An adaptive Flying Ad-hoc Network (FANET) for disaster response operations to improve quality of service (QoS)

Pandey, Ankur; Shukla, Piyush Kumar; Agrawal, Ratish

doi:10.1142/s0217984920500104

Cited by 23 publications

(9 citation statements)

References 23 publications

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“…In the study, the route identification function is used to select a route that considers both the remaining energy of the nodes and their Euclidean distance from one another. The Fruit Fly Optimization Algorithm (FFOA) improves the functionality and efficiency of clustering with K-Means and is compared to other optimization approaches, such as comprehensive learning particle swarm optimizer (CLPSO), classification of Compositional-Aware COrrelation NETworks (CACONET), Grey Wolf Optimization Based Clustering In Vehicular Ad-Hoc Networks (GWOCNET), and Energy-aware Cluster-based Routing in FANETs ECRNET [50]. These comparisons are made according to various performance characteristics that the FFOA exhibits.…”

Section: ) Bio-inspiredmentioning

confidence: 99%

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

et al. 2023

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Flying Ad hoc Network (FANET) is a self-organizing wireless network that constitutes swarms of flying nodes, namely Unmanned Aerial Vehicles (UAV), and communicates in close proximity. It has various distinguishing characteristics that set it apart from other ad hoc networks, posing some issues, particularly in routing. UAVs are highly dynamic and have frequent topology changes. Hence, the network urges an efficient routing technique to coordinate the node swarms and enhance the evaluation metrics of the network. The biological behavior of various living organisms, such as animals, insects, microbes, and humans, inspires researchers to solve various routing problems in ad hoc networks. Decentralized self-organized swarms of UAVs closely resemble the biological system. Therefore, the Bio-Inspired Algorithms (BIA) resolve a wide range of routing challenges in FANET. A Systematic Literature Review (SLR) is adopted to survey FANET routing methods based on non-hybrid and hybrid BIAs to properly comprehend the existing bio-inspired strategies used in FANET routing. The review will be beneficial for the researchers in the specified area. To our knowledge, no SLR has been conducted about the FANET routing protocol that employs BIA. This paper examines (1) the characteristics and features of existing routing algorithms, (2) the need of both non-hybrid and hybrid BIA for effective and optimal routing, (3) an analysis of the method's simulation tools, evaluation metrics, and mobility models, (4) the current issues and scope of the study related to the specified method.INDEX TERMS Bio-Inspired Algorithm (BIA), Flying Ad hoc Networks (FANET), Metaheuristic optimization, Systematic Literature Review (SLR), Unmanned Aerial Vehicle (UAV).

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Section: ) Bio-inspiredmentioning

confidence: 99%

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

et al. 2023

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“…[14] recommended Using a hybrid FANETs UAV clustering scheme, which considers the impact of mobility on the election of cluster heads, and improves the hybrid metaheuristic algorithm to improve clustering efficiency, thereby enhancing the lifespan of the system but UAV clustering scheme at the same time has high complexity. An adaptive flying Ad-hoc network [15] improves the performance and efficiency of K-Means clustering by means of fruit fly optimization algorithm, but it has high complexity and fails to assign weights for the degree of impact of the performance metrics, which affects the balance of the network structure.…”

Section: Introductionmentioning

confidence: 99%

Structure management scheme of flying ad-hoc networks (FANET): A dynamic weight values weighted clustering algorithm

Wang¹,

Zhang²,

Liu

et al. 2022

J. Phys.: Conf. Ser.

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To resolve the problem of structure management in Unmanned Aerial Vehicle(UAV) formation, a dynamic weight values weighted clustering algorithm(DWVWCA) is proposed in this article, in which the cluster scheme for network optimization is used. Firstly, the residual energy model is improved to be more realistic. Secondly, the combined weighting method is introduced to make the comprehensive weight value of the UAV subjective and objective. Finally, the dynamic weight threshold is introduced to calculate the comprehensive weight value in real-time. The simulation results show that DWVWCA can effectively improve the objectivity of cluster head election, extend the UAV formation survival cycle, balance the network structure, reduce the number of cluster reorganization and fusion, and enhance the network stability.

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“…In [6], authors have studied ground user communication through a UAV-assisted relay network to minimize the network's energy consumption. In [7], authors have investigated UAV-assisted communication for disaster management using a Fruit Fly optimization clustering algorithm. In [8], authors have shown that UAV provides ground node coverage in post-disaster scenarios and evaluates the optimal relay hops of the device-to-device (D2D) wireless network.…”

Section: Introductionmentioning

confidence: 99%

When UAV and ad-hoc NOMA-BS meets in Disaster: A scheduling and mode selection approach

Ghosha¹,

Roy²,

Kundu³

2022

SKITRJ

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With the recent growth in wireless communication, non-orthogonal multiple access (NOMA) plays an essential role in the 5G network. In the event of natural disasters protecting the life of peoples has been playing an important role around the world. Natural disasters cannot be bypassed, but their effect can be mitigated by a comprehensive network model of the disaster management system. In this paper, an unmanned aerial vehicle (UAV) and ad-hoc NOMA-BS can be applied for serving the ground users in a non-functional area (NFA) or disaster area. Disaster areas can be divided into two, such as wide area and confined areas. However, a UAV can communicate to ground users based on line-of-sight (LoS) and non-line-of-sight (NLoS) in a wide area using a scheduling approach. Also, an ad-hoc NOMA-BS can serve NOMA signal to ground users in a confined area. Furthermore, a mode selection algorithm can be applied for the users to determine whether they are nearer to UAV and ad-hoc NOMA BS or not. The expression for coverage probability and average rate are developed in our proposed UAV-assisted NOMA network. The outage probability, coverage probability, and average rate are investigated for several network parameters. Finally, results are shown via simulation in MATLAB.

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An adaptive Flying Ad-hoc Network (FANET) for disaster response operations to improve quality of service (QoS)

Cited by 23 publications

References 23 publications

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

Optimized Routing of UAVs Using Bio-Inspired Algorithm in FANET: A Systematic Review

Structure management scheme of flying ad-hoc networks (FANET): A dynamic weight values weighted clustering algorithm

When UAV and ad-hoc NOMA-BS meets in Disaster: A scheduling and mode selection approach

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