Citation

Amodu, Oluwatosin Ahmed and Raja Mahmood, Raja Azlina and Althumali, Huda and Jarray, Chedia and Adnan, Mohd Hirzi and Bukar, Umar Ali and Abdullah, Nor Fadzilah and Nordin, Rosdiadee and Ahmad Zukarnain, Zuriati (2025) A question-centric review on DRL-based optimization for UAV-assisted MEC sensor and IoT applications, challenges, and future directions. Vehicular Communications, 53. p. 100899. ISSN 22142096

Text A question-centric review on DRL-based optimization for UAV-assisted MEC sensor and IoT applications, challenges, and future directions.pdf - Published Version Restricted to Repository staff only Download (5MB)

Abstract

Unmanned Aerial Vehicle (UAV)-assisted Internet of Things (IoT) applications vary widely including data monitoring, data collection and analysis, intelligent navigation and object tracking, surveillance and emergency response, vehicular and intelligent transport, and agricultural, marine, and photogrammetry. Mobile Edge Computing (MEC)-based UAV-assisted IoT networks enable resource-constrained mobile or IoT devices to offload computationally demanding tasks to UAVs or edge nodes with more computing power in order to improve battery consumption, performance, or Quality of Service. UAV-assisted IoT applications generally require a lot of precision for efficient UAV control involving UAV movement and position optimization and Deep Reinforcement Learning (DRL) has recently been identied as one of the most prominent techniques for facilitating this and optimizing of the terrestrial network performance, thus improving the operation of these applications. This paper aims to answer twelve important research questions relating to the research on DRL for Mobile Edge Computing (MEC)-based UAV-assisted sensor and IoT applications from 47 systematically selected articles. The questions cover a variety of topics including the UAV-assisted MEC IoT applications studied, variants of deployed DRL, the purpose of DRL, Markov Decision Processes (MDPs) components, unique network architectural features, environments and integrated technologies, role of UAVs, optimization constraints, joint optimization frameworks, energy-management techniques, metrics examined, benchmark algorithms and performance results as well as identied probable future considerations based on the review. Lastly, the challenges and future directions of DRL application in UAV-assisted MEC systems are discussed. This paper aims to provide both communication generalists and optimization specialists with a comprehensive understanding of the research landscape in this field.

Item Type:	Article
Uncontrolled Keywords:	Drones, Internet of Things, Wireless sensor network
Subjects:	Q Science > QA Mathematics > QA71-90 Instruments and machines > QA75.5-76.95 Electronic computers. Computer science
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	28 Mar 2025 00:16
Last Modified:	28 Mar 2025 00:16
URII:	http://shdl.mmu.edu.my/id/eprint/13625

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