Unveiling Contextual Similarity of Things via Mining Human-Thing Interactions in the Internet of Things

TL;DR

This paper introduces DisCor-T, a graph-based method that mines human-thing interactions to discover implicit correlations among physical objects, enhancing management and application in the Internet of Things.

Contribution

The paper presents a novel graph-based approach using RWR to model and analyze human-thing interactions for uncovering correlations in IoT environments.

Findings

DisCor-T effectively discovers implicit correlations among things.

The approach improves things classification accuracy.

Evaluation confirms the method's feasibility and utility.

Abstract

With recent advances in radio-frequency identification (RFID), wireless sensor networks, and Web services, physical things are becoming an integral part of the emerging ubiquitous Web. Finding correlations of ubiquitous things is a crucial prerequisite for many important applications such as things search, discovery, classification, recommendation, and composition. This article presents DisCor-T, a novel graph-based method for discovering underlying connections of things via mining the rich content embodied in human-thing interactions in terms of user, temporal and spatial information. We model these various information using two graphs, namely spatio-temporal graph and social graph. Then, random walk with restart (RWR) is applied to find proximities among things, and a relational graph of things (RGT) indicating implicit correlations of things is learned. The correlation analysis lays a solid foundation contributing to improved effectiveness in things management. To demonstrate the utility, we develop a flexible feature-based classification framework on top of RGT and perform a systematic case study. Our evaluation exhibits the strength and feasibility of the proposed approach.