Relaying Data With Joint Optimization of Energy and Delay in Cluster-Based UAV-Assisted VANETs

Mokhtari, Somayeh; Nouri, Nima; Abouei, Jamshid; Avokh, Avid; Plataniotis, Konstantinos N.

doi:10.1109/jiot.2022.3188563

Cited by 20 publications

(4 citation statements)

References 45 publications

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“…The evaluation proceeds through two scenarios: the first scenario is according to the variation of the calculated performance speed of various attacks, such as flooding attacks, insider attacks, wormhole attacks, and position falsification attacks, of the proposed BDTC-SIOVTs. In the second scenario, the considered parameters of the proposed BDTC-SIOVTs are compared with earlier works such as LAKA-IOD [16], NCAS-IOD [17], and TPDA-IOV [18].…”

Section: Resultsmentioning

confidence: 99%

“…This method reduces the network delay but fails to improve the efficiency, throughput, and delivery ratio. The authors of [18] developed a novel approach to reduce the energy consumption and delay called Non-Orthogonal Multiple Access (NOMA), which is mainly utilized for the process of bandwidth allocation. The additional methods which are used to improve the clustering-based UAVs network are K-means clustering and truncated Gaussian distribution.…”

Section: Related Workmentioning

confidence: 99%

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Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)

Jghef¹,

Jasim

Ghanimi

et al. 2022

Drones

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The Internet of Drone Things (IoDT) is a trending research area where drones are used to gather information from ground networks. In order to overcome the drawbacks of the Internet of Vehicles (IoV), such as congestion issues, security issues, and energy consumption, drones were introduced into the IoV, which is termed drone-assisted IoV. Due to the unique characteristics of the IoV, such as dynamic mobility and unsystematic traffic patterns, the performance of the network is reduced in terms of delay, energy consumption, and overhead. Additionally, there is the possibility of the existence of various attackers that disturb the traffic pattern. In order to overcome this drawback, the drone-assisted IoV was developed. In this paper, the bio-inspired dynamic trust and congestion-aware zone-based secured Internet of Drone Things (BDTC-SIoDT) is developed, and it is mainly divided into three sections. These sections are dynamic trust estimation, congestion-aware community construction, and hybrid optimization. Initially, through the dynamic trust estimation process, triple-layer trust establishment is performed, which helps to protect the network from all kinds of threats. Secondly, a congestion-aware community is created to predict congestion and to avoid it. Finally, hybrid optimization is performed with the combination of ant colony optimization (ACO) and gray wolf optimization (GWO). Through this hybrid optimization technique, overhead occurs during the initial stage of transmission, and the time taken by vehicles to leave and join the cluster is reduced. The experimentation is performed using various threats, such as flooding attack, insider attack, wormhole attack, and position falsification attack. To analyze the performance, the parameters that are considered are energy efficiency, packet delivery ratio, routing overhead, end-to-end delay, packet loss, and throughput. The outcome of the proposed BDTC-SIoDT is compared with earlier research works, such as LAKA-IOD, NCAS-IOD, and TPDA-IOV. The proposed BDTC-SIoDT achieves high performance when compared with earlier research works.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)

Jghef¹,

Jasim

Ghanimi

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

“…Therefore, it enables effective and efficient temporary communication during emergencies when terrestrial infrastructures are damaged or unavailable. Owing to the distinctive traits of the IoD paradigm, vehicle-to-vehicle communication, a type of device-to-device communication [15], can be enhanced by deploying a swarm of unmanned aerial vehicles (UAVs) as a relay network to establish reliable wireless communication between vehicles in VANETs [16].…”

Section: Introductionmentioning

confidence: 99%

3D Simulation Model for IoD-to-Vehicles Communication in IoD-assisted VANET

Ahmed

Sheltami

Mahmoud

et al. 2023

Preprint

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Vehicle ad-hoc networks (VANETs) have gradually emerged to enhance transportation information, entertainment, safety, and other services. However, such infrastructures have certain limitations, causing intermittent network disconnection. Further, in urban areas, terrain heights act as obstacles and hinder or attenuate transmitted signals. In this study, we propose a dynamic 3D internet of drones (IoD) collaborative communication approach for efficient VANET-assistance (3DIoDAV) by integrating the IoD network and VANET to support terrestrial communication. We model IoD locations as an optimization problem to optimize the IoD nodes in three-dimensional terrain. Improved particle swarm optimization is used to optimally deploy IoD nodes in 3D terrain for minimizing the number of isolated vehicles. The proposed approach considers the terrain profile influence on communication. Therefore, we propose a 3D propagation model for efficient IoD-to-vehicle (IoD2V) communication in 3D space. Experiments are performed based on the received signal from ground vehicles to examine the performance of the proposed model and the 3DIoDAV approach. Simulation results show different behaviors of IoD nodes in two-dimensional (2D) and 3D scenarios. Comparison with 2D VANET-assisted and IoDAV approaches demonstrates the proposed 3DIoDAV approach’s ability to detect terrain obstacles, which guarantees the dispatching of IoD nodes into the most appropriate locations in 3D space, thereby minimizing the impact of terrain obstacles on communication.

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“…Therefore, it enables effective and efficient temporary communication during emergencies when terrestrial infrastructures are damaged or unavailable. Owing to the distinctive traits of the IoD paradigm, vehicle-to-vehicle communication, a type of device-to-device communication (Khoshafa et al, 2020), can be enhanced by deploying a swarm of unmanned aerial vehicles (UAVs) as a relay network to establish reliable wireless communication between vehicles in VANETs Mokhtari et al (2022).…”

Section: Introductionmentioning

confidence: 99%

3D simulation model for IoD-to-vehicles communication in IoD-assisted VANET

Ahmed,

Sheltami,

Mahmoud

et al. 2023

Front. Built Environ.

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Vehicle ad hoc networks (VANETs) have gradually emerged to enhance transportation information, entertainment, safety, and other services. However, such infrastructures have certain limitations, causing intermittent network disconnection. Further, in urban areas, terrain heights act as obstacles and hinder or attenuate transmitted signals. In this study, we propose a dynamic 3D internet of drones collaborative communication approach for efficient VANET-assistance (3DIoDAV) by integrating the IoD network and VANET to support terrestrial communication. We model IoD locations as an optimization problem to optimize the IoD nodes in three-dimensional terrain. Improved particle swarm optimization is used to optimally deploy IoD nodes in 3D terrain for minimizing the number of isolated vehicles. The proposed approach considers the terrain profile influence on communication. Therefore, we propose a 3D propagation model for efficient IoD-to-vehicle (IoD2V) communication in 3D space. Experiments are performed based on the received signal from ground vehicles to examine the performance of the proposed model and the 3DIoDAV approach. Simulation results show different behaviors of IoD nodes in two-dimensional (2D) and 3D scenarios. Comparison with 2D VANET-assisted and IoDAV approaches demonstrates the proposed 3DIoDAV approach’s ability to detect terrain obstacles, which guarantees the dispatching of IoD nodes into the most appropriate locations in 3D space, thereby minimizing the impact of terrain obstacles on communication.

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Relaying Data With Joint Optimization of Energy and Delay in Cluster-Based UAV-Assisted VANETs

Cited by 20 publications

References 45 publications

Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)

Bio-Inspired Dynamic Trust and Congestion-Aware Zone-Based Secured Internet of Drone Things (SIoDT)

3D Simulation Model for IoD-to-Vehicles Communication in IoD-assisted VANET

3D simulation model for IoD-to-vehicles communication in IoD-assisted VANET

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