3D Multi-Drone-Cell Trajectory Design for Efficient IoT Data Collection

Weisen Shi; Junling Li; Nan Cheng; Feng Lyu; Yanpeng Dai; Haibo Zhou; Xuemin; Shen

doi:10.1109/icc.2019.8761719

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arXiv · 1906.00776

3D Multi-Drone-Cell Trajectory Design for Efficient IoT Data Collection

Weisen Shi·Junling Li·Nan Cheng·Feng Lyu·Yanpeng Dai·Haibo Zhou·Xuemin·Shen

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Abstract

Drone cell (DC) is an emerging technique to offer flexible and cost-effective wireless connections to collect Internet-of-things (IoT) data in uncovered areas of terrestrial networks. The flying trajectory of DC significantly impacts the data collection performance. However, designing the trajectory is a challenging issue due to the complicated 3D mobility of DC, unique DC-to-ground (D2G) channel features, limited DC-to-BS (D2B) backhaul link quality, etc. In this paper, we propose a 3D DC trajectory design for the DC-assisted IoT data collection where multiple DCs periodically fly over IoT devices and relay the IoT data to the base stations (BSs). The trajectory design is formulated as a mixed integer non-linear programming (MINLP) problem to minimize the average user-to-DC (U2D) pathloss, considering the state-of-the-art practical D2G channel model. We decouple the MINLP problem into multiple quasi-convex or integer linear programming (ILP) sub-problems, which optimizes the user association, user scheduling, horizontal trajectories and DC flying altitudes of DCs, respectively. Then, a 3D multi-DC trajectory design algorithm is developed to solve the MINLP problem, in which the sub-problems are optimized iteratively through the block coordinate descent (BCD) method. Compared with the static DC deployment, the proposed trajectory design can lower the average U2D pathloss by 10-15 dB, and reduce the standard deviation of U2D pathloss by 56%, which indicates the improvements in both link quality and user fairness.

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BibTeX RIS

Weisen Shi, Junling Li, Nan Cheng, Feng Lyu, Yanpeng Dai, Haibo Zhou, Xuemin, Shen. 2019-05-29. 3D Multi-Drone-Cell Trajectory Design for Efficient IoT Data Collection. https://doi.org/10.1109/icc.2019.8761719

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