CSWA: Aggregation-Free Spatial-Temporal Community Sensing
This addresses privacy and efficiency issues in community sensing for applications like urban monitoring, though it appears incremental as it builds on compressive sensing and distributed optimization.
The paper tackles the problem of obtaining environment information like temperature and air pollution in subareas without aggregating sensor data, achieving low approximation errors such as less than 0.2°C in temperature sensing and 10 units of PM2.5 index in air pollution sensing, performing comparably to or better than state-of-the-art centralized methods.
In this paper, we present a novel community sensing paradigm -- {C}ommunity {S}ensing {W}ithout {A}ggregation}. 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 \emph{Decentralized Spatial-Temporal Compressive Sensing} framework based on \emph{Parallelized Stochastic Gradient Descent}. Through learning the \emph{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 $0.2 ^\circ$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.