingly popular due to the growth of data size and the need of sensing with the reduced center. Based on Edge computing architecture, we propose a novel crowdsensing framework called Edge-Mediated Spatial-Temporal Crowdsensing. This algorithm targets on receiving the environment information such as air pollution, temperature, and traffic flow in some parts of the goal area, and does not aggregate sensor data with its location information. Specifically, EdgeSense works on top of a secured peer-To-peer network consisted of participants and propose a novel Decentralized Spatial-Temporal Crowdsensing framework based on Parallelized Stochastic Gradient Descent. To approximate the sensing data in each part of the target area in each sensing cycle, EdgeSense uses the local sensor data in participants' mobile devices to learn the low-rank characteristic and then recovers the sensing data from it. We evaluate the EdgeSense on the real-world data sets (temperature  and PM2.5  data sets), where our algorithm can achieve low error in approximation and also can compete with the baseline algorithm which is designed using centralized and aggregated mechanism.
S. Yang et al., "EdgeSense: Edge-Mediated Spatial-Temporal Crowdsensing," IEEE Access, vol. 7, pp. 95122-95131, Institute of Electrical and Electronics Engineers (IEEE), Sep 2019.
The definitive version is available at https://doi.org/10.1109/ACCESS.2018.2870298
Keywords and Phrases
Distributed Sensing; Crowdsourcing; Edge Computing
International Standard Serial Number (ISSN)
Article - Journal
© 2019 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Sep 2019