ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles

Cao, Dun; Jiang, Yuchen; Wang, Jin; Ji, Baofeng; Alfarraj, Osama; Tolba, Amr; Ma, Xiaomin; Liu, Yonghe

doi:10.3390/s20051338

Cited by 20 publications

(13 citation statements)

References 51 publications

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“…In the future research work, we can combine multiple data transmission models with the UAV-fog computing framework, such as using the UAV ad hoc network or cooperative UAVs to transmit data [17,31,35], or we can consider extending the UAV-fog collaborative data collection framework to more application fields, such as electric vehicles and smart grid [36], cooperative fog-based IoT [37], or intelligent transportation [38,39].…”

Section: Figure 10 the Simulation Results In The One Simulatormentioning

confidence: 99%

Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services

Luo

Wang

et al. 2020

IEEE Access

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Unmanned aerial vehicles (UAVs) can provide remote data collection services with quality of service guarantees. The typical application fields include geographic information systems, such as topological survey and natural disasters and hazards monitoring. In the bad geographic environment, wireless communication performance of UAVs cannot be guaranteed. Therefore, the efficiency of remote data collection cannot be guaranteed. This paper proposes a collaborative framework of UAVs and fog computing for remote data collection. Our goal is to maximize the revenue of UAVs with the support of fog computing, so we need to find the optimal computation resources allocation and task assignment scheme. This is a mixed integer nonlinear programming problem. The block coordinate descent method is used to solve this problem, which allows the original problem to be divided into the optimal task assignment subproblem and the optimal computation resource allocation sub-problem. The greedy algorithm, heuristic algorithm and brute force algorithm are proposed to solve the optimal task assignment sub-problem. The convex optimization analysis method is used to solve the optimal resource allocation sub-problem. The genetic algorithm is used as a benchmark to compare with the heuristic-based block coordinate descent algorithm. The numerical simulation and network simulator based-simulation results show that the proposed UAV-Fog collaborative data collection problem can be efficiently solved by the block coordinate descent algorithm based on the heuristic strategy.

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Section: Figure 10 the Simulation Results In The One Simulatormentioning

confidence: 99%

Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services

Luo

Wang

et al. 2020

IEEE Access

Self Cite

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“…In [18], a security framework is proposed to protect UAVs against malicious behavior that targets UAV communication systems. In addition, the challenges of cyber detection methods for the security of UAVs have been investigated.…”

Section: Grayhole Attackmentioning

confidence: 99%

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

Faraji-Biregani¹,

Fotohi

2020

J Supercomput

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Unmanned Aerial Vehicles (UAVs) can be deployed to monitor very large areas without the need for network infrastructure. UAVs communicate with each other during flight and exchange information with each other. However, such communication poses security challenges due to its dynamic topology. To solve these challenges, the proposed method uses two phases to counter malicious UAV attacks. In the first phase, we applied a number of rules and principles to detect malicious UAVs. In this phase, we try to identify and remove malicious UAVs according to the behavior of UAVs in the network in order to prevent sending fake information to the investigating UAVs. In the second phase, a mobile agent based on a three-step negotiation process is used to eliminate malicious UAVs. In this way, we use mobile agents to inform our normal neighbor UAVs so that they do not listen to the data generated by the malicious UAVs. Therefore, the mobile agent of each UAV uses reliable neighbors through a three-step negotiation process so that they do not listen to the traffic generated by the malicious UAVs. The NS-3 simulator was used to demonstrate the efficiency of the SAUAV method. The proposed method is more efficient than CST-UAS, CS-AVN, HVCR, and BSUM-based methods in detection rate, false positive rate, false negative rate, packet delivery rate, and residual energy.

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“…In the curved road scenario which overlays the node selection method, adaptive relay-node selection (ARNS) is used to redefine the optimal position of the node while considering obstacle distribution. The broadcasting characteristics of ARNS are used to classify the road structure [50].…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization

Tariq

Iqbal

Aadil

2020

Wireless Communications and Mobile Computing

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Technology advancement in the field of vehicular ad hoc networks (VANETs) improves smart transportation along with its many other applications. Routing in VANETs is difficult as compared to mobile ad hoc networks (MANETs); topological constraints such as high mobility, node density, and frequent path failure make the VANET routing more challenging. To scale complex routing problems, where static and dynamic routings do not work well, AI-based clustering techniques are introduced. Evolutionary algorithm-based clustering techniques are used to solve such routing problems; moth flame optimization is one of them. In this work, an intelligent moth flame optimization-based clustering (IMOC) for a drone-assisted vehicular network is proposed. This technique is used to provide maximum coverage for the vehicular node with minimum cluster heads (CHs) required for routing. Delivering optimal route by providing end-to-end connectivity with minimum overhead is the core issue addressed in this article. Node density, grid size, and transmission ranges are the performance metrics used for comparative analysis. These parameters were varied during simulations for each algorithm, and the results were recorded. A comparison was done with state-of-the-art clustering algorithms for routing such as Ant Colony Optimization (ACO), Comprehensive Learning Particle Swarm Optimization (CLPSO), and Gray Wolf Optimization (GWO). Experimental outcomes for IMOC consistently outperformed the state-of-the-art techniques for each scenario. A framework is also proposed with the support of a commercial Unmanned Aerial Vehicle (UAV) to improve routing by minimizing path creation overhead in VANETs. UAV support for clustering improved end-to-end connectivity by keeping the routing cost constant for intercluster communication in the same grid.

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ARNS: Adaptive Relay-Node Selection Method for Message Broadcasting in the Internet of Vehicles

Cited by 20 publications

References 51 publications

Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services

Revenue Optimization of a UAV-Fog Collaborative Framework for Remote Data Collection Services

Secure communication between UAVs using a method based on smart agents in unmanned aerial vehicles

IMOC: Optimization Technique for Drone-Assisted VANET (DAV) Based on Moth Flame Optimization

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