New Crowd Sensing Computing in Space-Air-Ground Integrated Networks
Yingjie Wang, Mingze Wang, Lingkang Meng, Qi Zhang, Xiangrong Tong, Zhipeng Cai · 2021
With the development of Space-Air-Ground Inte-grated Networks (SAGINs), how to design a distributed task cooperation mechanism with low-latency computing, high data rates, low delay and high coverage is an important research content. Crowd Sensing computing could provide the effective solutions for SAGINs. This paper first considers the combination of SAGINs with crowd sensing computing for improving the task completion efficiency in SAGINs. The crowdsoucing methods are considered into the SAGINs in order to solve the complex and large-scale sensing tasks. In this paper, based on Blockchain and Multi-access Edge Computing (MEC), the traditional centralized application service architecture is transferred into a lightweight and distributed network architecture. Under this lightweight and distributed network architecture, this paper proposes a Q-learning based Task Cooperation (QTC) mechanism through dividing the three-dimensional space based on Voronoi diagram. We conduct the comparison experiments for the proposed QTC mechanism on real dataset. The performances of the proposed QTC mechanism on rate of task completion and total income of task execution are evaluated by the comparison experiments.