Wireless magnetic sensors for traffic surveillance

Haoui, Amine; Kavaler, Robert; Varaiya, Pravin

doi:10.1016/j.trc.2007.10.004

Cited by 142 publications

(69 citation statements)

References 5 publications

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“…To this extent, we associate to each possible edge, {(i, j, δ)} ∈ E, a normalized weight δ ∈]0 : 1] that models the decrease of the quality of the wireless link with the distance. In our current setup, this weight is calculated based on the Sensys Networks VSN240 sensors 1 model, which are used on roads all around the world and can be deployed densely [24]. These nodes use a nominal output power of 0 dBm and have a receiver sensitivity of −95 dBm in the 2.4 GHz band.…”

Section: B Creating the Connectivity Graphmentioning

confidence: 99%

Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management

Faye

Chaudet

2016

IEEE Trans. Veh. Technol.

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Abstract-In a near future, wireless networks will be one of the key technologies for road traffic management in smart cities. Vehicles and dedicated roadside units should be interconnected through wireless technologies such as IEEE 802.11p (WAVE). Traffic light and road signs may also take their place in this architecture, forming a large-scale network of small devices that report measurements, take orders from a control center, and are able to take decisions autonomously based on their local perception. Such a network shares many similarities with classical wireless sensor and actuator networks, starting with its distributed organization and with the role of the control center. However, its topology, and subsequently the appropriate selection of protocols and algorithms, will be strongly influenced by each city's characteristics. In this article, we characterize and discuss probable topologies of these networks. The aim of this work is to provide network models that can be used to evaluate protocols and algorithms using realistic scenarios in place of generic random graphs. We deploy such networks over 52 city maps extracted from OpenStreetMap and characterize the resulting graphs, with a particular focus on connectivity aspects (degree distribution and connected components). The tools, the complete datasets, OMNeT++ network models are available freely online.

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Section: B Creating the Connectivity Graphmentioning

confidence: 99%

Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management

Faye

Chaudet

2016

IEEE Trans. Veh. Technol.

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“…In many countries across the world, including the US, efforts are being made to estimate vehicle densities and speed distributions for traffic management purposes. This is acheived using a variety of modern equipment like road side sensors, traffic light cameras and remote sensing imagery [37][38][39][40]. More discussion on this assumption and the accuracy of estimates are detailed in the next subsection during the description of the proposed protocol.…”

Section: Vehicular Network Modelmentioning

confidence: 99%

“…These organizations regularly collect information on vehicle volumes, densities, and speeds along road segments to improve congestion control, signalling, accident management, and other traffic attributes. A number of recent studies also propose innovative and accurate approaches to obtain such information including deployment of road side sensors and remote sensing imagery to obtain such information [37][38][39][40]. http://www.journaloftrustmanagement.com/content/1/1/4…”

Section: Protocols Description and Analysismentioning

confidence: 99%

Detecting Sybil attacks in vehicular networks

2014

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A Sybil attack consists of an adversary assuming multiple identities to defeat the trust of an existing reputation system. When Sybil attacks are launched in vehicular networks, the mobility of vehicles increases the difficulty of identifying the malicious vehicle location. In this paper, a novel protocol for Sybil detection in vehicular networks is presented. Considering that vehicular networks are cyber-physical systems, the technique exploits well grounded results in the physical (i.e., transportation) domain to detect the Sybil attacks in the cyber domain. Compared to existing works that rely on additional cyber hardware support, or complex cryptographic primitives for Sybil detection, the protocol leverages the theory of platoon dispersion that models the physics of naturally occurring vehicle dispersion. Specifically, the proposed technique employs a certain number of roadside units that periodically collect reports from vehicles regarding their physical neighborhood. Leveraging from existing models of platoon dispersion, a protocol was designed to detect anomalously close neighborhoods that are reflective of Sybil attacks. To the best of the authors' knowledge, this paper is unique in integrating a well established theory in transportation engineering for detecting cyber space attacks in vehicular networks. The resulting protocol is simple, efficient, and robust in diverse attack environments.

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“…WSN is used for environmental monitoring and decision making as an alert system in hazardous environments [39] as well as for supporting intelligent transportation systems to provide vehicles with information about surrounding traffic environments [40]. In food and agriculture industries, WSNs can be used for environmental monitoring in precision agriculture [41].…”

Section: Comparison Of Ir-uwb With Other Wireless Short-range Protocolsmentioning

confidence: 99%

Review of the State of Art of Tunable Impulse Ultra-Wideband Technology as Integrator for Wireless Sensing and Identifications Short-Range Networks

Jawad¹,

Ismail²,

Hajjawi³

et al. 2014

WSN

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Wireless magnetic sensors for traffic surveillance

Cited by 142 publications

References 5 publications

Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management

Characterizing the Topology of an Urban Wireless Sensor Network for Road Traffic Management

Detecting Sybil attacks in vehicular networks

Review of the State of Art of Tunable Impulse Ultra-Wideband Technology as Integrator for Wireless Sensing and Identifications Short-Range Networks

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