Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks

Nway, Nway; Alsenwi, Madyan; Tun, Yan Kyaw; Han, Zhu; Hong, Choong Seon

doi:10.1109/tits.2022.3150176

Cited by 45 publications

(14 citation statements)

References 32 publications

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“…Due to energy and computation resource constraints of aerial platforms, especially for UAVs, offloading computationally heavy tasks from cellular-connected NTNs to edge nodes will improve the network perseverance. In this regard, joint task offloading, communication and computation resource allocation problems to minimize the energy consumption of mobile devices and UAVs and/or latency especially in a multi-UAV scenario can be formulated and solved using reinforcement learning methods [216,217].…”

Section: Ntn-aided Pervasive Computingmentioning

confidence: 99%

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

et al. 2023

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Non terrestrial networks (NTN) involving 'in the sky' objects such as low-earth orbit satellites, high altitude platform systems (HAPs) and Unmanned Aerial Vehicles (UAVs) are expected to be integral components of next generation cellular systems. With the deployment of 5G services and beyond, NTNs are leveraged to assist as aerial base stations in providing ubiquitous network connectivity and service to ground users or be deployed as aerial users connected to the cellular network. NTN-aided wireless communication offers multiple benefits such as mobility, flexibility, resistance to ground physical attacks and wide coverage. However, due to their limited resources and the current design of terrestrial cellular systems that do not account for aerial users, and other restrictions such as service requirements, limited available power and storage resources on high-throughput satellites, resource allocation, location of the high altitude platform base station and the flight trajectory of the UAVs need to be intelligently controlled to satisfy various objectives both from an aerial base station and overall network perspectives. To achieve this, many works have explored Reinforcement Learning (RL) techniques to allow aerial platforms in non-terrestrial networks to learn from past observations and achieve some optimal control policy. In this paper and differently from prior surveys, we contribute a comprehensive review of the control objectives required by non-terrestrial platforms that have been solved using RL formulations. We provide an up-to-date overview of the latest applications of RL techniques for different NTN-aided wireless communication aspects. The survey focuses on Markov Decision Process (MDP) formulations in terms of states, actions, and rewards. We synthesize a taxonomy from the surveyed literature and provide a comprehensive representation of the current usages of RL in NTN-aided wireless communications. A qualitative analysis of the level of realism achieved in the works presented in the literature is provided based on several factors that pertain to the simulation environment, station deployment setting, wireless channel assumption, and energy considerations. We also curate a list of challenges that remain to be considered by the research community in order to achieve more efficient deployments and close the simulation-to-reality gap.

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Section: Ntn-aided Pervasive Computingmentioning

confidence: 99%

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

et al. 2023

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“…The total delay problem experienced by users in the system is decomposed into three sub-problems for solution. Ei et al [22] studies a two-level MEC system assisted by multiple UAVs. The Block Successive Upper-bound Minimization algorithm is used to solve the joint problem of resource allocation and task unloading.…”

Section: Related Workmentioning

confidence: 99%

Joint Optimization between Task Offloading and Resource Allocation in Mobile Edge Computing with multiple UAVs

Chen¹,

Gao

Zou³

et al. 2023

Preprint

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Energy consumption and completion time are two hot issues in UAV (Unmanned Aerial Vehicles) assisted edge computing.The current research is mainly focused on the interaction between UAVs and edge servers, while the interplay between UAVs is rarely considered.In this paper, in the considered scenario with multiple UAVs and servers, the UAVs may possess idle resources, and thus function as temporary servers to provide task offloading services for other UAVs.We begin by formulating a multi-objective joint optimization problem, which aims to balance the energy consumption and time delay in order to maximize the benefits of the system. Then we use a multi-objective genetic algorithm called Non-dominated Sorting Genetic Algorithm II (NSGA-II) to iteratively find the optimal offloading strategy.Through numerical simulation, the results obtained from the numerical simulation demonstrate the superiority of the proposed method in achieving the Pareto optimal frontier that balances energy consumption and completion time.

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“…Previous studies discussed UAV computing over a terrestrial MEC station [16,[138][139][140][141][142][143][144]. For example, the authors of [138] presented UAV trajectory control based on DRL in UAV computing, but task offloading and resource allocation were not considered.…”

Section: Collaboration Of Multi-uav Computingmentioning

confidence: 99%

“…In multi-UAV computing, the aim is to optimize fairness among everything in smart environments. The authors of [144] proposed an energy-efficient communication and computation resource management in a two-stage mobile computing system aided by multi-UAVs. The authors postulated that the UAV compute a portion of users' offloaded computing activities while the remainder is transmitted to the MEC-enabled BS.…”

Section: Collaboration Of Multi-uav Computingmentioning

confidence: 99%

See 1 more Smart Citation

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Alsamhi

Shvetsov

Kumar

et al. 2022

Drones

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Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

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Energy-Efficient Resource Allocation in Multi-UAV-Assisted Two-Stage Edge Computing for Beyond 5G Networks

Cited by 45 publications

References 32 publications

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

Reinforcement Learning in the Sky: A Survey on Enabling Intelligence in NTN-Based Communications

Joint Optimization between Task Offloading and Resource Allocation in Mobile Edge Computing with multiple UAVs

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

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