A survey on task scheduling and optimization techniques for IoT-enabled UAV with Edge / Fog computing

Aram Satouf (), Ali Hamidoğlu (), Omer Melih Gul (), Alar Kuusik (), Seifedine Nimer Kadry () and Ali Elghirani ()
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Aram Satouf: Bahcesehir University
Ali Hamidoğlu: Bahcesehir University
Omer Melih Gul: Bahcesehir University
Alar Kuusik: Tallinn University of Technology
Seifedine Nimer Kadry: Lebanese American University
Ali Elghirani: Libyan International Medical University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 3, No 9, 33 pages

Abstract: Abstract The Internet of Things (IoT) and cloud computing are two technologies that are rapidly growing and have the potential to change many different industries. IoT devices provide real-time data gathering and analysis, enabling organizations to make data-driven choices. Cloud computing offers a scalable and flexible platform for storing and processing the enormous amounts of data IoT devices create. However, several issues like excessive latency, network inefficiency, and security concerns have arisen due to the centralized architecture of cloud computing, particularly in IoT applications where strict real-time performance and operational reliability requirements are present including intelligent transportation systems (ITS), unmanned ground and aerial vehicles (UGVs, UAVs). Edge computing, a new architecture that tries to decentralize data processing from the cloud to the network edge, has been presented as a solution to these problems. In this survey, we investigate the role of cloud, fog, and edge computing in the smart environment containing wireless sensor networks (WSNs) and mobile IoT devices, especially UAVs. Integration of optimization algorithms and meta-heuristic techniques are provided in the context of IoT applications. Furthermore, we discuss the benefits of fog and edge computing, use cases, and the Quality of Service (QoS) used for task scheduling.

Keywords: Internet of Things (IoT); task scheduling; unmanned aerial vehicles (UAVs); wireless sensor networks (WSN); fog and edge computing; optimization; meta-heuristic (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11235-025-01320-z

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