The recent emergence of the promising paradigm of the Social Internet of Things (SIoT) is a result of an intelligent amalgamation of the social networking concepts with the Internet of Things (IoT) objects (also referred to as “things”) in an attempt to unravel the challenges of network discovery, navigability, and service composition. This is realized by facilitating the IoT objects to socialize with one another, i.e., similar to the social interactions amongst human beings. A fundamental issue that mandates careful attention is to thus establish, and over time, maintain trustworthy relationships amongst these IoT objects. Therefore, a trust framework for SIoT must include object-object interactions, the aspects of social relationships, credible recommendations, etc., however, the existing literature has only focused on some aspects of trust by primarily relying on the conventional approaches that govern linear relationships between input and output. In this paper, an artificial neural network-based trust framework, Trust–SIoT, has been envisaged for identifying the complex non-linear relationships between input and output in a bid to classify trustworthy objects. Moreover, Trust–SIoT has been designed for capturing a number of key trust metrics as input, i.e., direct trust by integrating both current and past interactions, reliability and benevolence of an object, credible recommendations, and the degree of relationship by employing knowledge graph embedding. Finally, we have performed extensive experiments to evaluate the performance of Trust–SIoT vis-á-vis state-of-the-art heuristics on two real-world datasets. The results demonstrate that Trust–SIoT achieves a higher F1-score and lower MAE and MSE scores.

The Internet of Things (IoT) is an evolving network of billions of interconnected physical objects, such as, numerous sensors, smartphones, wearables, and embedded devices. These physical objects, generally referred to as the smart objects, when deployed in real-world aggregates useful information from their surrounding environment. As-of-late, this notion of IoT has been extended to incorporate the social networking facets which have led to the promising paradigm of the ‘Social Internet of Things’ (SIoT). In SIoT, the devices operate as an autonomous agent and provide an exchange of information and services discovery in an intelligent manner by establishing social relationships among them with respect to their owners. Trust plays an important role in establishing trustworthy relationships among the physical objects and reduces probable risks in the decision making process. In this paper, a trust computational model is proposed to extract individual trust features in a SIoT environment. Furthermore, a machine learning-based heuristic is used to aggregate all the trust features in order to ascertain an aggregate trust score. Simulation results illustrate that the proposed trust-based model isolates the trustworthy and untrustworthy nodes within the network in an efficient manner.

Recent considerable state-of-the-art advancements within the automotive sector, coupled with an evolution of the promising paradigms of vehicle-to-everything communication and the Internet of Vehicles (IoV), have facilitated vehicles to generate and, accordingly, disseminate an enormous amount of safety-critical and non-safety infotainment data in a bid to guarantee a highly safe, convenient, and congestion-aware road transport. These dynamic networks require intelligent security measures to ensure that the malicious messages, along with the vehicles that disseminate them, are identified and subsequently eliminated in a timely manner so that they are not in a position to harm other vehicles. Failing to do so could jeopardize the entire network, leading to fatalities and injuries amongst road users. Several researchers, over the years, have envisaged conventional cryptographic-based solutions employing certificates and the public key infrastructure for enhancing the security of vehicular networks. Nevertheless, cryptographic-based solutions are not optimum for an IoV network primarily, since the cryptographic schemes could be susceptible to compromised trust authorities and insider attacks that are highly deceptive in nature and cannot be noticed immediately and are, therefore, capable of causing catastrophic damage. Accordingly, in this article, a distributed trust management system has been proposed that ascertains the trust of all the reputation segments within an IoV network. The envisaged system takes into consideration the salient characteristics of familiarity, i.e., assessed via a subjective logic approach, similarity, and timeliness to ascertain the weights of all the reputation segments. Furthermore, an intelligent trust threshold mechanism has been developed for the identification and eviction of the misbehaving vehicles. The experimental results suggest the advantages of our proposed IoV-based trust management system in terms of optimizing the misbehavior detection and its resilience to various sorts of attacks.

The recent advancement in home automation technologies and inter-connectivity of smart appliances has contributed immensely in developing intelligent home environment. The complex smart home environment consists of smart devices from different vendors having different configuration and communication standards that introduce the problem of a heterogeneous subsystem in a smart home. A need is emerging for a central management system to control all these various devices. In this paper, we propose an action-based framework that acts as a middleware between the user and smart appliances which allow the user to control these heterogeneous devices in a federated manner. The proposed framework is based on a controller, for accepting user input and sending instructions to the intended smart devices. Framework maintains the integrity of an instruction send to the device and employs the mechanism so that only authentic user can access them. We also incorporate Human System Interaction (HSI) in our framework, by developing an Android application which receives input in the form of motion gestures, to fulfill the human-centric interaction requirements of the user to communicate with the smart home appliances. We evaluate the performance of our framework by implementing the controller in a local home network and the cloud.

Recent technological breakthroughs in vehicular ad hoc networks and the Internet of Things (IoT) have transformed vehicles into smart objects thus paving the way for the evolution of the promising paradigm of the Internet of Vehicles (IoV), which is an integral constituent of the modern intelligent transportation systems. Simply put, IoV attributes to the IoT-on-wheels, wherein vehicles broadcast safety-critical information among one another (and their immediate ambiences) for guaranteeing highly reliable and efficacious traffic flows. This, therefore, necessitates the need to fully secure an IoV network since a single malicious message is capable enough of jeopardizing the safety of the nearby vehicles (and their respective passengers) and vulnerable pedestrians. It is also pertinent to mention that a malicious attacker, i.e., vehicle, is not only able to send counterfeited safety-critical messages to its nearby vehicles and the traffic management authorities but could further enable a compromised vehicle to broadcast both spoofed coordinates and speed-related information. It is, therefore, of the utmost importance that malicious entities and their messages be identified and subsequently eliminated from the network before they are able to manipulate the entire network for their malicious gains. This paper, therefore, delineates on the convergence of the notion of trust with the IoV primarily in terms of its underlying rationale. It further highlights the opportunities which transpire as a result of this convergence to secure an IoV network. Finally, open research challenges, together with the recommendations for addressing the same, have been discussed.