Asynchronous distributed optimization via dual decomposition for delay-constrained flying ad hoc networks

Wen, Shaojie; Huang, Chuanhe

doi:10.1016/j.comcom.2019.02.006

Cited by 6 publications

(7 citation statements)

References 35 publications

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“…In Table (5), all the FANET CBRPs covered thus far are evaluated against one another using a variety of criteria in order to distinguish them and get a sense of which routing protocol should be used in a particular context. Table (6) examines and statistically discusses the various simulation tools utilised as a verification approach. From our study, UAV networks are prone to fast mobility and topological change.…”

Section: Comparison Of Cluster-based Routingmentioning

confidence: 99%

“…FANETs are becoming increasingly common, and they can provide actuation services while limiting human interaction and potentially life-threatening dangers [1]- [5]. FANETs have been receiving increased attention from researchers [6]. FANETs may be used in various contexts, from civilian to military, because of their robustness, adaptability, low running costs,…”

Section: Introductionmentioning

confidence: 99%

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Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

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Flying Ad-Hoc Network (FANET) is a set of Unmanned Aerial Vehicles (UAVs) interconnected wirelessly. FANETs self-organize and provide low-cost, adaptable, and simple-to-implement flying nodes, enabling them to complete complicated tasks more quickly and collectively. The high mobility of nodes and the highly dynamic topology pose challenges to communication design, particularly when creating a routing protocol for UAV networks; this has inspired researchers to contribute and develop this technology. Hierarchical routing technique known as clustering is necessary to offer scalability, survivability, and distribute payload among UAVs to maintain the performance. This study has proposed a comprehensive survey of the cluster-based routing protocols (CBRPs) in terms of their strengths, weaknesses, specific applications, method, number of nodes, and future improvements for serving FANETs. Moreover, 21 CBRPs based FANETs were reviewed in terms of their topology, challenges, scalability, characteristics, clustering strategy, outstanding features, cluster head (CH) selection, routing metrics, and performance measures.In addition, open issues that need to be addressed in future studies in the field of routing protocols for UAV networks were also debated. INDEX TERMSClustering algorithm, flying ad hoc network, routing protocol, scalability, unmanned aerial vehicle.

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Section: Comparison Of Cluster-based Routingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

2022

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“…Constraint (7) ensures that the channel capacity used by the UAVs is no smaller than the given threshold to meet the requirement of correct decoding. Constraint (8) represents SIC requirement for correct decoding of superimposed signals at GB, and (9) limits the maximum power levels of UAVs. Constraint (10) means that any UAV can only transmit signals on one channel at a specific time.…”

Section: Problem Formulation and Solutionmentioning

confidence: 99%

“…Combined objective function of problem P and constraint set gðχÞ are convex and continuous over their domain. Assume that there is one set fχ ∈ Ψg that makes (7), (8), and (9) hold as strict inequalities.…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

“…Next, we divide the UAVs whose channel gain differences satisfy the constraints into a group, in which the group number should be no more than the upper bound served by each channel. When each UAV is associated with a specific channel, we adjust the power levels of UAVs with asynchronous update [8] mechanism until the power levels of UAVs remain unchanged. Finally, we propose a channel allocation algorithm and a power control algorithm, which consider the effects of UAVs' fast mobility and the initialization parameters on the optimization performance.…”

Section: Introductionmentioning

confidence: 99%

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Joint Channel Allocation and Power Control for Uplink NOMA‐Assisted Multi‐UAV Networks

Wen

Deng

Liu³

2021

Wireless Communications and Mobile Computing

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The explosive growth of data leads to that the traditional wireless networks cannot enable various quality of service (QoS) communication for cellular-connected multi-UAV (unmanned aerial vehicle) networks. To overcome this obstacle, we solve the joint optimization problem of channel allocation and power control for uplink NOMA-assisted multi-UAV networks. Firstly, we design a mixed integer nonlinear programming framework, where the channel gains are characterized with integral form in time interval and sorted in nondescending order as the priority index of the decoded signal. In order to propose a feasible algorithm, the initial power levels of UAVs are obtained and integrated into the original problem which is reduced to integer programming problem. Then, the UAVs whose channel gain differences satisfy the constraints will be divided into a group to share the same channel, while the initial power levels of UAVs are adjusted to get a more satisfactory initial solution for power control. Combining the solution of channel allocation and the initial power levels, we solve power control problem with asynchronous update mechanism until the power levels of UAVs remain unchanged. Finally, we propose a channel allocation algorithm and a power control algorithm with the asynchronous optimization mechanism, respectively. Simulation results show that the proposed algorithms can effectively improve the network performance in terms of the aggregated rate.

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PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

Ilango,

Sivarajan,

RaviChandran

et al. 2024

Int J Communication

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SummaryFlying ad hoc network (FANET) is a technology that has seen tremendous growth in the past years due to its application in various military and civil developments. Conventional topology‐aided routing schemes are not suitable for large‐scale FANETs. This is because of the higher mobility rate in UAVs with the architecture differences. Hence, several path entries in routing become invalid and the neighboring nodes can be occupied before the interruption. Hence, it is very important to solve the afore‐explained issues in FANET. Initially, the task is assigned to the UAVs. These tasks are performed by satisfying the demands such as timing, congestion, and energy to attain the effective load‐balancing performance. Then, the optimal clustering and cluster head (CH) selection are performed using the developed Percentage of Circle Search and Spotted Hyena Optimizer (PCSSHO) in the communication path. Here, the feature selection is done based on constraints like position, speed, moving direction, height variation, link quality, and inter‐ and intra‐cluster distance. Then, the developed PCSSHO model performs routing by considering different constraints such as “end‐to‐end delay, delivery ratio, power consumption, and link quality.” Thus, the recommenced load balancing model in FANET secures an enhanced performance rate than the conventional load balancing frameworks.

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Asynchronous distributed optimization via dual decomposition for delay-constrained flying ad hoc networks

Cited by 6 publications

References 35 publications

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Cluster-Based Routing Protocols for Flying Ad Hoc Networks (FANETs)

Joint Channel Allocation and Power Control for Uplink NOMA‐Assisted Multi‐UAV Networks

PCSSHO: An implementation of Percentage of Circle Search and Spotted Hyena Optimizer for multi‐constraint load balancing and routing framework in FANET

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