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To facilitate the consumers’ online shopping, it is very necessary to build a reliable and sustainable product evaluation management (PEM) system to store, secure, and retrieve the evaluation data of products. To this end, we propose a novel PEM system based on blockchain technologies. In this PEM system, the evaluation data of products including evaluation scores and comments are stored in the interplanetary file system (IPFS), and then the data hash addresses are returned and stored on the blockchain. Then, the retrieval of evaluation data is implemented by designing and deploying smart contracts on the Ethereum blockchain. Since it is almost impossible to change any data stored in blockchain and IPFS, the proposed PEM system can protect the comment data from intentional or unintentional modifications. Moreover, the proposed PEM system allows users to efficiently and reliably retrieve the relevant evaluation data of a certain product by a given query keyword with the increase of data stored on the blockchain. In addition, to achieve high sustainability, the proposed system also contains an economic incentive mechanism, which is beneficial to form a virtuous circle in online shopping. The proposed system is simulated on the popular blockchain platform, i.e., Ethereum with Solidity programming language. The experimental results and analysis demonstrate that the proposed PEM system achieves desirable usability, reliability, and sustainability.

Graph sampling frequently compresses a large graph into a limited screen space. This paper proposes a hierarchical structure model that partitions scale-free graphs into three blocks: the core, which captures the underlying community structure, the vertical graph, which represents minority structures that are important in visual analysis, and the periphery, which describes the connection structure between low-degree nodes. A new algorithm named hierarchical structure sampling (HSS) was then designed to preserve the characteristics of the three blocks, including complete replication of the connection relationship between high-degree nodes in the core, joint node/degree distribution between high- and low-degree nodes in the vertical graph, and proportional replication of the connection relationship between low-degree nodes in the periphery. Finally, the importance of some global statistical properties in visualization was analyzed. Both the global statistical properties and local visual features were used to evaluate the proposed algorithm, which verify that the algorithm can be applied to sample scale-free graphs with hundreds to one million nodes from a visualization perspective.

Intelligent Transport Systems (ITS) is a developing technology that will significantly alter the driving experience. In such systems, smart vehicles and Road-Side Units (RSUs) communicate through the VANET. Safety apps use these data to identify and prevent hazardous situations in real-time. Detection of malicious nodes and attack traffic in Intelligent Transportation Systems (ITS) is a current research subject. Recently, researchers are proposing graph-based machine learning techniques to identify malicious users in the ITS environment, through which it is easy to analyze the network traffic and detect the malicious devices. Therefore, graph-based machine learning techniques could be a technique that efficiently detect malicious nodes in the ITS environment. In this context, this article aims to provide a technique for resolving authentication and security issues in ITS using lightweight cryptography and graph-based machine learning. Our solution uses the concepts of identity based authentication technique and graph-based machine learning in order to provide authentication and security to the smart vehicle in ITS. By authenticating smart vehicles in ITS and identifying various cyber threats, our proposed method substantially contributes to the development of intelligent transportation communication environment.

About 70% of the earth’s surface is covered with water, and providing safe transportation in this vast area is the responsibility of the maritime transportation system. The maritime transportation system provides safety to the vessel’s passengers, manages the route of the vessels to avoid collision, and monitors the vessel’s condition during the journey. All tasks performed by the maritime transport system require continuous monitoring of the vessel performance, and for this, different IoT devices are installed on the vessel board. The IoT devices collect data related to the physical parameters of the vessel and share it with the coast-side servers. As the shared data contains confidential information about the vessel, there is a need for secure data sharing techniques, allowing only the authenticated personals to access the data received from the maritime IoT devices. In this context, we developed an identity-based secure information sharing scheme for the maritime transport system that maintains proper authentication measures. Our proposed approach uses the concept of identity-based encryption for authentication management. Our proposed approach is IND-sID-CCA secure and works efficiently with maritime IoT devices.