The fifth-generation (5G) technology-enabled vehicular network has been widely used in intelligent transportation in recent years. Since messages shared among vehicles are always broadcasted by openness environment’ nature, which is vulnerable to several privacy and security problems. To cope with this issue, several researchers have proposed pseudonym authentication schemes for the 5G-enabled vehicular network. Nevertheless, these schemes applied complected and time-consumed operations. Therefore, this paper proposes a fog computing-based pseudonym authentication (FC-PA) scheme to decrease the overhead of performance in 5G-enabled vehicular networks. The FC-PA scheme applies only one scalar multiplication operation of elliptic curve cryptography to prove information. A security analysis of our work explains that our scheme satisfies privacy-preserving and pseudonym authentication, which are resilient against common security attacks. With performance efficiency, our work can obtain better trade-offs between efficiency and security than the well-known recent works.

Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are $0.0031$, $2.0185$ to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase.

The privacy and security of the information exchanged between automobiles in 5G-enabled vehicular networks is at risk. Several academics have offered a solution to these problems in the form of an authentication technique that uses an elliptic curve or bilinear pair to sign messages and verify the signature. The problem is that these tasks are lengthy and difficult to execute effectively. Further, the needs for revoking a pseudonym in a vehicular network are not met by these approaches. Thus, this research offers a fog computing strategy for 5G-enabled automotive networks that is based on the Chebyshev polynomial and allows for the revocation of pseudonyms. Our solution eliminates the threat of an insider attack by making use of fog computing. In particular, the fog server does not renew the signature key when the validity period of a pseudonym-ID is about to end. In addition to meeting privacy and security requirements, our proposal is also resistant to a wide range of potential security breaches. Finally, the Chebyshev polynomial is used in our work to sign the message and verify the signature, resulting in a greater performance cost efficiency than would otherwise be possible if an elliptic curve or bilinear pair operation had been employed.

Vehicle in vehicular ad hoc networks (VANETs) broadcasts beacons about their traffic status wirelessly for improving traffic safety and efficiency. Before deployment of the VANET system, problems related to security and privacy should be carefully addressed. In this article, we propose a lightweight authentication with conditional privacy-preserving scheme for guaranteeing secure communication in VANET. The proposed scheme is suitable for addressing issues related to security and privacy because it combines the tamper-proof device (TPD) based schemes with the roadside unit (RSU) based schemes. Based on elliptic curve cryptography, the proposed scheme preloads the initial public parameters and keys of the system in each TPD of RSU instead of the TPD of the on-border unit (OBU). Furthermore, the proposed scheme not only achieve security and privacy requirements but also resists common security attacks. The performance evaluation shows that the proposed scheme has a lower cost compared with other existing schemes in terms of computation cost and communication cost.

Artificial intelligence (AI) is a subfield of computer science concerned with developing intelligent machines capable of performing tasks similar to those performed by humans. This human‐created intelligence began more than 60 years ago. The goal of previous generations of applications was to demonstrate generic human‐like behaviour. The goal has expanded with the advancement and increased compliance of this technology. It includes areas such as healthcare, gaming, and smart devices. The COVID‐19 epidemic has posed a significant barrier to maintaining a sustainable strategy for mental health support clients with major mental illnesses and clinicians who have had to shift delivery modes quickly. In this study, we have conducted a systematic literature review (SLR) to provide an overview of the current state of the literature related to software measurement of healthcare using artificial intelligence. The study followed a secondary research strategy. The systematic literature review aim was to analyze software measurement of mental health illness in terms of previous literature. This study screened out of 28 research papers out of 1076 initial searches. We used Science Direct, IEEE Xplore, Springer Link, ACM, and Hindawi as database search engines. The research objective was to explore the needs of software applications and automation in the healthcare sector to bring efficiency to the systems. The research concluded that the healthcare setting crucially requires the implementation of software automation.

The quick progress of 5G networks has allowed for intelligent driving. The primary environment for intelligent driving is provided by vehicular ad hoc networks (VANETs), which relay real-time data and communications between moving vehicles and fixed infrastructure. Since the communication is open-access, the message exchanged is vulnerable to privacy and security attacks. To address with this challenge, several authentication schemes have proposed. Nevertheless, the complexity of current these schemes means that re-authenticating vehicle identities every time they reach a new area of infrastructure coverage significantly hampers the overall network’s efficiency. This paper has proposed a handover authentication, called HAFC scheme based on fog computing to achieve fastly re-authentication of vehicles via secure property transfer among infrastructures (fog servers) for 5G-assisted vehicular blockchain networks. The proposed HAFC scheme consists of both stages namely, initial-authentication stage and handover-authentication stage. In security analysis shows that the proposed HAFC scheme’s vehicle to fog server-for both stages is Computational Diffie-Hellma (CDH)-secure. According to the simulation results, the novel handover authentication stage takes only a fraction of the time required for the first one.