Sensify: A Learning-Based Budget-Aware Task Assignment in Mobile Crowdsensing

Accurate and comprehensive data acquisition is critical for modern data-driven environmental applications. Mobile Crowdsensing (MCS) offers an effective approach by leveraging user participation to collect environmental data through task assignment. To minimize costs, MCS platforms often partition the environment into subareas and utilize inference algorithms to extrapolate data for entire subareas based on partial sensing in a limited subset. However, determining the optimal set of users for sensing tasks remains challenging due to constraints such as user availability and the complexity of data inference models. This paper introduces Sensify, a task assignment strategy that optimizes data acquisition by accounting for data correlations and budget constraints. Sensify efficiently selects subareas and recruits cost-effective users for sensing tasks, incorporating user-specific contexts such as location and device power availability during task assignment. To adaptively manage the platform budget, the strategy considers a dynamic set of users with varying costs over time. A deep recurrent reinforcement learning-based network is employed to select optimal subareas for sensing, while user recruitment is dynamically optimized using a reinforcement learning approach. Specifically, a modified Contextual Combinatorial Multi-Armed Bandit (CC-MAB) framework is utilized to handle the volatility and variability in user costs. Experiments conducted on two real-world datasets demonstrate that Sensify can improve data acquisition by up to 7% compared to existing approaches.