Task scheduling for mobile edge computing enabled crowd sensing applications

Crowd sensing effectively solves the dilemma of massive data collection faced by most data-driven applications. Recently, mobile edge computing (MEC) is proposed to extend the frontier of cloud to the network edge so that it is quite suitable to integrate MEC with current crowd sensing systems. In this paper, we focus on the basic problem of task scheduling in such systems. The problem discussed here has some unique challenges, e.g., edge devices are not dedicated to perform sensing tasks, task scheduling on edge devices and edge servers are highly coupled, and it is hard to achieve long-term objectives. To this end, we first present a workflow framework that captures the unique execution logic of sensing tasks. Then we propose a staged scheme to decouple the original scheduling problem. Moreover, we leverage Lyapunov optimisation technique to achieve long-term objective. The experiment results verify the effectiveness and efficiency of our proposed algorithm.

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