Research Article
Activate Cost-Effective Mobile Crowd Sensing with Multi-access Edge Computing
@INPROCEEDINGS{10.1007/978-3-030-67720-6_6, author={Zhengzhe Xiang and Shuiguang Deng and Yuhang Zheng and Dongjing Wang and Cheng Zhang and Yuanyi Chen and Zengwei Zheng}, title={Activate Cost-Effective Mobile Crowd Sensing with Multi-access Edge Computing}, proceedings={Communications and Networking. 15th EAI International Conference, ChinaCom 2020, Shanghai, China, November 20-21, 2020, Proceedings}, proceedings_a={CHINACOM}, year={2021}, month={2}, keywords={Multi-access Edge Computing Mobile crowd sensing Task assignment Service deployment Incentive mechanism}, doi={10.1007/978-3-030-67720-6_6} }- Zhengzhe Xiang
Shuiguang Deng
Yuhang Zheng
Dongjing Wang
Cheng Zhang
Yuanyi Chen
Zengwei Zheng
Year: 2021
Activate Cost-Effective Mobile Crowd Sensing with Multi-access Edge Computing
CHINACOM
Springer
DOI: 10.1007/978-3-030-67720-6_6
Abstract
Recently, the mobile crowd sensing (MCS) technique is believed to be an important role in multi-source data acquisition tasks. With devices or people with different sensing abilities in the cities, we can easily split and distribute the complex task in an appropriate way so that those devices or people can be stimulated to collect data within different scopes individually, while the results of them can be analyzed and integrated collaboratively to fulfill that complex task. However, in typical centralized architecture, the latency brought by unstable and time-consuming long-distance network transmission limits the development of MCS. The multi-access edge computing (MEC) technique is now regarded as the key tool to solve this problem. By establishing a service provisioning system based at the edge of the network, the latency can be reduced and the analysis or integration can also be conducted in time with the help of corresponding services deployed on nearby edge servers. However, as the edge servers are resource-limited, the sensing abilities vary among devices or people, and the budget of fulfilling a task is determined, we should be more careful in task assignment and service deployment. In this paper, we investigate the relationship between the task quality and the cost in the MEC-based MCS system and propose the analysis framework of it based on two classical cost-performance balancing problems. Besides, we conduct comprehensive experiments to evaluate the performance of our approach. The results show that the proposed approach can easily obtain exact solutions, and the factors that may impact the results are also adequately explored.
