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.
In emergency response operations, using uncrewed aerial vehicles (UAVs) has recently become a promising solution due to their flexibility and easy deployment. However, tasks performed by the UAVs, e.g., object detection and human pose recognition, usually require a high computation capacity and energy supply. Furthermore, offloading tasks to the edge server-equipped base stations may not always be possible because of a lack of infrastructure or distance. Therefore, UAV-aided edge servers can be deployed near UAV scouts to provide computing services. However, a UAV can not perform all types of tasks since it has limitations on memory, available software, central processing unit (CPU), and graphics processing unit (GPU) capacity. Therefore, this study focuses on task offloading (TO), power, and computation resource allocation (PRA) problems in a multi-layer MEC-enabled UAV network while taking into account CPU and GPU requirements of tasks, the capacity of the devices (i.e., computational resources, power, and energy), and limitations on the type of tasks a UAVs can perform. The problem is formulated as a non-convex mixedinteger nonlinear problem to minimize the weighted sum of the maximum energy consumption ratio in the network and total task execution latency ratio, and then decomposed and converted into an integer and a convex problem. A messy genetic algorithm (mGA)-based TO and PRA strategy (mGA-TPR) is proposed to solve the problem, where two PRA strategies are based on the Karush-Kuhn-Tucker conditions used to solve the PRA problem. Simulation results verify that the proposed scheme can outperform the baseline methods.INDEX TERMS Multi-access edge computing, task offloading, resource allocation, messy genetic algorithm A solution to reduce the execution latency of the application and UAV energy consumption is to offload the task
Mobile crowd-sensing (MCS) is a data collection paradigm, which recruits mobile users with smart devices to perform sensing tasks on a city-wide scale. In MCS, a key challenge is task allocation, especially when MCS applications are time-sensitive, and the platform needs to consider task completion order (since a worker may perform multiple tasks and different task completion orders lead to different travel costs and response times, i.e., the times needed to arrive at the task venues), requirements of tasks (such as deadline and required sensor) and workers heterogeneity. In other words, the task allocation problem consists of multiple task completion order problems, which is challenging to solve due to the large solution space. Therefore, in this paper, we first formulate the considered problem into two related integer linear programming problems (i.e., assignment and task completion order problems) using a decomposition technique in order to reduce the problem size and enable the use of diverse searching strategies. Then, a deep Q-learning (DQN)-based algorithm, namely assignment DQN with a local search (A-DQN w/ LS), is proposed to determine the task-worker assignments, which iteratively employs an asymmetric traveling salesman (ATSP) heuristic to find the task completion orders of the workers. The local optimizer is applied at the end of the A-DQN algorithm to deal with the computation time and local optima. Simulation results show that the proposed method outperforms existing approaches under different sensing dynamics in terms of total cost. INDEX TERMS Deep reinforcement learning, mobile crowd-sensing, task allocation, tabu search
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