Distributed Joint Optimization of Deployment, Computation Offloading and Resource Allocation in Coalition-based UAV Swarms

Yao, Kailing; Xu, Yuhua; Chen, Jin; Gong, Yuping; Yang, Yang; Yao, Changhua; Du, Zhiyong

doi:10.1109/wcsp49889.2020.9299672

Cited by 9 publications

(8 citation statements)

References 35 publications

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“…It should be noted that changing the strategy of a single UAV has a direct impact on the entire system, so the system's utility will converge to the optimal state [116]. In coalitionbased UAV-MEC swarms, the authors [117] investigated the joint deployment, computation offloading, power management, and channel access optimization issues. A random best and better response algorithm is proposed to solve the problem of satisfying the distributed nature of UAV-MEC swarms.…”

Section: -Coalition Formation Game (Cfg)mentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network.

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Section: -Coalition Formation Game (Cfg)mentioning

confidence: 99%

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Baktayan¹,

Al-Baltah²

2022

Sustainable Engineering and Innovation

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“…The optimization of UAV deployment is a research hotspot in the UAV field. Yao et al [22] analyzed the distribution characteristics of UAVs and proposed a random optimal response algorithm to solve the problem of UAV deployment, aiming to minimize the system energy consumption. Yang et al [23] designed an IoT architecture based on multi-UAV-assisted MEC and proposed a UAV deployment strategy based on a differential evolutionary algorithm, which achieved the load balancing among multiple UAVs.…”

Section: Related Workmentioning

confidence: 99%

Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

Chen

Zheng

Zhang

et al. 2021

Peer-to-Peer Netw. Appl.

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The combination of unmanned aerial vehicles (UAVs) and mobile edge computing (MEC) technology breaks through the limitations of traditional terrestrial communications. The effective line-of-sight channel provided by UAVs can greatly improve the communication quality between edge servers and mobile devices (MDs). To further enhance the Quality-of-Service (QoS) of MEC systems, a multi-UAV-enabled MEC system model is designed. In the proposed model, UAVs are regarded as edge servers to offer computing services for MDs, aiming to minimize the average task response time by jointly optimizing UAV deployment and computation offloading. Based on the problem definition, a two-layer joint optimization method (PSO-GA-G) is proposed. First, the outer layer utilizes a Particle Swarm Optimization algorithm combined with Genetic Algorithm operators (PSO-GA) to optimize UAV deployment. Next, the inner layer adopts a greedy algorithm to optimize computation offloading. The extensive simulation experiments verify the feasibility and effectiveness of the proposed PSO-GA-G. The results show that the PSO-GA-G can achieve a lower average task response time than the other three baselines.

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“…Authors in [35]- [37] worked on optimizing the energy consumption in a swarm of drones. In [35], the drones are classified into two types, i.e., user devices (UDs) and helper devices (HDs) where HDs have more computing capability than UDs.…”

Section: A Resource Management In a Group Of Drones In Different Architecturesmentioning

confidence: 99%

“…There was only one drone for updating the learning model. In [37], the deployment and computation problem is solved by a random best and better response (RAN-BBR) algorithm. The authors clustered drones into some coalitions with the same altitude.…”

Section: A Resource Management In a Group Of Drones In Different Architecturesmentioning

confidence: 99%

“…Obviously, the presented system model and learning strategies in [35]- [37] are not fully distributed and the computation offloading in each cluster also depends on the decision by the cluster head, which limits those works. In fact, each time just one drone is randomly aggregating the learning model and the others do not have any aggregation at the same time, i.e.…”

Section: A Resource Management In a Group Of Drones In Different Architecturesmentioning

confidence: 99%

See 1 more Smart Citation

Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach

et al. 2021

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Today, unmanned aerial vehicles (UAVs) or drones are increasingly used to enable and support multi-access edge computing (MEC). However, transferring data between nodes in such dynamic networks implies considerable latency and energy consumption, which are significant issues for practical real-time applications. In this paper, we consider an autonomous swarm of heterogeneous drones. This is a general architecture that can be applied for applications that need in-field computation, e.g. real-time object detection in video streams. Collaborative computing in a swarm of drones has the potential to improve resource utilization in a real-time application i.e., each drone can execute computations locally or offload them to other drones. In such an approach, drones need to compete for using each other's resources; therefore, efficient orchestration of the communication and offloading at the swarm level is essential. The main problem investigated in this work is computation offloading between drones in a swarm. To tackle this problem, we propose a novel federated learning (FL)-based fast and fair offloading strategy with a rating method. The simulation results demonstrate the effectiveness of the proposed strategy over other existing methods and architectures with average improvements of -23% in energy consumption, -15% in latency, +18% in throughput, and +9% in fairness.INDEX TERMS Swarm of drones, Multi-access edge computing, Collaborative computing, Federated learning.

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Distributed Joint Optimization of Deployment, Computation Offloading and Resource Allocation in Coalition-based UAV Swarms

Cited by 9 publications

References 35 publications

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

Joint computation offloading and deployment optimization in multi-UAV-enabled MEC systems

Fast and Fair Computation Offloading Management in a Swarm of Drones Using a Rating-Based Federated Learning Approach

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