Recently, connected vehicles (CV) are becoming a promising research area leading to the concept of CV as a Service (CVaaS). With the increase of connected vehicles and an exponential growth in the field of online cab booking services, new requirements such as secure, seamless and robust information exchange among vehicles of vehicular networks are emerging. In this context, the original concept of vehicular networks is being transformed into a new concept known as connected and autonomous vehicles. Autonomous vehicular use yields a better experience and helps in reducing congestion by allowing current information to be obtained by the vehicles instantly. However, malicious users in the internet of vehicles may mislead the whole communication where intruders may compromise smart devices with the purpose of executing a malicious ploy. In order to prevent these issues, a blockchain technique is considered the best technique that provides secrecy and protection to the control system in real time conditions. In this paper, the issue of security in smart sensors of connected vehicles that can be compromised by expert intruders is addressed by proposing a blockchain framework. This study has further identified and validated the proposed mechanism based on various security criteria, such as fake requests of the user, compromise of smart devices, probabilistic authentication scenarios and alteration in stored user’s ratings. The results have been analyzed against some existing approach and validated with improved simulated results that offer 79% success rate over the above-mentioned issues.

Currently, data generated by smart devices connected through internet is increasing relentlessly. An effective and efficient paradigm is needed to deal with the bulk amount of data produced by the internet of things. Deep learning and Edge Computing are the emerging technologies, which are used for efficient processing of huge amount of data with distinct accuracy. In this world of advanced Information Systems, one of the major issues is authentication. Several techniques have been employed to solve this problem. Face recognition is considered as one of the most reliable solutions. Usually for face recognition, Scale-Invariant Feature Transform (SIFT) and Speeded Up Robust Features (SURF) have been used by research community. This paper proposes an algorithm for face detection and recognition based on Convolution Neural Networks (CNN), that outperforms the traditional techniques. In order to validate the efficiency of the proposed algorithm, a smart class room for the student’s attendance using face recognition has been proposed. The face recognition system is trained on publically available Labelled Faces in the Wild (LFW) dataset. The system can detect approximately 35 faces and recognizes 30 out of them from the single image of 40 students. The proposed system achieved 97.9data. Moreover, generated data by smart class rooms is computed and transmitted through an Internet of Things (IoT) based architecture using the edge computing. A comparative performance study shows that our architecture outperforms in terms of data latency and real time response.

The Internet of Things (IoT) paradigm has integrated the sensor network silos to the Internet and enabled the provision of value-added services across these networks. These smart devices are now becoming socially conscious by following the social Internet of Things (SIoT) model that empowers them to create and maintain social relationships among them. The Social Internet of Vehicle (SIoV) is one application of SIoT in the vehicular domain that has evolved the existing intelligent transport system (ITS) and vehicular ad-hoc networks (VANETs) to the next phase of Intelligent by adding socializing aspect and constant connectivity. SIoV generates a massive amount of real-time data enriched with context and social relationship information about vehicles, drivers, passengers, and the surrounding environment. Therefore, the role of privacy management becomes essential in SIoV, as data is collected and stored at different layers of its architecture. The challenge of privacy is aggravated because the dynamic nature of SIoV poses a major threat in its adoption. Motivated by the need to address these aspects, this paper identifies the challenges involved in managing privacy in SIoV. Furthermore, the paper analyzes the privacy issues and factors that are essential to be considered for preserving privacy in SIoV environments from different perspectives including the privacy of a person, behavior and action, communication, data and image, thoughts and feelings, location and space, and association. In addition, the paper discusses the blockchain-based solutions to preserve privacy for SIoV.

A smart city refers to an intelligent environment obtained by deploying all available resources and recent technologies in a coordinated and smart manner. Intelligent sensors (Internet of Things (IoT) devices) along with 5G technology working mutually are steadily becoming more pervasive and accomplish users' desires more effectively. Among a variety of IoT use cases, e-voting is a considerable application of IoT that relegates it to the next phase in the growth of technologies related to smart cities. In conventional applications, all the devices are often assumed to be cooperative and trusted. However, in practice, devices may be disrupted by the intruders to behave maliciously with the aim of degradation of the network services. Therefore, the privacy and security flaws in the e-voting systems in particular lead to a huge problem where intruders may perform a number of frauds for rigging the polls. Thus, the potential challenge is to distinguish the legitimate IoT devices from the malicious ones by computing their trust values through social optimizer in order to establish a legitimate communication environment. Further, in order to prevent from future modifications of data captured by smart devices, a Blockchain is maintained where blocks of all legitimate IoT devices are recorded. This article has introduced a secure and transparent e-voting mechanism through IoT devices using Blockchain technology with the aim of detecting and resolving the various threats caused by an intruder at various levels. Further, in order to validate the proposed mechanism, it is analyzed against various security parameters such as message alteration, Denial of Service (DoS) and Distributed Denial of Service (DDoS) attack and authentication delay.

The Internet of things is the next stage in the evolution of the Internet that is being materialized with the integration of billions of smart objects. The state-of-the-art communication technologies have enabled the previously isolated devices to become an active part of the Internet. This constant connectivity opens new avenues for novel applications such as the realization of social Internet of things and its subdomain the social Internet of vehicles. Socializing requires sharing of information that entails trust, especially in an open and broad social environment. This article highlights the key factors involved in conceptualizing an efficient trust model for social Internet of vehicles. Furthermore, it focuses on the unique challenges involved in designing the trust models for social Internet of vehicles. Several trust models exist in literature; however, most of the existing trust models are specific to their domains, for example, Internet of things, social Internet of things, or general vehicular networks. This article presents a brief review of the trust models that have the potential to be implemented in Social Internet of vehicles. Finally, the authors present an overview of how trending concepts and emerging technologies like blockchain and fog computing can assist in developing a trust-based social Internet of vehicles model for high-efficiency, decentralized architecture and dynamic nature of vehicular networks.