Contextual Risk-based Decision Modeling for Vehicular Networks

Thayananthan, Vijey; Shaikh, Riaz Ahmed

doi:10.5815/ijcnis.2016.09.01

Cited by 11 publications

(17 citation statements)

References 21 publications

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“…[8] Probabilistic model + Bayesian network Supports single safety application scenario [9] Bayes rules + Dempster Shafer theory Supports multiple application scenarios [10] Probabilistic model Supports single safety application scenario [11] Simple average Supports multiple application scenarios [19] Bayesian framework +probabilistic model Supports multiple application scenarios [20] Trajectory-planning algorithm Supports single traffic safety application [21] Probabilistic reasoning based on Bayesian networks Supports single traffic safety application [22] Partially observable markov decision process (POMDP) with Bayesian theory Supports multiple application scenarios [23] Bayesian network and fuzzy features Support for multiple application scenarios [24] Risk mitigation with an algorithm using a weighted average of risk estimates Supports single traffic efficiency application [25] Risk-aware link choice algorithm Supports single traffic efficiency application [26] Probabilistic model and Bayesian network Supports single traffic safety application [27] Probabilistic framework with stochastic function Supports single traffic safety application [28] Probabilistic situation assessment method Supports single traffic safety application [29] Fuzzy controllers and high-precision global positioning system Supports single safety application scenario…”

Section: Reference Methodology Application Domainmentioning

confidence: 99%

“…Vijey and Shaikh [10] have proposed a probability distribution based risk estimation model for VANETs. In this model, risk is based on threat likelihood and impact.…”

Section: Related Workmentioning

confidence: 99%

“…Various risk-based decision systems [7][8][9][10][11] have already been proposed in the literature. The fuzzy risk-based decision model of vehicular networks is one of them.…”

Section: Introductionmentioning

confidence: 99%

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Risk-Based Decision Methods for Vehicular Networks

Shaikh

Thayananthan

2019

Electronics

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Vehicular networks play a key role in building intelligent transport systems for smart cities. For the purpose of achieving traffic efficiency, road safety, and traveler comfort, vehicles communicate and collaborate with each other as well as with the fixed infrastructure. In practice, not all vehicles are trustworthy. A faulty or malicious vehicle may forward or share inaccurate or bogus information, which may cause adverse things, such as, road accidents and traffic congestion. Therefore, it is very important to evaluate risk before a vehicle takes any decision. Various risk-based decision systems have already been proposed in the literature. The fuzzy risk-based decision model of vehicular networks is one of them. In this paper, we have proposed various extensions in the fuzzy risk-based decision model to achieve higher robustness, reliability, and completeness. We have presented the theoretical and simulation-based analysis and evaluation of the proposed scheme in a comprehensive manner. In addition, we have analytically cross verified the theoretical and simulation-based results. Qualitative comparison of the proposed scheme has also been presented in this work.

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Section: Reference Methodology Application Domainmentioning

confidence: 99%

“…Vijey and Shaikh [10] have proposed a probability distribution based risk estimation model for VANETs. In this model, risk is based on threat likelihood and impact.…”

Section: Related Workmentioning

confidence: 99%

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Risk-Based Decision Methods for Vehicular Networks

Shaikh

Thayananthan

2019

Electronics

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“…Security, safety, and privacy are the parts of the business solutions in transportation service management as in Fig. 3 [40][41][42][43][44][45][46][47][48][49][50][51][52]. The performance of security issues depends on emerging technologies such as photonic technology based on 5G and beyond.…”

Section: Scenario 2 (Security Safety and Privacy)mentioning

confidence: 99%

A Theoretical Model and Business Solutions for Improving 5G+ Transportation Services

Thayananthan¹,

Algarni²

2020

IJCA

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Business models for improving the transportation system, which includes the services of transport facilities, predict the future of users' requirements according to the emerging technologies. All predictions raise the number of questions that support to increase business challenges such as management of 5G transportation services. Management issues cover technical challenges considered to improve the customer relationship and cost for utilizing the service mentioned in the 5G-transportation system. During the traveling or driving time, driverless vehicles face many challenges managed through the services without proper management. Security and energy management are examples of current problems. These problems involve the technical challenges of 5G, and other emerging technologies considered for developing the business model in this paper. As an appropriate method, an efficient model of 5G transportation is introduced as a business model for analyzing the challenges mentioned above. In this model, few challenges need more discussions and analysis because users' and customers' requirements are evolving with the future emerging technologies. Further, this model will encourage the users, including service providers, to make necessary decisions for enhancing the management facilities. General TermsIn this paper, the theoretical model of the transportation service as a general term is considered. Throughout this research, transportation issues based on 5G are considered to improve the 5G solutions of transportation facilities

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“…These specific improvements depend on vehicle contexts, drivers' attitude, etc. reduce unnecessary accidents [31,32].…”

Section: Fig 3: Transportation Facilities With Iot Based 5g Networkmentioning

confidence: 99%

Internet of Things based 5G Infrastructure for Securing Transportation Facilities in Smart Cities

Thayananthan¹,

Algarni²

2019

IJCA

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The fifth generation (5G) is going to dominate in the smart cities where 5G provides all the facilities with maximum security and safety. Regarding transportation services, unnecessary accidents rates are increasing with cyber attacks and threats. All security issues for 5G based infrastructure will be facing many challenges such as secure transportation services which is one of the 2030 initiatives in many countries. Despite many security solutions, developing energy-efficient cryptographic algorithms are recommended to secure the future transport systems which not only improve the security but also reduce the cost. The main aim of this strategic research is to develop a secure transportation system using efficient security solutions which not only reduce the exorbitant accident rates but also increase safety system that enhances the livability of smart cities. Employing secure multi-level IoT and 5G based infrastructure used within the transportation systems will be an efficient method. In this system, appropriate applied cryptographic algorithms will be employed to improve transportation services. According to the research idea of this project, the proposed model of the future transportation system will be delivered with better security solutions. In expected results, the dynamic security solutions will be considered. They are vital requirements to minimize accidents and secure smart cities. This research will be leading us to implement an effective security solution for future transport systems. Therefore, each passenger who is driving or using driverless vehicles will be protected from the evolving attacks within the smart cities. General TermsIn this paper, the security of the transport service as a general term is considered. Throughout this research, security issues of IoT based 5G are considered to improve the security solutions of transportation facilities

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Contextual Risk-based Decision Modeling for Vehicular Networks

Cited by 11 publications

References 21 publications

Risk-Based Decision Methods for Vehicular Networks

Risk-Based Decision Methods for Vehicular Networks

A Theoretical Model and Business Solutions for Improving 5G+ Transportation Services

Internet of Things based 5G Infrastructure for Securing Transportation Facilities in Smart Cities

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