Inferring Ties in Social IoT Using Location-Based Networks and Identification of Hidden Suspicious Ties

Abstract EN

Stochastic Internet of Things (IoT)-based communication behavior of the progressing world is tremendously impacting social networks.

The growth of social networks helps to quantify the effect on the Social Internet of Things (SIoT).

Multiple existences of two persons at several geographical locations in different time frames hint to predict the social connection.

We investigate the extent to which social ties between people can be inferred by critically reviewing the social networks.

Our study used Chinese telecommunication-based anonymized caller data records (CDRs) and two openly available location-based social network data sets, Brightkite and Gowalla.

Our research identified social ties based on mobile communication data and further exploits communication reasons based on geographical location.

This paper presents an inference framework that predicts the missing ties as suspicious social connections using pipe and filter architecture-based inference framework.

It highlights the secret relationship of users, which does not exist in real data.

The proposed framework consists of two major parts.

Firstly, users’ cooccurrence based on the mutual location in a specific time frame is computed and inferred as social ties.

Results are investigated based upon the cooccurrence count, the gap time threshold values, and mutual friend count values.

Secondly, the detail about direct connections is collected and cross-related to the inferred results using Precision and Recall evaluation measures.

In the later part of the research, we examine the false-positive results methodically by studying the human cooccurrence patterns to identify hidden relationships using a social activity.

The outcomes indicate that the proposed approach achieves comprehensive results that further support the theory of suspicious ties.