Data Collection from Smart-City Sensors through Large-Scale Urban Vehicular Networks

Khan, Muhammad Awais; Sargento, Susana; Luís, Miguel

doi:10.1109/vtcfall.2017.8288308

Cited by 11 publications

(8 citation statements)

References 16 publications

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“…Vehicles as a carrier of sensors for sensing the urban environment is a common solution. To accomplish the data collection from sensors deployed in different locations in urban, data forwarding schema based on the probability of a vehicle to reach a road side unit was proposed in the literature [15]; a distributed age-aware data collection scheme was proposed based on Lyapunov optimization technique in the literature [16], including a sampling method with a threshold at the source vehicle and a data forwarding strategy based on learning. For the traffic data collection in urban scene, P. Salvo et al proposed a traffic data collection method based on LTE and V2V [17], and the way of data collection can switch between LTE and V2V according to the vehicle density.…”

Section: Related Workmentioning

confidence: 99%

UAV-Assisted Sensor Data Dissemination in mmWave Vehicular Networks Based on Network Coding

Huang

et al. 2022

Wireless Communications and Mobile Computing

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Due to good maneuverability, UAVs and vehicles are often used for environment perception in smart cities. In order to improve the efficiency of sensor data sharing in UAV-assisted mmWave vehicular network (VN), this paper proposes a sensor data sharing method based on blockage effect identification and network coding. The concurrent sending vehicles selection method is proposed based on the availability of mmWave link, the number of target vehicles of sensor data packet, the distance between a sensor data packet and target vehicle, the number of concurrent sending vehicles, and the waiting time of sensor data packet. The construction method of the coded packet is put forward based on the status information about the existing packets of vehicles. Simulation results demonstrated that efficiency of the proposed method is superior to baseline solutions in terms of the packet loss ratio, transmission time, and packet dissemination ratio.

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Section: Related Workmentioning

confidence: 99%

UAV-Assisted Sensor Data Dissemination in mmWave Vehicular Networks Based on Network Coding

Huang

et al. 2022

Wireless Communications and Mobile Computing

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“…In [33], Touil et al propose a data collection scheme based on a clustering approach; the objective is to reduce the impact of mobility and density on the data collection stations. Khan et al [34] propose a data forwarding algorithm for data collection; it is based on a ranking scheme of On-Board Units (OBUs) and the hop count of data traffic. The authors in [35] propose a Quality-oriented Data Collection (QDC) to provide high quality data for vehicular application and services.…”

Section: ) Data Collectionmentioning

confidence: 99%

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Lamssaggad¹,

Benamar²,

Hafid³

et al. 2021

IEEE Access

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With the proliferation of embedded technologies and wireless capabilities, today's vehicles are no longer isolated mechanical machines. They become part of a hyper-connected system-Intelligent Transportation Systems (ITS)-that has the potential to support multiple levels of autonomy and intelligence improving considerably the safety, efficiency, and sustainability of transportation networks. However, this raises new security issues that make the whole system prone to cybersecurity attacks that threaten both the safety and privacy of all road-users. This paper gives a short background tutorial on the main security issues and the different attacks that hinder Intelligent Transport Systems. To enable secure and safe ITS applications, this paper provides a comprehensive analysis of existing solutions and highlights their strengths and limitations. Finally, this survey presents key challenges in the field, and discusses recent trends that must be factored in by researchers, implementers, and car manufactures to improve the security of ITS.

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“…The trend of varying distance is captured with the rolling (moving) average and standard deviation (SD), which produces a pair of tensors. With recursion (19) and (20), the moving average (μ (t) i,j ) and SD (σ…”

Section: A Design and Complexity Analysis Of Ranking Algorithm Of Nodesmentioning

confidence: 99%

Robust, Resilient and Reliable Architecture for V2X Communications

Khan

Ghosh

Busari

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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The new developments in mobile edge computing (MEC) and vehicle-to-everything (V2X) communications has positioned 5G and beyond in a strong position to answer the market need towards future emerging intelligent transportation systems and smart city applications. The major attractive features of V2X communication is the inherent ability to adapt to any type of network, device, or data, and to ensure robustness, resilience and reliability of the network, which is challenging to realize. In this work, we propose to drive these further these features by proposing a novel robust, resilient and reliable architecture for V2X communication based on harnessing MEC and blockchain technology. A three stage computing service is proposed. Firstly, a hierarchcial computing architecture is deployed spanning over the vehicular network that constitutes cloud computing (CC), edge computing (EC), fog computing (FC) nodes. The resources and data bases can migrate from the high capacity cloud services (furthest away from the individual node of the network) to the edge (medium) and low level fog node, according to computing service requirements. Secondly, the resource allocation filters the data according to its significance, and rank the nodes according to their usability, and selects the network technology according to their physical channel characteristics. Thirdly, we propose a blockchain-based transaction service that ensures reliability. We discussed two use cases for experimental analysis, plugin electric vehicles in smart grid scenarios, and massive IoT data services for autonomous cars. The results show that car connectivity prediction is accurate 98% of the times, where 92% more data blocks are added using micro-blockchain solution compared to the public blockchain, where it is able to reduce the time to sign and compute the proof-of-work (PoW), and deliver a low-overhead Proof-of-Stake (PoS) consensus mechanism. This approach can be considered a strong candidate architecture for future V2X, and with more general application for everythingto-everything (X2X) communications.

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Data Collection from Smart-City Sensors through Large-Scale Urban Vehicular Networks

Cited by 11 publications

References 16 publications

UAV-Assisted Sensor Data Dissemination in mmWave Vehicular Networks Based on Network Coding

UAV-Assisted Sensor Data Dissemination in mmWave Vehicular Networks Based on Network Coding

A Survey on the Current Security Landscape of Intelligent Transportation Systems

Robust, Resilient and Reliable Architecture for V2X Communications

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