Mobility-Aware Fog-Aided Mobile Crowdsensing: From Spatiotemporal and Continuous-Time Perspective

Abstract

Mobile crowdsensing (MCS) leverages distributed devices to collect and share real-time data for location-driven applications, yet its performance is constrained by the absence of continuous-time theoretical frameworks addressing worker mobility and energy efficiency. To our knowledge, there is no continuous-time framework yet that addresses both worker mobility patterns and power control strategies for MCS performance. This paper proposes a novel analytical framework for fog-assisted MCS in location-driven scenarios, adopting a continuous-time and spatiotemporal perspective. Two mobility models are employed: two-dimensional Brownian motion and constrained Brownian motion, to characterize the physical locations of MCS workers under realistic pathway constraints. Assuming time-uncorrelated Rayleigh fading, we evaluate key performance indicators, including departure time, instantaneous capacity, fog-aware temporal average transmission success probability (TATSP), cumulative sensing data and region of interest (ROI)-capacity. Furthermore, we assess the power consumption and energy efficiency of the path-loss inversion power control strategy in fog-aided MCS system. Our findings offer a comprehensive perspective on the spatiotemporal evolution of worker performance, establishing foundational principles for task allocation in MCS with unpredictable mobility patterns. Theoretical results suggest that long-term sensing tasks may be preferentially assigned to low-mobility workers to optimize fog-aware TATSP. Moreover, the energy efficiency decreases monotonically with respect to the initial link distance. While numerical results, validated by Monte Carlo simulations and real-world GPS trajectory data from the Geolife dataset, illustrate how mobility, initial link distance, and path-loss coefficient influence instantaneous capacity, ROI-capacity, cumulative sensing data and energy efficiency. It is also revealed that, assigning the tasks to the nearest low-mobility workers presents a simple yet effective scheme.