Breaking into social nervous system

Abstract

Gathering and analyzing of real time communicational traces in a society empowers us to formulate behavioral structure of its members. Reality Mining the core concept to support this enables us to collect digital breadcrumbs left by people while they perform their daily activities. Collection of these signals through sociometric badges and then formulating them for a visual view is shown in the further section of this paper. The model proposed in this paper is based on multi-level information gathering and filtration system. In this model society is divided in groups on the basis of their intra-group and inter-group interactions. It determines the sequestered groups and the quickest information distributing group. This filtration is processed on the server and all the data transactions are accomplished with secure protocols. For collection of communicational traces we argue use of mobile devices as sensors, which process data to further server. Further incorporation of influential model and centrality approaches enable us to detect most influential person in the sub-group. Implementation of web based multi-level architecture allows easy extension, wider area coverage, storing and processing large log records and easy integration with preexisting communication network.