Dynamic positioning of UAVs to improve network coverage in VANETs

Islam, Md. Mahmudul; Khan, Muhammad Toaha Raza; Saad, Malik Muhammad; Tariq, Muhammad Ashar; Kim, Dongkyun

doi:10.1016/j.vehcom.2022.100498

Cited by 15 publications

(8 citation statements)

References 35 publications

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“…Developed in [35] is a method to improve the packet delivery ratio and throughput of unmanned aerial vehicles (UAVs). Here, the particle swarm optimization algorithm is utilized for finding the optimal deployment of UAVs, which is based on vehicle density, heading direction, and previous coverage information.…”

Section: Optimization-associated Researchmentioning

confidence: 99%

Privacy-Preserving Mobility Model and Optimization-Based Advanced Cluster Head Selection (P2O-ACH) for Vehicular Ad Hoc Networks

Abdulsattar¹,

Mohammed²,

Alkhayyat

et al. 2022

Electronics

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In vehicular ad hoc networks (VANETs), due to the fast-moving mobile nodes, the topology changes frequently. This dynamically changing topology produces congestion and instability. To overcome this issue, privacy-preserving optimization-based cluster head selection (P2O-ACH) is proposed. One of the major drawbacks analyzed in the earlier cluster-based VANETs is that it creates a maximum number of clusters for communication that leads to an increase in energy consumption which reflects in a degradation of the performance. In this paper, enhanced rider optimization algorithm (ROA)-based CH selection is performed and that optimally selects the CH so that effective clusters are created. By analyzing this, the behavior of the bypass rider’s CH is chosen, and this forms the optimized clusters, and during the process of transmission, privacy-preserving mobility patterns are used to secure the network from all kinds of malfunctions which are performed by the new vehicle blending and migration process. The proposed P2O-ACH is simulated using NS-2, and for performance analysis, two scenarios are taken, which contain a varying number of vehicles and varying speeds. For a varying number of vehicles and speeds, the considered parameters are energy efficiency, energy consumption, network lifetime, packet delivery ratio, packet loss, network latency, network throughput, and routing overhead. From the results, it is understood that the proposed method performed better when compared with earlier work, such as GWO-CH, ACO-SCRS, and QMM-VANET.

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Section: Optimization-associated Researchmentioning

confidence: 99%

Privacy-Preserving Mobility Model and Optimization-Based Advanced Cluster Head Selection (P2O-ACH) for Vehicular Ad Hoc Networks

Abdulsattar¹,

Mohammed²,

Alkhayyat

et al. 2022

Electronics

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“…One of the difficulties concern infrastructure deployment, where UAVs can act as flying RSUs, relaying data to vehicles outside the RSUs’ coverage range. In this context, a collaborative network coverage enhancement scheme was proposed by Islam et al [ 78 ] to bring these uncovered vehicles within the infrastructure’s coverage. The PSO algorithm was used to determine the best positions to deploy the UAVs, taking into account factors such as vehicle density, heading direction, and previous coverage information.…”

Section: Optimization Problems In Vehicular Networkmentioning

confidence: 99%

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

Peyman

Fluechter

Panadero

et al. 2023

Sensors

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Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an `Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.

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“…Localization WL. To assist the vehicle in determining its location, the authors of [16] offer two novel positioning strategies: a biased position (WL) using the signal-to-noise ratio (SNR) obtained from the exchange messages and a weighted position next to the vehicle (WLD) using the distance SNR. Their approach, which weights each surrounding vehicle coordinate built on SNR values and distances, develops the idea of LC centroid localization.…”

Section: Evaluation Of Centroid Localization CL and Weightedmentioning

confidence: 99%

Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

et al. 2023

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Vehicular ad hoc networks (VANETs) are an eminent area of intelligent transportation systems (ITS) which includes vehicle tracking, positioning, and emergency warnings. For protective applications, vehicle localization in the urban area is a significant problem. Global Positioning Systems (GPS) are one of the many solutions that have been offered, although they do not offer accuracy. To locate a target vehicle accurately, a unique approach called received signal strength- (RSS-) based localization scheme has been proposed. By detecting signals within its range, it establishes communication through roadside units (RSUs) and determines the typical RSS. Following the discovery of the RSS, the RSS-based localization algorithm helped in determining the precise position of the vehicle. The high signal-to-noise ratio of the proposed algorithm, which is derived from neighbouring RSUs, is its key component. The vehicle’s Cramer Rao lower bound (CRLB) is examined after its position is discovered. Various experimentations considering dynamic vehicles ranging from 2 to 100, localization error, ranging error, and throughput have been conducted which demonstrates that the proposed algorithm (RSS) has better results (87%, 80%, and 120) than other well-established least squares (LS) and weighted least squares methods (WLS).

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Dynamic positioning of UAVs to improve network coverage in VANETs

Cited by 15 publications

References 35 publications

Privacy-Preserving Mobility Model and Optimization-Based Advanced Cluster Head Selection (P2O-ACH) for Vehicular Ad Hoc Networks

Privacy-Preserving Mobility Model and Optimization-Based Advanced Cluster Head Selection (P2O-ACH) for Vehicular Ad Hoc Networks

Optimization of Vehicular Networks in Smart Cities: From Agile Optimization to Learnheuristics and Simheuristics

Received Signal Strength‐Based Localization for Vehicle Distance Estimation in Vehicular Ad Hoc Networks (VANETs)

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