CSWA: Aggregation-Free Spatial-Temporal Community Sensing
In this paper, we present a novel community sensing paradigm CSWA-Community Sensing Without Sensor/Location Data Aggregation. CSWA is designed to obtain the environment information (e.g., air pollution or temperature) in each subarea of the target area, without aggregating sensor and location data collected by community members. CSWA operates on top of a secured peer-to-peer network over the community members and proposes a novel Decentralized Spatial-Temporal Compressive Sensing framework based on Parallelized Stochastic Gradient Descent. Through learning the low-rank structure via distributed optimization, CSWA approximates the value of the sensor data in each subarea (both covered and uncovered) for each sensing cycle using the sensor data locally stored in each member's mobile device. Simulation experiments based on real-world datasets demonstrate that CSWA exhibits low approximation error (i.e., less than 0.2°C in city-wide temperature sensing task and 10 units of PM2.5 index in urban air pollution sensing) and performs comparably to (sometimes better than) state-of-the-art algorithms based on the data aggregation and centralized computation.
J. Bian et al., "CSWA: Aggregation-Free Spatial-Temporal Community Sensing," Proceedings of the 32nd AAAI Conference on Artificial Intelligence (2018, New Orleans, LA), pp. 2087-2094, AAAI press, Feb 2018.
32nd AAAI Conference on Artificial Intelligence, AAAI 2018 (2018: Feb. 2-7, New Orleans, LA)
Intelligent Systems Center
Keywords and Phrases
Air pollution; Approximation algorithms; Artificial intelligence; Distributed computer systems; Peer to peer networks; Stochastic systems; Structural optimization; Centralized computation; Compressive sensing; Distributed optimization; Environment information; State-of-the-art algorithms; Stochastic gradient descent; Temperature sensing; Urban air pollution; Temperature sensors
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Article - Conference proceedings
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