Joint optimisation of UAV grouping and energy consumption in MEC‐enabled UAV communication networks

Zhu, Zhengying; Qian, Li Ping; Shen, Jiafang; Huang, Liang; Wu, Yuan

doi:10.1049/iet-com.2019.1179

Cited by 10 publications

(8 citation statements)

References 29 publications

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“…Based on the topological information of UAV selfassembling network routing protocol design, UAV selfassembling network communication data greedy forwarding, and peripheral forwarding according to the communication transmission between UAV and UAV swarm, the communication data forwarding mode should be designed according to the actual situation, combined with the spatial characteristics of UAV in the process of operation; two forwarding modes are designed from two aspects, namely, greedy forwarding and peripheral forwarding [4][5][6]. The first one is greedy forwarding: in the UAV self-organizing network structure, the neighboring transmission node with the smallest spatial linear distance between the local neighbor table and the transmission target node is selected from the transmission node as the next data delivery node, and the node is also taken as the core node in the routing protocol.…”

Section: Introductionmentioning

confidence: 99%

UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm

Zhuo

Song²,

Xu³

2022

Computational and Mathematical Methods in Medicine

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Topological information is provided, and research on the design of routing protocols for UAV self-assembling networks is conducted, in order to enable fleet communication transfer between UAVs and UAVs and enhance their communication transmission rate in the self-assembling network. A new routing protocol is proposed through greedy forwarding and peripheral forwarding of UAV self-assembling network communication data, UAV self-assembling network planarization processing, dynamic adjustment of routing mode based on topological information, and routing protocol decision content generation. The proposed network is described using stochastic geometry theory, with the UAV and building locations modeled as two independently distributed Poisson point processes and the building shape modeled as a rectangular body with height obeying the Rayleigh distribution. An estimated equation for typical user coverage is produced using this model. The simulation results show that the approximate expression matches with the simulation results with reduced computational complexity, which verifies the validity of the approximate analysis. By comparing it with the clustering-based routing protocol, it is concluded that the new routing protocol conditions for UAV self-assembly network can realize the communication transmission between UAVs and drones and further promote their communication transmission rate.

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Section: Introductionmentioning

confidence: 99%

UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm

Zhuo

Song²,

Xu³

2022

Computational and Mathematical Methods in Medicine

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show abstract

“…Nevertheless, all UAVs superposing on the same resource block may cause severe decoding delay and co-channel interference. To deal with this problem, a multi-UAV grouping method is proposed in [79] to reduce the number of UAVs on the same resource block. Then, the UAVs' transmit power and BSs' computation resources are optimized based on the Karush-Kuhn-Tucker (KKT) conditions to minimize the sum of energy consumption related to communication and computing.…”

Section: When Uavs Serve As Mobile Usersmentioning

confidence: 99%

A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Song

Qin

Hao

et al. 2022

Computer Communications

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“…Game theory has been widely used in resource optimizations of distributed wireless networks due to its low computational complexity and well effectiveness [47]- [52]. Hence, the offloading game model is formulated as (24) in which M is the set of coalitions, N is the set of UAV members and U m,i is the utility function of n m,i . It can be found that when the member changes its own strategy, it has an impact on other members in same coalition, the neighbors and neighbors' coalition members.…”

Section: A Game Modelmentioning

confidence: 99%

Joint Computation Offloading, Channel Access and Scheduling Optimization in UAV Swarms: A Game-Theoretic Learning Approach

Chen

Wang

et al. 2021

IEEE Open J. Comput. Soc.

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Coalition-based unmanned aerial vehicle (UAV) swarms have been widely used in urgent missions. To fasten the completion, mobile edge computing (MEC) has been introduced into UAV networks where coalition leaders act as servers to help members with data computing. This paper investigates a relative delay optimization in MEC-assisted UAV swarms. Considering that the scheduling methods have great impact on the delay, some theoretical analysis are made and a scheduling method based on shortest effective job first (SEJF) is proposed. Based on the coupled relationship between scheduling and resource allocation, the computation offloading and channel access problem are then jointly optimized. To solve the problem in distributed UAV networks, the optimization problem is formulated as an offloading game. It is proved that the game is an exact potential game (EPG) and it has at least one pure strategy Nash Equilibrium (PNE). To reach the PNE, a distributed offloading algorithm based on concurrent best-better response (CBBR) is designed. Finally, the simulations show that the performance of the proposed CBBR algorithm is better than traditional algorithms. Compared with other scheduling methods, the proposed scheduling method based on SEJF reduces the delay by up to 30%.

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Joint optimisation of UAV grouping and energy consumption in MEC‐enabled UAV communication networks

Cited by 10 publications

References 29 publications

UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm

UAV Communication Network Modeling and Energy Consumption Optimization Based on Routing Algorithm

A comprehensive survey on aerial mobile edge computing: Challenges, state-of-the-art, and future directions

Joint Computation Offloading, Channel Access and Scheduling Optimization in UAV Swarms: A Game-Theoretic Learning Approach

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