ICRA: An Intelligent Clustering Routing Approach for UAV Ad Hoc Networks

Guo, Jianqiu; Gao, Huamin; Liu, Zhiquan; Huang, Feiran; Zhang, Junwei; Li, Xinghua; Ma, Jianfeng

doi:10.1109/tits.2022.3145857

Cited by 53 publications

(18 citation statements)

References 32 publications

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“…This is especially important for mass-connected IoT terminal devices, which are often in remote areas and have extremely high energy consumption requirements, so UAVs' flexible mobility makes them an important means of assisting in IoT network access. A crucial means of increasing capacity [ 18 , 19 ]. However, the contradiction between the huge number of ground terminal users and limited wireless network access resources and UAV range time makes how to reduce access network congestion and overload, improve access resource utilization and terminal device access success rate, increase access efficiency, and realize energy-efficient network connection establishment based on UAVs an important problem that needs to be solved urgently and has attracted much discussion and attention in academia and industry.…”

Section: Related Workmentioning

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: Related Workmentioning

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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“…But the algorithm does not take into account the flight trajectories of UAVs. In [ 29 ], an intelligent clustering routing approach (ICRA) algorithm was proposed which takes four factors into account to calculate the utility of a UAV, including the residual energy of the UAV, the degree centrality of the UAV, the velocity similarity, and the link holding time. The reinforcement learning was utilized to help clustering strategy adjustment module learn the benefits brought by adopting different strategies to calculate the UAVs utility in a specific network state and determine the optimal clustering strategy accordingly.…”

Section: Related Workmentioning

confidence: 99%

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

et al. 2023

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At present, the applications of multiple unmanned aerial vehicles (UAVs) are becoming more and more widespread, covering many civil and military fields. When performing tasks, UAVs will form a flying ad hoc network (FANET) to communicate to each other. However, subject to high mobility, dynamic topology, and limited energy of FANETs, maintaining stable communication performance is a challenging task. As a potential solution, the clustering routing algorithm divides the entire network into multiple clusters to achieve strong network performance. Meanwhile, the accurate localization of UAV is also strongly required when FANETs are applied in the indoor scenario. In this paper, we propose a firefly swarm intelligence based cooperative localization (FSICL) and automatic clustering (FSIAC) for FANETs. Firstly, we combine the firefly algorithm (FA) and Chan algorithm to better cooperative locate the UAVs. Secondly, we propose the fitness function consisting of link survival probability, node degree-difference, average distance, and residual energy, and take it as the light intensity of the firefly. Thirdly, the FA is put forward for cluster-head (CH) selection and cluster formation. Simulation results indicate that the proposed FSICL algorithm achieves the higher localization accuracy faster, and the FSIAC algorithm achieves the higher stability of clusters, longer link expiration time (LET), and longer node lifetime, all of which improve the communication performance for indoor FANETs.

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“…In 2022, Guo, J., et al [31] proposed the intelligent cluster routing technique for UANETs. This module consists of a clustering component, a clustering adjustment component, and a routing component.…”

Section: Literature Reviewmentioning

confidence: 99%

R2NET: Storage and Analysis of Attack Behavior Patterns

2023

KSII TIIS

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Cloud computing has evolved significantly, intending to provide users with fast, dependable, and low-cost services. With its development, malicious users have become increasingly capable of attacking both its internal and external security. To ensure the security of cloud services, encryption, authorization, firewalls, and intrusion detection systems have been employed. However, these single monitoring agents, are complex, time-consuming, and they do not detect ransomware and zero-day vulnerabilities on their own. An innovative Record and Replay-based hybrid Honeynet (R2NET) system has been developed to address this issue. Combining honeynet with Record and Replay (RR) technology, the system allows fine-grained analysis by delaying time-consuming analysis to the replay step. In addition, a machine learning algorithm is utilized to cluster the logs of attackers and store them in a database. So, the accessing time for analyzing the attack may be reduced which in turn increases the efficiency of the proposed framework. The R2NET framework is compared with existing methods such as EEHH net, HoneyDoc, Honeynet system, and AHDS. The proposed system achieves 7.60%, 9.78%%, 18.47%, and 31.52% more accuracy than EEHH net, HoneyDoc, Honeynet system, and AHDS methods.

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ICRA: An Intelligent Clustering Routing Approach for UAV Ad Hoc Networks

Cited by 53 publications

References 32 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

Firefly swarm intelligence based cooperative localization and automatic clustering for indoor FANETs

R2NET: Storage and Analysis of Attack Behavior Patterns

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