In the near future, intelligent vehicles will be part of the Internet of Things (IoT) and will offer valuable services and opportunities that could revolutionise human life in smart cities. The Vehicular Ad-hoc Network (VANET) is the core structure of intelligent vehicles. It ensures the accuracy and security of communication in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) modes to enhance road safety and decrease traffic congestion. However, VANET is subject to security vulnerabilities such as denial-of-service (DoS), replay attacks and Sybil attacks that may undermine the security and privacy of the network. Such issues may lead to the transmission of incorrect information from a malicious node to other nodes in the network. In this paper, we present a biometrics blockchain (BBC) framework to secure data sharing among vehicles in VANET and to retain statuary data in a conventional and trusted system. In the proposed framework, we take advantage of biometric information to keep a record of the genuine identity of the message sender, thus preserving privacy. Therefore, the proposed BBC scheme establishes security and trust between vehicles in VANET alongside the capacity to trace identities whenever required. Simulations in OMNeT++, veins and SUMO were carried out to demonstrate the viability of the proposed framework using the urban mobility model. The performance of the framework is evaluated in terms of packet delivery rate, packet loss rate and computational cost. The results show that our novel model is superior to existing approaches.

Vehicular ad hoc networks (VANETs) are a special kind of wireless communication network that facilitates vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. This technology exhibits the potential to enhance the safety of roads, efficiency of traffic, and comfort of passengers. However, this can lead to potential safety hazards and security risks, especially in autonomous vehicles that rely heavily on communication with other vehicles and infrastructure. Trust, the precision of data, and the reliability of data transmitted through the communication channel are the major problems in VANET. Cryptography-based solutions have been successful in ensuring the security of data transmission. However, there is still a need for further research to address the issue of fraudulent messages being sent from a legitimate sender. As a result, in this study, we have proposed a methodology for computing vehicle’s reputation and subsequently predicting the trustworthiness of vehicles in networks. The blockchain records the most recent assessment of the vehicle’s credibility. This will allow for greater transparency and trust in the vehicle’s history, as well as reduce the risk of fraud or tampering with the information. The trustworthiness of a vehicle is confirmed not just by the credibility, but also by its network behavior as observed during data transfer. To classify the trust, an ensemble learning model is used. In depth tests are run on the data set to assess the effectiveness of the proposed ensemble learning with feature selection technique. The findings show that the proposed ensemble learning technique achieves a 99.98% accuracy rate, which is notably superior to the accuracy rates of the baseline models.

Over recent years, the Internet of Things (IoT) has been widely adopted in various domains, including modern manufacturing. However, IoT security remains a critical challenge due to resource constraints and deployment in unsecured environments. This paper introduces a lightweight IoT authentication protocol leveraging physical unclonable functions (PUFs) tailored for smart manufacturing. The protocol employs lightweight cryptographic techniques and a PUF-based challenge–response mechanism to ensure key security properties such as confidentiality, integrity, anonymity, and perfect forward secrecy. Through a combination of formal analysis, the proposed protocol demonstrates resilience against prevalent attacks while significantly reducing computational and communication overheads. Comparative performance analysis highlights its efficiency and suitability for resource-constrained IoT environments.

The Internet of Vehicles (IoV) scenarios prominently feature resource access, information sharing for user navigation and infrastructure-based communications. External attackers and illegitimate data circulations are thwarted primarily through secure sharing and access controls. Authenticity, accessibility, and privacy of shared information resources might be affected if adverse and unauthorized access or false access are allowed to enter in IoV networks. This article provides an augmenting security approach for handling multi-vehicle resource access requests and sharing. The approach named as Dynamic Secure Multi-Access Scheme (DSMAS) that formulates access policies with amendments from the interacting infrastructure units. The amendments are identified from the resource access span and access success ratio observed in a series of intervals. Support vectors play a crucial role in the amendment decision-making process by effectively mitigating the risks associated with incorrect and delayed accesses. The intervals during which access is provided to vehicles are secured by the utilization of two-factor authentication keys for a specified period. In this process, the key validity relies on the interval time for which the access is granted. The dynamic nature of the system enables the adaptation of connection handovers and resource availability to comply with the access policies of the cloud infrastructure. The efficacy of the proposed scheme is assessed by the evaluation of access time, success rate, authentication time, and disconnection ratio, revealing its superiority over existing approaches.

Mobile crowdsourcing (MCS) is an autonomous paradigm that integrates humans, technology, devices, and computing methods for cost-effective task completion. The tasks are outsourced from service providers, organizations, etc. for ease of completion. Such a process is concerned with trust and privacy-dependent security considerations. This article provides a solution for mitigating illegitimate workers causing malicious injections in heterogeneous task processing instances. The method named Rendezvous Trust-enabled Task Processing (RTTPM) recommends workers and devices based on individual highness value. This highness represents the trust observed from the previous task completion intervals. In this trust computing and verification process two distinct phases of Q-learning are utilized; the transitions between false data injection and task completion are independently validated for highness value derivations. Learning offers a best-fit reward with updates based on the maximum possible highness derivatives from the transitions. If the derivatives are diverging from the rendezvous maximum reward, then the worker/ device is malicious, and its trust is nullified. Such nullified workers/devices are thwarted from the current task handling interval preventing further malicious injections. The reward computation is halted for the same from which a new transition phase is initiated. This process is active until all the tasks are processed and their precise completion is maximized.