Mobile Edge Computing is the current paradigm in transportation systems (MEC). In order to demonstrate certain significant paradigm capabilities to visit nearby destination sites, this computing approach is simulated. With the use of network apps and services, this strategy exchanges information with the least amount of delay possible while displaying real-time capabilities that are immediately available. Researchers have developed an intelligent framework that makes use of cutting-edge applications for deploying strategies, constructing architecture, and creating communication methodologies by merging the efficient transport system with MEC. In order to satisfy this need, this chapter covers the several traditional ways for integrating MEC in vehicle networks. This criterion suggests that an intelligent transportation system should be developed in the context of future smart cities. In order to provide the best performance, security is a major concern in MEC-based automotive systems. Security precautions are managed via a Cyber-Physical Transportation System (CPTS), which combines a large number of sensors and wireless mobile devices. Sensing, communications, and traffic control are all things it is capable of. Maintaining the heterogeneous nature of variables as traffic sensors in Vehicular Ad Hoc Networks (VANET) while taking into account a diversity of abilities necessitates the use of the CPTS of MEC technique. Furthermore, the connected cars are used in the real-time application execution and application with respect to the edge node as the network edge for computational reasons. Due to its secure service deployment for autonomous vehicles, the Internet of Things (IoT), and Internet of vehicles, researchers use MEC for a variety of applications. The edge of networks have been deployed using MEC because to its properties and without the use of terminal servers. However, only a few research studies have been systematically used for MEC deployment. Secure service design settings with MEC are also somewhat uncommon. Therefore, using intelligent ways to analyse numerous secured MC research is necessary. Here, we cover some of the challenging and unresolved issues surrounding the secure design of mobile services in edge computing. The first problem is a significant security restriction on secure access control systems. Second, as information and new services continue to proliferate and develop online, security difficulties with data transfer have arisen. These challenges include high traffic volumes, scalability issues, and other issues. Thirdly, the widespread use of in-car MEC could lead to improper exploitation of vehicle position data. Additionally, a subsequent study will investigate the development of MEC in more challenging contexts and use the acquired information in a variety of application areas.Additionally, in a vehicular edge-based environment, it is necessary to supply a wide range of comprehensive middleware solutions to support a variety of classes of communications between the cloud server and the sensing layer.Last but not least, a number of unresolved concerns are not included in the current studies that examine potential future study directions.

• Mobile Edge Computing (MEC),
• Vehicular Ad Hoc Networks (VANETs),
• Vehicular Cloud Computing (VCC),
• Edge Computing Vehicles (ECVs),
• Edge Cloud Computing (ECC),
• Vehicles-to-Everything (V2X) communication system,
• Edge Content Delivery and Update (ECDU)

[1] J. Cui, L. Wei, H. Zhong, J. Zhang, Y. Xu and L. Liu, "Edge Computing in VANETs-An Efficient and Privacy-Preserving Cooperative Downloading Scheme," in IEEE Journal on Selected Areas in Communications, Vol. 38, No. 6, pp. 1191-1204, 2020.

[2] Alshimaa H. Ismail, Nirmeen A. El-Bahnasawy and Hesham F. A. Hamed, "AGCM: Active Queue Management-Based Green Cloud Model for Mobile Edge Computing", Wireless Personal Communication, Vol. 105, pp. 765–785, 2019.

[3] Thavavel Vaiyapuri, Velmurugan Subbiah Parvathy, V. Manikandan, N. Krishnaraj, Deepak Gupta and K. Shankar, "A Novel Hybrid Optimization for Cluster‐Based Routing Protocol in Information-Centric Wireless Sensor Networks for IoT Based Mobile Edge Computing", Wireless Personal Communications, 2021.

[4] G. Li, X. Li, Q. Sun, L. Boukhatem and J. Wu, "An Effective MEC Sustained Charging Data Transmission Algorithm in VANET-Based Smart Grids," in IEEE Access, vol. 8, pp. 101946-101962, 2020.

[5] Jafar Al-Badarneh, Yaser Jararweh, Mahmoud Al-Ayyoub, Ramon Fontes Mohammad Al-Smadia and Christian Rothenberg, "Cooperative mobile edge computing system for VANET-based software-defined content delivery", Computers & Electrical Engineering, Vol. 71, pp. 388-397, 2018.

[6] Naserali Noorani and Seyed Amin Hosseini Seno, "SDN- and fog computing-based switchable routing using path stability estimation for vehicular ad hoc networks", Peer-to-Peer Networking and Applications, Vol. 13, pp. 948-964, 2020.

[7] Xiaoge Huang, Ke Xu, Chenbin Lai, Qianbin Chen and Jie Zhang, "Energy-efficient offloading decision-making for mobile edge computing in vehicular networks", EURASIP Journal on Wireless Communications and Networking, No. 35, 2020.

[8] Mohammad Fathian, Gholam Reza Shiran and Ahmad Reza Jafarian-Moghaddam, "Two New Clustering Algorithms for Vehicular Ad-Hoc Network Based on Ant Colony System", Wireless Personal Communications, Vol. 83, pp. 473-491, 2015.

[9] Kai Lei, Junjie Fang, Qichao Zhang, Junjun Lou, Maoyu Du, Jiyue Huang, Jianping Wang and Kuai Xu, "Blockchain-Based Cache Poisoning Security Protection and Privacy-Aware Access Control in NDN Vehicular Edge Computing Networks", Journal of Grid Computing, Vol. 18, pp. 593-613, 2020.

[10]P. Sun and N. Samaan, "A Novel VANET-Assisted Traffic Control for Supporting Vehicular Cloud Computing," in IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 11, pp. 6726-6736, Nov. 2021.

[11]Chuntao Ding, Ao Zhou, Jie Huang, Ying Liu and Shangguang Wang, "ECDU: an edge content delivery and update framework in Mobile edge computing", EURASIP Journal on Wireless Communications and Networking, No. 268, 2019.

[12] Chung-Ming Huang, Shih-Yang Lin and Zhong-You Wu, "The k-hop-limited V2V2I VANET data offloading using the Mobile Edge Computing (MEC) mechanism", Vehicular Communications, Vol. 26, 2020.

[13] Mu Zhang, Song Wang and Qing Gao, "A joint optimization scheme of content caching and resource allocation for internet of vehicles in mobile edge computing", Journal of Cloud Computing, Vol. 9, No. 33, 2020.

[14] Xing Chen, Shihong Chen, Yun Ma, Bichun Liu, Ying Zhang and Gang Huang, "An adaptive offloading framework for Android applications in mobile edge computing", Science China Information Sciences, Vol. 62, No. 82102, 2019.

[15] J. Wang, D. Feng, S. Zhang, J. Tang and T. Q. S. Quek, "Computation Offloading for Mobile Edge Computing Enabled Vehicular Networks," in IEEE Access, vol. 7, pp. 62624-62632, 2019.

[16] Z. Haitao, D. Yi, Z. Mengkang, W. Qin, S. Xinyue and Z. Hongbo, "Multipath Transmission Workload Balancing Optimization Scheme Based on Mobile Edge Computing in Vehicular Heterogeneous Network," in IEEE Access, vol. 7, pp. 116047-116055, 2019.

[17] Z. Wu, Z. Yang, C. Yang, J. Lin, Y. Liu and X. Chen, "Joint deployment and trajectory optimization in UAV-assisted vehicular edge computing networks," in Journal of Communications and Networks, vol. 24, no. 1, pp. 47-58, Feb. 2022.

[18] Chunhui Liu, Kai Liu, Hualing Ren, Xincao Xu, Ruitao Xie and Jingjing Cao, "RtDS: real-time distributed strategy for multi-period task offloading in vehicular edge computing environment", Neural Computing and Applications, 2021.

[19] M. Li, J. Gao, L. Zhao and X. Shen, "Deep Reinforcement Learning for Collaborative Edge Computing in Vehicular Networks," in IEEE Transactions on Cognitive Communications and Networking, vol. 6, no. 4, pp. 1122-1135, Dec. 2020.

[20] J. Zhang, H. Zhong, J. Cui, M. Tian, Y. Xu and L. Liu, "Edge Computing-Based Privacy-Preserving Authentication Framework and Protocol for 5G-Enabled Vehicular Networks," in IEEE Transactions on Vehicular Technology, vol. 69, no. 7, pp. 7940-7954, July 2020.