TY - JOUR
T1 - Risk-based decision methods for vehicular networks
AU - Shaikh, Riaz Ahmed
AU - Thayananthan, Vijey
N1 - Funding Information:
This article contains the results and findings of a research project that is funded by King Abdulaziz City for Science and Technology (KACST) Grant No. LGP-36-215.
Funding Information:
Funding: This article contains the results and findings of a research project that is funded by King Abdulaziz City for Science and Technology (KACST) Grant No. LGP-36-215.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/6
Y1 - 2019/6
N2 - 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.
AB - 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.
KW - Decision methods
KW - Risk methods
KW - Vehicular networks
UR - http://www.scopus.com/inward/record.url?scp=85069867619&partnerID=8YFLogxK
U2 - 10.3390/electronics8060627
DO - 10.3390/electronics8060627
M3 - Article
AN - SCOPUS:85069867619
VL - 8
JO - Electronics
JF - Electronics
SN - 2079-9292
IS - 6
M1 - 627
ER -
