Task Allocation in Eco-friendly Mobile Crowdsensing: Problems and Algorithms

• DOI: 10.1007/s11036-019-01312-9
• Publication Date: 2019-07-05T08:03:32Z
• Authors: Wei Gong; Xiaoyao Huang; Baoxian Zhang; Yawei Zhao
• Keywords: Crowdsensing

Mobile crowdsensing has emerged as a new sensing paradigm which has many advantages over traditional sensing paradigms. In this paper, we focus on the task allocation problem for eco-friendly mobile crowdsensing which aims to minimize carbon emissions under various constraints such as task deadline and road traffic constraints. We first describe the system model of eco-friendly mobile crowdsensing and formulate the task allocation problem in offline scenario and online scenario, respectively. Then we propose Eco-Friendly Task Allocation algorithm (EFTA) to address the allocation problem in offline scenario. This algorithm consists of two processes including transportation selection and worker-task matching. After this, we propose Online Eco-Friendly Task Allocation algorithm (OEFTA) to tackle the allocation problem in online scenario. The algorithm adopts greedy online task assignment/reassignment upon arrival of a new task or a new worker. Extensive simulation results show our proposed algorithms have much better performance than baseline algorithms.