TY - JOUR
T1 - A Comprehensive Review of Computing Paradigms, Enabling Computation Offloading and Task Execution in Vehicular Networks
AU - Waheed, Abdul
AU - Shah, Munam Ali
AU - Mohsin, Syed Muhammad
AU - Khan, Abid
AU - Maple, Carsten
AU - Aslam, Sheraz
AU - Shamshirband, Shahab
N1 - Funding Information:
This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) through the Academic Center of Excellence in Cyber Security Research-University of Warwick under Grant EP/R007195/1, The Alan Turing Institute under Grant EP/N510129/1, Autotrust under Grant EP/R029563/1, and the National Centre of Excellence for the IoT Systems Cybersecurity, PETRAS under Grant EP/S035362/1.
Publisher Copyright:
© 2013 IEEE.
PY - 2022/1/3
Y1 - 2022/1/3
N2 - Road safety, optimized traffic management, and passenger comfort have always been the primary goals of the vehicle networking research community. Advances in computer and communication technologies have made the dream of modern intelligent vehicles a reality through the use of smart sensors, cameras, networking devices, and storage capabilities. Autonomous operation of modern intelligent vehicles requires massive computations where tasks are outsourced. In recent years, various computing paradigms, e.g., mobile cloud computing (MCC), vehicular cloud computing (VCC), multi-access or mobile edge computing (MEC), vehicular edge computing (VEC), vehicular fog computing (VFC), and volunteer computing based VANET (VCBV), have been developed to move computational resources close to the user and handle the delay-sensitive applications of modern intelligent vehicles. Therefore, in this study, we provide a comprehensive overview of all computing paradigms related to vehicular networks. We also present the architectural details, similarities, differences, and key features of each computing paradigm. Finally, we conclude the study with open research challenges in vehicular networks along with future research directions.
AB - Road safety, optimized traffic management, and passenger comfort have always been the primary goals of the vehicle networking research community. Advances in computer and communication technologies have made the dream of modern intelligent vehicles a reality through the use of smart sensors, cameras, networking devices, and storage capabilities. Autonomous operation of modern intelligent vehicles requires massive computations where tasks are outsourced. In recent years, various computing paradigms, e.g., mobile cloud computing (MCC), vehicular cloud computing (VCC), multi-access or mobile edge computing (MEC), vehicular edge computing (VEC), vehicular fog computing (VFC), and volunteer computing based VANET (VCBV), have been developed to move computational resources close to the user and handle the delay-sensitive applications of modern intelligent vehicles. Therefore, in this study, we provide a comprehensive overview of all computing paradigms related to vehicular networks. We also present the architectural details, similarities, differences, and key features of each computing paradigm. Finally, we conclude the study with open research challenges in vehicular networks along with future research directions.
KW - Multi-access edge computing
KW - Vehicular cloud computing
KW - Vehicular edge computing
KW - Vehicular fog computing
KW - Vehicular network
KW - Volunteer computing based VANET
UR - http://www.scopus.com/inward/record.url?scp=85122597541&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3138219
DO - 10.1109/ACCESS.2021.3138219
M3 - Article
AN - SCOPUS:85122597541
SN - 2169-3536
VL - 10
SP - 3580
EP - 3600
JO - IEEE Access
JF - IEEE Access
ER -