Intelligent Transportation System (ITS) is gaining attention but at the same time, road accidents, congestion, delays, etc. have also increased. Relative information about such events is vital. Such information can be presented in legal processes as digital proof. Availability of the information is not a problem as multidimensional data have been recorded all the time by ITS. Recording all the information in ITS arises the problem of fetching relevant information and removing other facts and figure that are not required to describe certain situations such as an accident. To address this issue, we analyze road accident data and reduce various dimensions with Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Non-negative Matrix Factorization (NMF). We conduct comparative analysis with three datasets where error rate for PCA is 32% with Dataset1. Likewise, error rate for LDA and NMF are 36% and 35%, receptively. While keeping in mind that such reduced data is helpful in many legal processes, we introduce Blockchain in the framework. Blockchain can make data immutable thus can be considered as digital proof. Blockchain also requires a smart contract in this situation between insurance companies to collect data in case of any uncertain situation. Such analysis can offer a different point of views and trends in data. Information can be more explainable to define the situation and helps to develop a friendly environment for day-to-day customers. The proposed framework provides dimensionality reduction of data that eventually reduce the data dimension to store in Blockchain.

Internet of Things (IoT) has been at the center of attention among researchers for the last two decades. Their aim was to convert each real-world object into a virtual object. Recently, a new idea of integrating the Social Networking concept into the Internet of Things has merged and is gaining popularity and attention in the research society due to its vast and flexible nature. It comprises of the potential to provide a platform for innovative applications and network services with efficient and effective manners. In this paper, we provide the sustenance for the Social Internet of Things (SIoT) paradigm to jump to the next level. Currently, the SIoT technique has been proven to be efficient, but heterogeneous smart devices are growing exponentially. This can develop a problematic scenario while searching for the right objects or services from billions of devices. Small world phenomena have revealed some interesting facts and motivated many researchers to find the hidden links between acquaintances in order to reach someone across the world. The contribution of this research is to integrate the SIoT paradigm with the small world concept. By integrating the small world properties in SIoT smart devices, we empower the Smart Social Agent (SSA). The Smart Social Agent ensures the finding of appropriate friends (i.e., the IoT devices used by our friend circle) and services that are required by the user, without human intervention. The Smart Social Agent can be any smart device in SIoTs, e.g., mobile phones.

Blockchain may be an optimal solution when a detailed and transparent record of assets is necessary. It is imperative to manage and safeguard digital interactions or maintain a decentralized and shared system of records in applications, such as those used for electricity production, transmission, distribution, and consumption and those used for data sharing and secure payments. Such applications can benefit from blockchain technology to resolve these problems. In the proposed blockchain-based consumer electronics data sharing and safe payment framework, an innovative IoT meter detects monthly consumption and transmits the data to a decentralized application that is stored in the blockchain. This decentralized platform will generate the bill and provide incentives for legitimate consumers. Finally, the end-to-end latency and throughput were used to evaluate the performance of the proposed approach.

The tremendous growth of cyberspace has magnified the risk of advanced cyber-attacks, making the conventional security system deficient. An intrusion detec

Digital libraries, such as conference events, journal documents, books and thesis, research patents, and experiments generate a vast amount of data, named as, Scholarly Big Data. It covers scholarly related information for both researcher's perspective as well as publisher's perspective, such as academic activities, author's demography, academic social networks, etc. The relationships among Big Scholarly Data can be worthy of solving researcher as well as journal related concerns, if they are prudently treated to extract knowledge. The best approach to efficiently process these relationships is the graph. However, with the rapid growth in the number of digital articles by various libraries, the relationships raises exponentially, generating large graphs, which have become increasingly challenging to be handled in order to analyze scholarly information. On the other hand, many researchers and publishers/journals have severe concerns about the ranking control mechanisms and the consideration of quantity rather than quality. Therefore, in this paper, we proposed graph-based mechanisms to perform four critical decisions that are the need of the today's scholarly community. To improve the quality of the article, we proposed a mechanism for selecting and recommending suitable reviewers for a submitted paper based on researchers' expertise and their popularity in that particular field while avoiding conflict of interest. Also, due to shortcomings in the existing journal ranking approaches, we also designed a journal ranking mechanism including its new impact factor and relative ranking by using a modified version of traditional page ranking algorithm and excluding self-authors citations as well as self-journal citations. Similarly, researchers ranking is also important for various motives that is calculated based on the expert's field, citation count, and a number of publications while avoiding any loophole to increase the ranking such as, self-citations and wrong citations. Also, to efficiently process big graphs generated by a massive number of scholarly related relationships, we proposed an architecture that uses the parallel processing mechanism of the Hadoop ecosystem over the real-time analysis approach of Apache Spark with GraphX. Finally, the efficiency of the proposed system is evaluated in terms of processing time and throughput while implementing the designed decision mechanisms.