Smart City Gnosys

Smart city article details

Title	3D Trajectory Optimization For Multimission Uavs In Smart City Scenarios
ID_Doc	184
Authors	Zema N.R.; Natalizio E.; Di Puglia Pugliese L.; Guerriero F.
Year	2024
Published	IEEE Transactions on Mobile Computing, 23, 1
DOI	http://dx.doi.org/10.1109/TMC.2022.3215705
Abstract	There is a definite possibility that, in a recent future, Unmanned Aerial Vehicles (UAVs) will form the backbone of any smart city in terms of automation and networking. One approach to extend the UAVs' resources spectrum is to provide a mean for them to opportunistically recharge and connect to otherwise unreachable networks: provide Training and Recharge Areas (TRAs). In these dedicated areas, the UAVs could dock to Energy and Data Dispensers (EDD) devices to resupply their batteries and exploit a high-speed connection. To autonomously move through the smart city while accomplishing a set of given tasks but, at the same time, consider visiting the EDDs, is part of a tridimensional trajectory planning problem that needs to be addressed. In this paper, we formally define the combinatorial optimization problem representing the trajectory planning. We consider the case in which more than one UAV can be connected with the same EDD at the same time, by properly addressing the assignment of the bandwidth. Through simulative investigation, realistic values for the solution of the optimization problem are found. The behavior of the proposed model is compared with an "online" approach that does not require the same resources and knowledge and whose evaluation and comparison with the "offline" approach are performed through network simulation.
Author Keywords	Distributed control systems; MILP; reactive planning; wireless networks

Similar Articles

Id	Similarity	Authors	Title	Published
20976	0.942	Natalizio E.; Di Puglia Pugliese L.; Zema N.R.; Guerriero F.	Download And Fly: An Online Solution For The Uav 3D Trajectory Planning Problem In Smart Cities	DIVANet 2019 - Proceedings of the 9th ACM Symposium on Design and Analysis of Intelligent Vehicular Networks and Applications (2019)
34565	0.883	Eskandari M.; Savkin A.V.; Deghat M.	Kinodynamic Model-Based Uav Trajectory Optimization For Wireless Communication Support Of Internet Of Vehicles In Smart Cities	Drones, 8, 10 (2024)
40342	0.879	Wan X.; Ghazzai H.; Massoud Y.; Menouar H.	Optimal Collision-Free Navigation For Multi-Rotor Uav Swarms In Urban Areas	IEEE Vehicular Technology Conference, 2019-April (2019)
7073	0.876	Du P.; Shi Y.; Cao H.; Garg S.; Alrashoud M.; Shukla P.K.	Ai-Enabled Trajectory Optimization Of Logistics Uavs With Wind Impacts In Smart Cities	IEEE Transactions on Consumer Electronics, 70, 1 (2024)
32665	0.875	Zhou X.; Tian N.; Guo H.; Liu J.	Intelligent Uav Charging Station Deployment And Path Planning In Smart City	International Conference on Communication Technology Proceedings, ICCT (2023)
52354	0.873	Bahabry A.; Ghazzai H.; Vesonder G.; Massoud Y.	Space-Time Low Complexity Algorithms For Scheduling A Fleet Of Uavs In Smart Cities Using Dimensionality Reduction Approaches	SysCon 2019 - 13th Annual IEEE International Systems Conference, Proceedings (2019)
59314	0.872	Marques E.L., Jr.; Coelho V.N.; Coelho I.M.; Frota Y.A.D.M.; Koochaksaraei R.H.; Ochi L.S.; Coelho B.N.	Uavs Routes Optimization On Smart Cities And Regions	RAIRO - Operations Research, 56, 2 (2022)
46849	0.87	ElSayed M.; Mohamed M.	Robust Digital-Twin Airspace Discretization And Trajectory Optimization For Autonomous Unmanned Aerial Vehicles	Scientific Reports, 14, 1 (2024)
25409	0.868	Lasla N.; Ghazzai H.; Menouar H.; Massoud Y.	Exploiting Land Transport To Improve The Uav'S Performances For Longer Mission Coverage In Smart Cities	IEEE Vehicular Technology Conference, 2019-April (2019)
35118	0.868	Gao J.; Pan Y.; Li Z.; Han Q.; Chen Q.	Leveraging Public Buses To Relay Uavs For On-Demand Applications	Proceedings of the Annual International Conference on Mobile Computing and Networking, MOBICOM (2022)