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The phenomenal increase in the usage of Internet promotes the quality of trust in the scope of Internet of Things (IoT). Trust is beneficial in the provision of an effective, reliable, scalable, and trustworthy environment to users of the IoT network, where they can share their private information with each other on a secure communication platform. For successful communications among the Internet users, trust is an important factor to provide them with private infrastructures and secure environments, where exchanging data among devices becomes more easy and trustworthy. Therefore, trust management is a backbone for the successful and secure transmission of data among various nodes in a large-scale IoT network. To overcome the security issues, latency, and risk of malicious activities, a lightweight approach is proposed for those nodes in industrial IoT that cannot maintain security. LightTrust utilizes a centralized trust agent to generate and manage trust certificates that allow nodes to communicate for a specific time without performing trust computations. Trust agents also maintain a trust database to store the current trust degree for the aggregation/propagation purposes. Trust between two nodes is developed by direct observations in terms of compatibility, cooperativeness, and delivery ratio, whereas recommendations are used to develop trust in the context of indirect observations, i.e., experience or previous knowledge. The comparative simulations of the proposed and existing approaches are also performed whereby the results illustrate that the proposed approach efficiently maintains resilience and robust environments.

With the development of intelligent technologies, Internet of Things (IoT) opens up a new era in the field of automotive networks, namely Internet of Vehicles (IoV). The main goal of IoV is to provide a secure and reliable network to vehicles so that users can enjoy various services. However, vulnerabilities and incomplete protection mechanisms have led to a proliferation of security threats against IoV networks. Intrusion detection technology is an effective protection solution for IoV security, especially when Artificial Intelligence (AI) technology has been introduced into intrusion detection study. This paper first briefly introduces the concept and features of IoV, and then reviews the related research on AI-based IoV intrusion detection systems (IDSs). Finally, we discuss the open challenges and future research directions.

The past few years have witnessed an exponential growth of diverse Internet of Things (IoT) devices as well as compelling applications ranging from industrial production, intelligent transport, warehouse logistics to medical care. Dramatic advances in IoT technology not only bring enormous economic opportunities but also challenges. Recently, the integration of IoT and blockchain (BCoT) is considered a promising solution . Blockchain technology provides a secure and scalable framework to improve the data management of IoT devices. However, the huge computation and energy consumption in the blockchain consensus process prevents it from being directly applied as a generic platform for IoT. In this paper, we propose a cloud mining pool-aided BCoT architecture. The IoT devices can rent the computation resource from the cloud services dynamically. Then, the individual cloud miners are grouped into several mining pools to share block rewards to earn a steady profit and reduce the income variance. Based on this architecture, we study the mining pool selection problem and analyze the colony behaviors of IoT devices. We propose a centralized evolutionary game-based pool selection algorithm. In addition, considering the non-cooperative relationship among multiple miners, we also propose a lightweight distributed reinforcement learning algorithm, named the WoLF-PHC algorithm.