A Location-based Delay-constrained Task Assignment Framework in Mobile Crowd-sensing
- 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 the 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
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