The Internet of Vehicles (IoVs) integrates vehicles to the enormous realm of cyberspace which introduces some intelligence and convenience in the transportation sector. However, real-time traffic related information is exchanged over the open public internet among the vehicles and with other infrastructures. This exposes these networks to a myriad of security threats that can lead to accidents and congestions. Although many solutions have been developed over the recent past, most of them are inefficient while others are still susceptible to attacks. In this paper, we leverage on the k-valued modified bilinear inverse Diffie-Hellman problem and one-way hashing function to develop an efficient authentication protocol for IoVs. To demonstrate the robustness of its semantic security, we deploy the Real or Random (ROR) model. In addition, we execute extensive informal security anaysis to show that our scheme resists typical IoVs attacks such as forgery, privileged insider, and replay. Moreover, its performance evaluation shows that it incurs the lowest computation and communication overheads among its peers. Specifically, the proposed protocol reduces the transmission overheads by 8.5%, while increasing the supported security functionalities by 88.9%. It is therefor suitable for deployment in the IoV environment to mitigate the numerous security threats at relatively lower computation and energy costs.

This study presents a systematic literature review on blockchain-based authentication in smart environments that include smart city, smart home, smart grid, smart healthcare, smart farming and smart transportation. The review incorporated 39 articles presenting blockchain solutions for security and privacy issues through authentication mechanisms in these smart environments. Guided by three research questions to determine the main issues in smart environment, the availability of blockchain-based authentication solutions and identified research gaps and future research endeavors, this review used PRISMA method to provide insights on the use of blockchain-based authentication schemes. The research gap is that blockchain solutions are mostly at the proposal, and sometimes conceptual stage is in the reviewed articles. In addition, solutions presented in smart environments require exploration into blockchain. The findings show similar situational issues across different smart environments and the flexibility and adaptability of blockchain to provide solutions to the identified issues pertaining to security and privacy. More clearly, the authentication problem posed across different smart environments can be adapted to blockchain technology provided that it is combined with other technologies to increase efficiency, despite the existence of large-scale authentication mechanisms. Now, blockchain still has the unique, distributed feature of converting the current database into blockchain databases. This review guided future research directions which could further contribute to the sustainable management of smart environments.

Basket analysis is a prevailing technique to help retailers uncover patterns and associations of sold products in customer shopping transactions. However, as the size of transaction databases grows, the traditional basket analysis techniques and systems become less effective because of two issues in the applications of the big data age: data scalability and flexibility to adapt different application tasks. This paper proposes a scalable distributed frequent itemset mining (ScaDistFIM) algorithm for basket analysis on big transaction data to solve these two problems. ScaDistFIM is performed in two stages. The first stage uses the FP-Growth algorithm to compute the local frequent itemsets from each random subset of the distributed transaction dataset, and all random subsets are computed in parallel. The second stage uses an approximation method to aggregate all local frequent itemsets to the final approximate set of frequent itemsets where the support values of the frequent itemsets are estimated. We further elaborate on implementing the ScaDistFIM algorithm and a flexible basket analysis system using Spark SQL queries to demonstrate the system’s flexibility in real applications. The experiment results on synthetic and real-world transaction datasets demonstrate that compared to the Spark FP-Growth algorithm, the ScaDistFIM algorithm can achieve time savings of at least 90% while ensuring nearly 100% accuracy. Hence, the ScaDistFIM algorithm exhibits superior scalability. On dataset GenD with 1 billion records, the ScaDistFIM algorithm requires only 360 s to achieve 100% precision and recall. In contrast, due to memory limitations, Spark FP-Growth cannot complete the computation task.

Clustering a big distributed dataset of hundred gigabytes or more is a challenging task in distributed computing. A popular method to tackle this problem is to use a random sample of the big dataset to compute an approximate result as an estimation of the true result computed from the entire dataset. In this paper, instead of using a single random sample, we use multiple random samples to compute an ensemble result as the estimation of the true result of the big dataset. We propose a distributed computing framework to compute the ensemble result. In this framework, a big dataset is represented in the RSP data model as random sample data blocks managed in a distributed file system. To compute the ensemble clustering result, a set of RSP data blocks is randomly selected as random samples and clustered independently in parallel on the nodes of a cluster to generate the component clustering results. The component results are transferred to the master node, which computes the ensemble result. Since the random samples are disjoint and traditional consensus functions cannot be used, we propose two new methods to integrate the component clustering results into the final ensemble result. The first method uses component cluster centers to build a graph and the METIS algorithm to cut the graph into subgraphs, from which a set of candidate cluster centers is found. A hierarchical clustering method is then used to generate the final set of$k$cluster centers. The second method uses the clustering-by-passing-messages method to generate the final set of$k$cluster centers. Finally, the$k$-means algorithm was used to allocate the entire dataset into$k$clusters. Experiments were conducted on both synthetic and real-world datasets. The results show that the new ensemble clustering methods performed better than the comparison methods and that the distributed computing framework is efficient and scalable in clustering big datasets.

Knowledge extraction, data mining, e-learning or web applications platforms use heterogeneous and distributed data. The proliferation of these multifaceted platforms faces many challenges such as high scalability, the coexistence of complex similarity metrics, and the requirement of data quality evaluation. In this study, an extended complete formal taxonomy and some algorithms that utilize in achieving the detection and correction of contextual data quality anomalies were developed and implemented on structured data. Our methods were effective in detecting and correcting more data anomalies than existing taxonomy techniques, and also highlighted the demerit of Support Vector Machine (SVM). These proposed techniques, therefore, will be of relevance in detection and correction of errors in large contextual data (Big data).