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Adaptive task recommendation in Mobile crowd sensing (MCS) is a challenging problem, mainly because perceptual tasks are spatio-temporal in nature and worker preferences are dynamically changing. Although there have been some approaches to address the dynamics of task recommendation, these approaches suffer from several problems. First, they only learn the worker's past preferences and cannot cope with the situation where the worker's preferences may change in the next moment, and they only consider the current optimal recommendation instead of global optimization. Second, existing methods do not scale efficiently to the arrival of new workers or tasks, requiring the entire model to be retrained. To address these issues, we propose an adaptive task recommendation method (ATRec) based on reinforcement learning. Specifically, we formalize the adaptive task recommendation problem for each target worker as an interactive Markov decision process (MDP). Then, we use an improved matrix decomposition technique to construct worker-personalized latent factor states based on information such as task content and spatio-temporal context, enabling us to use a unified MDP to learn optimal strategies for different workers. After that, we design an adaptive update algorithm (AUA) based on Deep Q Network (DQN) to more accurately learn the dynamic changes of workers' preferences to adaptively update the task recommendation list of workers. In addition, we propose a personalized dimension reduction method (PDR) to reduce the size of the task set. Through comprehensive experimental results and analysis, we demonstrate the effectiveness of the ATRec approach. Compared with existing methods, ATRec can better solve the problem of adaptive task recommendation, and can more accurately predict workers' preferences and make recommendations. [ABSTRACT FROM AUTHOR]

Opportunistic sensing has become an appealing mobile crowd sensing (MCS) paradigm due to the fact that it can reduce the energy consumption and cost of cellular network connections. However, its success rate and transmission speed depend on the social interaction and mobility patterns of nodes. In this paper, we provide a spatiotemporal opportunistic transmission method for MCS networks. Firstly, to characterize the mobility patterns and social attributes of nodes more precisely and combine their advantages, this method defines spatiotemporal encountering and visiting parameters related to specific space-time units for nodes in a MCS network. Further, to realize reliable opportunistic transmission across regions and time intervals, this method searches publishers or participants of sensing tasks in a space-time unit according to the spatiotemporal encountering parameters of nodes in the unit and tracks the publishers or participants across the space-time units according to the spatiotemporal visiting parameters of nodes. The simulation results verify that the proposed method can achieve higher success rate with less transmission delay than existing typical methods. [ABSTRACT FROM AUTHOR]