An Efficient Task Allocation in Mobile Crowdsensing Environments

Abstract

Mobile Crowdsensing (MCS) is gaining attention for large-scale sensing that involves three types of entities: task requesters, workers equipped with sensing devices, and the platform that assigns tasks to workers considering their objectives and constraints. However, finding an allocation solution that satisfies the conditions above is NP-hard. A few studies suggested approximate solutions to this problem, focusing on one of the task’s objectives: coverage maximization. Yet, they implement it in a single-task environment or with weak objective consideration, i.e., they consider other objectives, reducing the utility the task will receive. This study proposes a task allocation that focuses only on maximizing the task coverage, where we improved the solution to consider future task coverage possibilities. We consider an opportunistic MCS environment in which sensing has no impact on user trajectories. We assume a one-to-many matching where a task can be assigned to several workers, while a worker can be matched to at most one task. We first formulate the problem mathematically and prove it to be NP-hard. Then, we design three heuristic-based solutions that are more efficient and perform extensive performance evaluations based on a real-world dataset. Each solution improves the data quality and has a maximum execution time of milliseconds.