DaTask: A Decomposition-Based Deadline-Aware Task Assignment and Workers’ Path-Planning in Mobile Crowd-Sensing

Abstract

Mobile crowd-sensing (MCS) has recently become a promising approach for massive data collection, which empowers common people to perform sensing tasks with their smart devices. In MCS, locations of tasks and workers are diverse, and workers need to visit different task venues to perform the tasks. The diversity of task and worker locations, tasks' location accessibility, and required sensor type make the task assignment problem highly challenging. In time-sensitive MCS applications, this task assignment problem becomes even more intractable because of the deadline and a lot of possible movement trajectories of the workers. In this paper, we introduce two types of workers and formulate the task assignment problem, which comprises an embedded structure. Furthermore, a decomposition technique is applied to decompose the original problem into a main problem (the assignment problem) and a set of sub-problems (traveling salesman problems). The assignment problem determines task-worker assignments, and the sub-problems determine trajectories of the workers. This decomposition allows using a simpler solution strategy. Then, a memetic genetic algorithm is proposed to address the assignment problem, while each sub-problem is solved using an asymmetric traveling salesman problem heuristic. Results from simulations verify that the proposed algorithm outperforms the baseline methods under various experimental settings. INDEX TERMS Asymmetric traveling salesman problem, mobile crowd-sensing, memetic genetic algorithm, participatory sensing, task allocation.

Time-Constrained Task Allocation and Worker Routing in Mobile Crowd-Sensing Using a Decomposition Technique and Deep Q-Learning

“…The considered task-worker assignment problem contains a set of workers' path-planning problems, which makes it very complex to solve. Therefore, we decompose the original problem into an assignment problem (i.e., the main problem) and a set of task completion order problems (i.e., subproblems) by employing a decomposition technique similar to one from [20]. In this section, the main problem and the subproblems are formulated as two ILP problems where the Deadline of task j ζ j Task duration of task j R j Set of required sensors for task j L j Location of task j w j…”

“…First, an ATSP heuristic, iterated 3opt (i3opt) [32], is applied to obtain the task completion order of each worker. Next, the values of Φ and T c (s) are obtained by using the task completion orders of the workers, and then reward r is calculated using Equation (20).…”

“…Content may change prior to final publication. [5,10], [10,20], [20,30], [30,40] [20,40] • MATC-IGA [17]: A genetic algorithm is employed to find the recruited workers set where the initial population is generated by using a random-greedy algorithm. MATC-IGA uses a repair operation to obtain a valid chromosome from the produced invalid chromosome through crossover and mutation.…”

“…• MGATA [20]: In this study, a memetic genetic algorithm is applied to find the task-worker assignments and uses an ATSP heuristic to obtain the worker's task completion order. However, because of the different assumptions and the considered scenario, MGATA can not be directly applied to our problem.…”

“…In our previous paper [20], although a similar problem has been considered, it has limitations. First, a metaheuristic approach, i.e., a memetic genetic algorithm for task allocation (MGATA), is used to find the task-worker assignment, which is more prone to fall into local optima since population-based algorithms are easy to lose genetic diversity due to selection pressure and genetic drift [21].…”