Socio-cyber network: The potential of cyber-physical system to define human behaviors using big data analytics

Abstract

The growing gap between users and the big data analytics requires innovative tools that address the challenges faced by big data volume, variety, and velocity. Therefore, it becomes computationally inefficient to analyze such massive volume of data. Moreover, advancements in the field of big data application and data science leads toward a new paradigm of human behavior, where various smart devices integrate with each other and establish a relationship. However, majority of the systems are either memoryless or computational inefficient, which are unable to define or predict human behavior. Therefore, keeping in view the aforementioned needs, there is a requirement for a system that can efficiently analyze a stream of big data within their requirements. Hence, this paper presents a system architecture that integrates social network with the technical network. We derive a novel notion of ‘Socio-Cyber Network’, where a friendship is made based on the geo-location information of the user, where trust index is used based on graphs theory. The proposed graph theory provides a better understanding of extraction knowledge from the data and finding relationship between different users. To check the efficiency of the proposed algorithms exploited in the proposed system architecture, we have implemented our proposed system using Hadoop and MapReduce. MapReduce for cyber-physical system (CPS) is supported by a parallel algorithm that efficiently process a huge volume of data sets. The system is implemented using Spark GraphX tool at the top of the Hadoop parallel nodes to generate and process graphs with near real-time. Moreover, the system is evaluated in terms of efficiency by considering the system throughput and processing time. The results show that the proposed system is more scalable and efficient.