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论文类型：会议论文

发表时间：2021-01-10

卷号：155

页面范围：360-368

关键字：MCS; multi-task allocation; incentive payment; quality utility

摘要：Mobile crowd sensing has attracted the attention of many researchers with the development of mobile phones. Many studies focus on the optimization of single-task oriented allocation, and most of them optimize only one objective. We consider multi-objective optimization for multi-task allocation in mobile crowd sensing with a limited participants pool. We formulate the problem for temporal-spatial coverage tasks. Two kinds of methods called constrain method and heuristic algorithm method are used to solve the multi-objective optimization problem. For the constrain method, we propose two new greedy algorithms: GMaxEOQU and GMinEOIP. GMaxEOQU pursues the max overall quality utility with budget constraint while GMinEOIP minimize the overall incentive payment with overall quality utility constraint. For the heuristic algorithm method, we propose a Pareto-optimal particle swarm optimization algorithm (PPSO) to search a set of Pareto-optimal solutions for the platform. Extensive experiments validate the performance of our algorithms. (C) 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/) Peer-review under responsibility of the Conference Program Chairs.