Model-Based Prediction of the Aerial Base Station Position Based on Local Information
M.Sc. Oleksandr Andryeyev
- Nowadays the use of Aerial Base Stations (ABSs) become more and more attractive approach to increase the system capacity in areas with temporarily high data demand. In our research we have found that centralized placement algorithms pose high computational and communication requirements on the studied system. Instead, we propose to use the distributed system with implicit control, where each agent acts based on local knowledge only.
To reduce mutual interference, it is necessary to separate ABSs in space. However, the agent may not have up-to-date information about its neighbors. Thus, it is proposed to use a model-based prediction using the model of unmanned aerial vehicle.
* Literature research on the topic, especially in multi-robot deployment systems;
* Design and implementation of the algorithm;
* Performance analysis.
• analytical mind;
• Python knowledge.
 O. Andryeyev and A. Mitschele-Thiel, “Efficiency vs. accuracy of aerial base station placement,” in International Conference on Networked Systems 2019 (NetSys 2019), (Garching b. München, Germany), pp. 1–6, IEEE, 03 2019.
 M. Erdelj, E. Natalizio, K. R. Chowdhury, and I. F. Akyildiz, “Help from the sky: Leveraging uavs for disaster management,” IEEE Pervasive Computing, vol. 16, no. 1, pp. 24–32, 2017.