SG-PBFT: A secure and highly efficient distributed blockchain PBFT consensus algorithm for intelligent Internet of vehicles

With recent developments in communication technologies, vehicular networks have become a reality with various applications. However, the cybersecurity aspect of vehicular networks is still an open issue that needs to be addressed with novel defence mechanisms against attacks. This paper first presents the state-of-the-art communication technologies in vehicular networks (either inter-vehicle networking or in-vehicle networking) along with their applications. Then we explore novel technologies including machine learning and blockchain as cybersecurity defence mechanisms in vehicular networks. Based on the extensive survey, we highlight some insights for future research to secure vehicular networks. 

Investigating the Prospect of Leveraging Blockchain and Machine Learning to Secure Vehicular Networks: A Survey

The energy harvesting cognitive wireless sensor network (EHCWSN) introduces energy harvesting technology and cognitive radio technology into the traditional wireless sensor network (WSN), which significantly prolongs the working life of the sensor node and effectively alleviates the congestion problem of the unlicensed spectrum. Due to the uncertainty of the energy harvesting process and the behavior of the primary user (PU), how to allocate and manage limited network resources is a crucial problem in the EHCWSN. In this work, a new Q-learning-based channel selection method is proposed for the energy harvesting process and the randomness of the PU's behavior in the sensor network. By continuously interacting and learning with the environment, the method guides the secondary user (SU) to select the channel with better channel quality. Moreover, we also propose a resource management and allocation mechanism with guaranteed QoS requirements for node traffic based on the framework of Lyapunov optimization theory. We design a low-complex online algorithm based on the optimization framework, which is then validated through extensive simulations. The results demonstrate that our design achieves higher accuracy with the QoS guarantee.

An Intelligent Resource Allocation Scheme in Energy Harvesting Cognitive Wireless Sensor Networks

The convergence of AI and IoT enables data to be quickly explored and turned into vital decisions, and however, there are still some challenging issues to be further addressed. For example, lacking of enough data in AI-based decision making [so-called sparse decision making (SDM)] will decrease the efficiency dramatically, or even disable the intelligent IoT networks. Taking the intelligent IoT networks as the network infrastructure, the recommendation systems have been facing such SDM problems. A naive solution is to introduce trust information. However, trust information may also face the difficulty of sparse trust evidence (also known as sparse trust problem). In our work, an accurate SDM model with two-way trust recommendation in the AI-enabled IoT systems is proposed, named TT-SVD. Our model incorporates both trust information and rating information more thoroughly, which can efficiently alleviate the above-mentioned sparse trust problem and therefore be able to solve the cold start and data sparsity problems. Specifically, we first consider the twofold trust influences from both trustees and trusters, which can be represented by a factor named trust propensity. To this end, we propose a dual model, including a truster model (TrusterSVD) and a trustee model (TrusteeSVD) based on an existing rating-only recommendation model called SVD++, which are integrated by the weighted average and yield the final model, TT-SVD. The experimental results show that our model outperforms the state-of-the-art, including SVD and TrustSVD in both the “all users” and “cold start users” cases, and the accuracy improvement can reach a maximum of 29%. Complexity analysis shows that our model is equally suitable for the case of large sparse data sets. In summary, our model can effectively solve the sparse decision problem by introducing the two-way trust recommendation, and hence improve the efficiency of the intelligent recommendation systems.

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TT-SVD: An Efficient Sparse Decision-Making Model With Two-Way Trust Recommendation in the AI-Enabled IoT Systems

Security analysis of indistinguishable obfuscation for internet of medical things applications

The pervasive communications between vehicles and dynamic mobility may significantly increase data exchange and therefore bring a huge amount of traffic data in InVANETs. Due to some environmental factors, like the vulnerability of wireless connection, limitation of in-car computing ability, and speed of vehicles, it is extremely challenging to design identity authentication protocols satisfying the requirements of both high security and efficiency simultaneously. To this end, the aggregate signature technology has been employed in InVANETs. However, the technology still suffers from high computational overhead due to the management of certificates, as well as the key escrow problem (i.e., the dependence on a fully trusted third party). In this article, we propose the SE-CLASA protocol for InVANETs in order to tackle the aforementioned problems. In addition, a novel factor-contained aggregation mechanism is proposed to resist an information injection attack investigated in our analysis. Moreover, we prove the security of the proposed SE-CLASA and conclude that it meets most known security requirements in a general InVANET scenario. Simulation results show the superiority of the proposed SE-CLASA, in terms of security and efficiency, compared to the most recent authentications in InVANETs.

A Security-Enhanced Certificateless Aggregate Signature Authentication Protocol for InVANETs

https://link-springer-com-443.webvpn.jmu.edu.cn/content/pdf/10.1007%2F978-3-319-78262-1_92.pdf

Wenjuan Zhou

Papers

A Security-Enhanced Certificateless Aggregate Signature Authentication Protocol for InVANETs

Data-Driven Security