Energy Efficient Hybrid Offloading in Space-Air-Ground Integrated Networks

Chen, Bingchang; Li, Na; Li, Yan; Tao, Xiaofeng; Sun, Guen

doi:10.1109/wcnc51071.2022.9771798

Cited by 12 publications

(8 citation statements)

References 11 publications

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“…6) Successive convex approximation (SCA) is an essential method within the SAGIN for addressing the resource allocation challenges critical to system sustainability and efficiency, especially when involving UAVs and ground users engaged in computational tasks [23] . SCA is instrumental in managing complex non-convex optimization issues, such as bandwidth allocation and UAV trajectory optimization [40] .…”

Section: A Classical Optimization Methodsmentioning

confidence: 99%

“…SCA has been effectively utilized in SAGIN contexts, where UAVs perform dual roles as computational task relays for ground users and as autonomous agents with their own operational tasks. It optimizes data offloading decisions to minimize the overall energy consumption, within the constraints imposed by task-related time delays [23] . Additionally, SCA is frequently integrated with other optimization methods such as alternating optimization to address interconnected sub-problems, including user scheduling, partial offloading, computational resource distribution, bandwidth management, and multi-UAV trajectory planning.…”

Section: A Classical Optimization Methodsmentioning

confidence: 99%

“…The power allocation subproblem in the resource allocation optimization was addressed using a convex optimization method, employing a multilevel water-filling method that satisfies the Karush-Kuhn-Tucker (KKT) conditions [23] Minimize the system energy consumption…”

Section: Satellites Civil Aircrafts Bssmentioning

confidence: 99%

See 2 more Smart Citations

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

Liang,

Yang,

Zhang

et al. 2024

J. Commun. Inf. Netw.

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The space-air-ground integrated networks (SAGIN) has emerged as a critical paradigm to address the growing demands for global connectivity and enhanced communication services. This paper gives a thorough review of the strategies and methodologies for resource allocation within SAGIN, focusing on the challenges and solutions within its complex structure. With the advent of technologies such as 6G, the dynamics of resource optimization have become increasingly complex, necessitating innovative approaches for efficient management. We examine the application of mathematical optimization, game theory, artificial intelligence (AI), and dynamic optimization techniques in SAGIN, offering insights into their effectiveness in ensuring optimal resource distribution, minimizing delays, and maximizing network throughput and stability. The survey highlights the significant advances in AI-based methods, particularly deep learning and reinforcement learning, in tackling the inherent challenges of SAGIN resource allocation. Through a critical review of existing literature, this paper categorizes various resource allocation strategies, identifies current research gaps, and discusses potential future directions. Our findings highlight the crucial role of integrated and intelligent resource allocation mechanisms in realizing the full potential of SAGIN for Manuscript

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Section: A Classical Optimization Methodsmentioning

confidence: 99%

Section: A Classical Optimization Methodsmentioning

confidence: 99%

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Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

Liang,

Yang,

Zhang

et al. 2024

J. Commun. Inf. Netw.

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“…where ω u denotes the channel bandwidth of the IoT device to the UAV, g i,m is the channel gain of the uplink, σ 2 is the additive white Gaussian noise (AWGN) power and p i is the transmission power of the channel [33].…”

Section: Channel Model 331 Iot Device-uav Channelmentioning

confidence: 99%

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Zhang

Jiang

et al. 2023

Drones

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In emergency situations, such as earthquakes, landslides and other natural disasters, the terrestrial communications infrastructure is severely disrupted and unable to provide services to terrestrial IoT devices. However, tasks in emergency scenarios often require high levels of computing power and energy supply that cannot be processed quickly enough by devices locally and require computational offloading. In addition, offloading tasks to server-equipped edge base stations may not always be feasible due to the lack of infrastructure or distance. Since Low Orbit Satellites (LEO) have abundant computing resources, and Unmanned Aerial Vehicles (UAVs) have flexible deployment, offloading tasks to LEO satellite edge servers via UAVs becomes straightforward, which provides computing services to ground-based devices. Therefore, this paper investigates the computational tasks and resource allocation in a UAV-assisted multi-layer LEO satellite network, taking into account satellite computing resources and device task volumes. In order to minimise the weighted sum of energy consumption and delay in the system, the problem is formulated as a constrained optimisation problem, which is then transformed into a Markov Decision Problem (MDP). We propose a UAV-assisted airspace integration network architecture, and a Deep Deterministic Policy Gradient and Long short-term memory (DDPG-LSTM)-based task offloading and resource allocation algorithm to solve the problem. Simulation results demonstrate that the solution outperforms the baseline approach and that our framework and algorithm have the potential to provide reliable communication services in emergency situations.

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“…MEC services are frequently deployed in SAGIN networks; the authors in [22] presented an iterative optimization approach and utilized a greedy algorithm and the SCA method to minimize the total computing cost of all GTs. Due to the limited battery capacity, the authors in [23] decreased energy consumption by enhancing the offloading ratios and allocation of processing resources for terrestrial users, UAVs, and satellites. While ensuring energy constraints, Ref.…”

Section: Introductionmentioning

confidence: 99%

Joint Resource Allocation Optimization in Space–Air–Ground Integrated Networks

Xu,

Yu,

Yang

2024

Drones

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A UAV-assisted space–air–ground integrated network (SAGIN) can provide communication services for remote areas and disaster-stricken regions. However, the increasing types and numbers of ground terminals (GTs) have led to the explosive growth of communication data volume, which is far from meeting the communication needs of ground users. We propose a mobile edge network model that consists of three tiers: satellites, UAVs, and GTs. In this model, UAVs and satellites deploy edge servers to deliver services to GTs. GTs with limited computing capabilities can upload computation tasks to UAVs or satellites for processing. Specifically, we optimize association control, bandwidth allocation, computation task allocation, caching decisions, and the UAV’s position to minimize task latency. However, the proposed joint optimization problem is complex, and it is difficult to solve. Hence, we utilize Block Coordinate Descent (BCD) and introduce auxiliary variables to decompose the original problem into different subproblems. These subproblems are then solved using the McCormick envelope theory, the Successive Convex Approximation (SCA) method, and convex optimization techniques. The simulation results extensively illustrate that the proposed solution dramatically decreases the overall latency when compared with alternative benchmark schemes.

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Energy Efficient Hybrid Offloading in Space-Air-Ground Integrated Networks

Cited by 12 publications

References 11 publications

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

Resource Allocation for Space-Air-Ground Integrated Networks: A Comprehensive Review

Resource Allocation and Offloading Strategy for UAV-Assisted LEO Satellite Edge Computing

Joint Resource Allocation Optimization in Space–Air–Ground Integrated Networks

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