AFCS: Aggregation-Free Spatial-Temporal Mobile Community Sensing

While spatial-temporal environment monitoring has become an indispensable way to collect data for enabling smart cities and intelligent transportation applications, the cost to deploy, operate and maintain a sensor network with sensors and massive communication infrastructure is too high to bear. Compared to the infrastructure-based sensing approach, community sensing, or namely mobile crowdsensing, that leverage community members' mobile devices to collect data becomes a feasible way to scale up the spatial-temporal coverage of the sensing system. However, a community sensing system would need to aggregate sensors and location data from community members and thus would raise concerns on privacy and data security In this paper, we present a novel community sensing paradigm AFCS -Sensor and Location Data Aggregation-Free Community Sensing, which is designed to obtain the environment information (e.g., spatial-temporal distributions of air pollution, temperature, and bike-shares) in each subarea of the target area, without aggregating sensor and location data collected by community members. AFCS proposes to orchestrate with the Trusted Execution Environments (TEEs) of every community member's mobile device to cover the communication, computation and storage with spatial-temporal data. Further, AFCS proposes a novel Decentralized Spatial-Temporal Compressive Sensing framework based on Parallelized Stochastic Gradient Descent. Through learning the latent structure of the spatial-temporal data via decentralized optimization, AFCS 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 every member's TEE instance. Experiments based on real-world datasets and the Virtual Mobile Infrastructure (VMI) with TEE emulations demonstrate that AFCS exhibits low approximation error (i.e., less than 0:2°C in city-wide temperature sensing, 10 units of PM2.5 index in urban air pollution sensing, and 2 bikes in city-wide bike sharing prediction) and performs comparably to (sometimes better than) state-of-the-art algorithms based on the data aggregation and centralized computation