Recent developments in IoT have facilitated advancements in Internet of Vehicles (IoV) with autonomous vehicles and roadside infrastructure as its key components. IoV aims to provide new innovative services for different modes of transport with adaptive traffic management and enables vehicles to broadcast messages to improve traffic safety and efficiency. However, due to non-trusted environments, it is difficult for vehicles to evaluate the credibility of the messages that they receive. Therefore, trust establishment in IoV is a key security concern that is constantly limited by scalability challenges. This paper proposes a blockchain based protocol for IoV using smart contracts, Physical Unclonable Functions (PUF), certificates, and a dynamic proof-of-work (dPoW) consensus algorithm. The blockchain, in conjunction with contracts, provides a secure framework for registering trusted vehicles and blocking malicious ones. PUFs are used to assign a unique identity to each vehicle via which trust is established. Certificates are issued by roadside units which preserve the privacy of vehicles, whereas the dPoW consensus allows the protocol to scale according to the incoming traffic generated by the vehicles. To demonstrate the feasibility and scalability of the proposed protocol, security and performance analyses are presented. A case study is also discussed along with a comparative analysis, which confirms that our protocol can provide superior decentralized trust management for IoV.

One of the most important and critical requirements for the Internet of Vehicles (IoV) is security under strict latency. Typically, authentication protocols for vehicular ad-hoc networks need to authenticate themselves frequently. This results in reduced application traffic and increased overhead. Moreover, the mobile nature of vehicles makes them a prime target for physical, side-channel, and cloning attacks. To address these issues, this paper presents an efficient protocol for authentication in the IoV. The proposed protocol uses Physical Unclonable Functions to provide the desired security characteristics. To reduce the overhead of authentication and improve the throughput of application layer packets, the proposed protocol uses a three layered infrastructure architecture for IoVs, i.e., road side units, road side unit gateways, and trusted authority. A vehicle needs to authenticate only once when it enters the area of a road side unit gateway which may engulf multiple road side units. A performance analysis of the protocol shows that the proposed strategy significantly reduces the number of authentication packets and MAC/PHY overhead while the security analysis demonstrates its robustness against various types of attacks.

Blockchain is emerging as a promising candidate for the uberization of Internet services. It is a decentralized, secure, and auditable solution for exchanging, and authenticating information via transactions, without the need of a trusted third party. Therefore, blockchain technology has recently been integrated with industrial Internet-of-things (IIoT) networks to help realize the fourth industrial revolution, Industry 4.0. Though blockchain-enabled IIoT networks may have the potential to support the services and demands of next-generation networks, the gap analysis presented in this work highlights some of the areas that need improvement. Based on these observations, the article then promotes the utility of reinforcement learning (RL) techniques to address some of the major issues of blockchain-enabled IIoT networks such as block time minimization and transaction throughput enhancement. This is followed by a comprehensive case study where a Q-learning technique is used for minimizing the occurrence of forking events by reducing the transmission delays for a miner. Extensive simulations have been performed and the results have been obtained for the average transmission delay which relates to the forking events. The obtained results demonstrate that the Q-learning approach outperforms the greedy policy while having a reasonable level of complexity. To further develop the blockchain-enabled IIoT networks, some future research directions are also documented. While this article highlights the applications of RL techniques in blockchain-enabled IIoT networks, the provided insights and results could pave the way for rapid adoption of blockchain technology.

The integration of Internet of Vehicles (IoV) with social networks has introduced Social IoV (SIoV) that will offer new applications in vehicular networks, e.g., personalized recommendations and route planning. This will be facilitated by heterogeneous access technologies and edge computing to offload tasks from vehicles via secure resource assignment. Thus, each vehicle in SIoV acts as a social subject that manages its own network. SIoV will lead to an explosive growth in network size, and induce issues like scalability and resource discovery. Blockchain is a potential candidate to address these, however, it is not suitable for SIoV with traditional proof-of-work (PoW) consensus. In this paper, we propose a framework that uses a dynamic PoW (dPoW) consensus with a checkpoint mechanism and a resource assignment policy. The dPoW consensus has different mining difficulty levels that change according to the communication traffic, whereas the checkpoint defines an alternative mechanism to generate the next block hash. The assignment policy manages an access control list to mandate the edge modules to securely distribute resources among vehicles. To study the feasibility of our framework, we present a formal security analysis using the Access Control Logic model. For the performance analysis, we use three metrics, i.e., scalability, latency, and security. With these analyses, we demonstrate that our framework offers enhanced security and can scale with a minimal increase in computation overhead. A case study with a comparative analysis is also discussed that evaluates the network dynamics and attests the superior performance of the framework under a real-life vehicular network scenario.

Industry 4.0 represents the fourth industrial revolution that will leverage the Industrial Internet of Things (IIoT) to introduce adaptive and autonomous systems that can self-heal and self-learn. IIoT aims to promote businesses and industries by realizing intelligent industrialization. However, the constantly surging data volumes that are generated by IIoT environments present security issues like data integrity and system scalability. Blockchain is a promising candidate to address these problems, which offers distributed system design principles. Though blockchain based IIoT frameworks may have the potential to support the demands and services of next-generation industrial systems, their integration is still constrained by significant challenges in scalability and security. Therefore, blockchain in its original structure with traditional proof-of-work consensus is not suitable for IIoT. To address this, we propose a blockchain architecture that uses a Dynamic Proof of Work consensus with a block checkpoint mechanism. The dynamic consensus functions with different mining difficulty levels and allows the architecture to efficiently scale with increase in communication traffic of IIoT devices, whereas the checkpoint defines an alternative mechanism to generate the next block hash in the blockchain. To study the scalability and feasibility of the architecture, thorough performance and security analyses are presented, which attest that it can scale and offer enhanced security fidelity with a minimal increase in block mining time.

Traditional malware propagation control schemes do not prevent device-to-device (D2D) malware spread, have high time cost, and may result in low probability of detecting compromised devices. Moreover, the unprecedented scale and heterogeneity of Internet of Things (IoT) devices make these schemes inapplicable to IoT networks. Therefore, to rectify these issues, this article presents a secure patching framework for IoT with different network isolation levels to efficiently mitigate and control malware propagation. It uses remote attestation to detect compromised devices with a high probability and identify the origin of malicious activities. It also proposes virtual patching of devices via physical unclonable functions (PUFs) to contain the malware spread. The isolation levels are based on the susceptible, exposed, infected, and resistant (SEIR) model that act as an access control list to quantify device operation and mitigate D2D malware spread. We present a security analysis based on the access control logic model. A performance evaluation with a comparative analysis is also discussed using the SEIR model. These analyses confirm the reduction in patching time and superior performance of our framework, i.e., with 10% of initially infected devices, IoT-Proctor had a reduction rate of malware five times faster than the existing techniques.