Cloud-assisted GPS-driven dynamic spectrum access in cognitive radio vehicular networks for transportation cyber physical systems

Rawat, Danda B.; Reddy, Swetha; Sharma, Nimish; Bista, Bhed Bahadur; Shetty, Sachin

doi:10.1109/wcnc.2015.7127765

Cited by 24 publications

(13 citation statements)

References 14 publications

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“…11 Mbps in 802.11a, 27 Mbps in 802.11p DSRC, 54 Mbps 802.11g, etc.) can be computed as [11] where p p is the probability of a channel being absent in the spectrum database (or a PU being present in the given channel). SU selects the best channel among available ones that offers highest data rate and satisfies its minimum data rate (R T ) requirements.…”

Section: Numerical Resultsmentioning

confidence: 99%

Resource Allocation for Cognitive Radio Enabled Vehicular Network Users

Rawat

Song

Shetty

2015

Dynamic Spectrum Access for Wireless Networks

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Vehicular communication networks are expected to provide safety and comfort services to passengers and drivers. To support diverse set of services and applications, vehicular network is expected to used variety of wireless access technologies for Vehicle-to-Vehicle (V2V) and Vehicle-to-roadside (V2R) communications. V2R communication could introduce high delay as roadside communication unit relays the information from source vehicle to destinations vehicles. Thus, for time critical messages, V2R based communication is not suitable to notify drivers in a timely manner. V2V based communication in VANET is performed in a peer-to-peer basis and the intended vehicles could exchange their information directly using single hop or multi-hop communications. In this case, performance of VANET depends on connectivity among vehicles since reliable connectivity for single hop or multi-hop communication is very important to forward time-critical information. The connectivity in VANET is directly related to density of vehicles, relative speed of the vehicles, association time of wireless technology, and transmission range and frequency bands used by vehicles. Time duration .T D / for association (sensing/searching and connection setup) and information exchange depends on transmission range .R/ and relative speed of vehicles .V r / as

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Section: Numerical Resultsmentioning

confidence: 99%

Resource Allocation for Cognitive Radio Enabled Vehicular Network Users

Rawat

Song

Shetty

2015

Dynamic Spectrum Access for Wireless Networks

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“…Extensive research is being done in distributed architectures for specific domains. For example, recent proposals can be found for robotics [18], industrial [19], smart driving [20], e-health [21] or smart cities [22][23][24] among many others.…”

Section: Distributed Systemsmentioning

confidence: 99%

Distributed computational model for shared processing on Cyber-Physical System environments

Mora

Colom

Gil

et al. 2017

Computer Communications

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This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  A computation model to distribute the application workload over the CPS is proposed.  The scheduling method dynamically shares the tasks among the computing nodes of the CPS.  This approach facilitates the networking and integration of heterogeneous computer devices.

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“…The Internet of Things is adding advantages into existing areas such as smart home, smart irrigation, smart agriculture, and smart logistics (see Figure 1 10–12 ). The Internet of Vehicles is one of the revolutionary approaches introduced by the Internet of Things 13 . The Internet of Vehicles is evolving from the vehicular ad hoc networks to achieve the important goal of transforming technology from smart phones into smart cars 14 .…”

Section: Introductionmentioning

confidence: 99%

Enhancing the security and performance of nodes in Internet of Vehicles

Vijayarangam¹,

Babu

Murugan³

et al. 2018

Concurrency and Computation

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Summary Internet of Vehicles is recorded as an ever growing area for connected vehicles to exchange their information with other vehicles using vehicle‐to‐vehicle communication. Vehicle‐to‐vehicle communication is possible by forming vehicular ad hoc networks, with the help of roadside units using vehicle‐to‐roadside communications in the network. Internet of networks has many advantages such as road safety, traffic management, and sharing information on daily traffic and updating traffic information. For example, the Internet of Vehicles can be used to reduce traffic and deaths occurring due to road accidents, to reduce the fuel needed, and to reduce travel time. Interconnected vehicles rapidly learn about road conditions and traffic and respond to the driver; thus, necessary actions are taken. However, the attacker can modify the information in the connected vehicles and, thus, creates problem on the road. Data fascination is one of the main attacks on connected vehicles where connected vehicles take action based on information from the other vehicle. In this paper, first, a model has been proposed to detect the data fascination attack using the hashing technique to enhance the security in the interconnected vehicles by adjusting the size of the contention window to transfer original information to other interconnected vehicles at the correct time. Second, a model has been suggested to reduce the travel time in case of traffic congestion. The efficiency of the proposed model is checked using numerical methods obtained from the simulation results. From the obtained results, the proposed approach prevents data fascination attacks in interconnected vehicles and provides high throughput with lower delay.

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Cloud-assisted GPS-driven dynamic spectrum access in cognitive radio vehicular networks for transportation cyber physical systems

Cited by 24 publications

References 14 publications

Resource Allocation for Cognitive Radio Enabled Vehicular Network Users

Resource Allocation for Cognitive Radio Enabled Vehicular Network Users

Distributed computational model for shared processing on Cyber-Physical System environments

Enhancing the security and performance of nodes in Internet of Vehicles

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