Overlapping Coalition Formation Game via Multi-Objective Optimization for Crowdsensing Task Allocation
نویسندگان
چکیده
With the rapid development of sensor technology and mobile services, service model crowd sensing (MCS) has emerged. In this model, user groups perceive data through carried terminal devices, thereby completing large-scale distributed tasks. Task allocation is an important link in MCS, but interests task publishers, users, platforms often conflict. Therefore, to improve performance MCS allocation, study proposes a repeated overlapping coalition formation game scheme based on multiple-objective particle swarm optimization (ROCG-MOPSO). The (OCF) used describe resource relationship between users tasks, design two strategies, allowing form coalitions for different Multi-objective optimization, other hand, strategy that considers multiple simultaneously problems. we use multi-objective algorithm adjust parameters OCF better balance thus obtain more optimal scheme. To verify effectiveness ROCG-MOPSO, conduct experiments dataset compare results with schemes related literature. experimental show our ROCG-MOPSO performs superiorly key indicators such as average revenue, platform completion rate, surplus resources.
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ژورنال
عنوان ژورنال: Electronics
سال: 2023
ISSN: ['2079-9292']
DOI: https://doi.org/10.3390/electronics12163454