Space-Time Low Complexity Algorithms for Scheduling a Fleet of UAVs in Smart Cities Using Dimensionality Reduction Approaches

Bahabry, Ahmed; Ghazzai, Hakim; Vesonder, Gregg; Massoud, Yehia

doi:10.1109/syscon.2019.8836828

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…Other studies on conceptual ideas include a study that compares how space is utilized in terms of architecture, information for design and human and insect construction [134]. Also, the significance of visibility in urban areas that could become urban landmarks has been highlighted regarding the adaptation of smart urban spaces [135].…”

Section: Conceptual Designmentioning

confidence: 99%

Transformation of Buildings and Urban Spaces to Adapt for Future Mobility: A Systematic Literature Review

Park,

Kim,

Maing

et al. 2023

Land

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The development of smart cities has led to transforming modern city operations by applying emerging technologies from the fourth industrial revolution such as the Internet of Things, BIM and machine learning. To prepare for the future development of cities, it is necessary to investigate the current status of city development, research topics and the direction toward future cities. In this study, a systematic literature review was conducted following the combination of literature review guides by Kitchenham and the PRISMA statement. The review identified 141 peer-reviewed academic papers from web-based archives, such as Web of Science and SCOPUS, that were categorized into five topics related to smart cities and urban areas. The technologies applied in the research were analyzed to determine the direction future cities should take based on current smart cities and emerging technologies that will shape individuals’ daily lives. However, it was discovered that research papers on critical areas, such as mobility and management, were lacking compared to those on building design and urban planning. This paper concludes with an example of a future urban area that has been transformed due to adoption of future mobility technology to address the shortcomings of the reviewed literature on building and urban environments.

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Section: Conceptual Designmentioning

confidence: 99%

Transformation of Buildings and Urban Spaces to Adapt for Future Mobility: A Systematic Literature Review

Park,

Kim,

Maing

et al. 2023

Land

View full text Add to dashboard Cite

show abstract

“…In [17] and [18], the authors solved MILP problem implementing a generic scheduling framework to manage a fleet of micro-UAVs to cover spatially and temporally distributed events. An extension of the later work, using low complexity algorithms, is presented in [19]. Another scheduling approach of UAVs is presented in [20] where an enhanced version of the ant-colony optimization (ACO) algorithm is utilized for UAVs task allocation and route planning.…”

Section: A Related Workmentioning

confidence: 99%

“…However, most of the solutions are based on MILP which are complex [16]- [19] or meta-heuristic solutions [14]- [16]. The proposed approaches remain centralized where a central node, e.g.…”

Section: A Related Workmentioning

confidence: 99%

A Generic Spatiotemporal Scheduling for Autonomous UAVs: A Reinforcement Learning-Based Approach

Bouhamed

Ghazzai

Besbes

et al. 2020

IEEE Open J. Veh. Technol.

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Considerable attention has been given to leverage a variety of smart city applications using unmanned aerial vehicles (UAVs). The rapid advances in artificial intelligence can empower UAVs with autonomous capabilities allowing them to learn from their surrounding environment and act accordingly without human intervention. In this paper, we propose a spatiotemporal scheduling framework for autonomous UAVs using reinforcement learning. The framework enables UAVs to autonomously determine their schedules to cover the maximum of pre-scheduled events spatially and temporally distributed in a given geographical area and over a predetermined time horizon. The designed framework has the ability to update the planned schedules in case of unexpected emergency events. The UAVs are trained using the Q-learning (QL) algorithm to find effective scheduling plan. A customized reward function is developed to consider several constraints especially the limited battery capacity of the flying units, the time windows of events, and the delays caused by the UAV navigation between events. Numerical simulations show the behavior of the autonomous UAVs for various scenarios and corroborate the ability of QL to handle complex vehicle routing problems with several constraints. A comparison with an optimal deterministic solution is also provided to validate the performance of the learning-based solution.

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Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey

Alam

Arafat

Moh

et al. 2022

Journal of Network and Computer Applications

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Space-Time Low Complexity Algorithms for Scheduling a Fleet of UAVs in Smart Cities Using Dimensionality Reduction Approaches

Cited by 6 publications

References 9 publications

Transformation of Buildings and Urban Spaces to Adapt for Future Mobility: A Systematic Literature Review

Transformation of Buildings and Urban Spaces to Adapt for Future Mobility: A Systematic Literature Review

A Generic Spatiotemporal Scheduling for Autonomous UAVs: A Reinforcement Learning-Based Approach

Topology control algorithms in multi-unmanned aerial vehicle networks: An extensive survey

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