Recently, the emerging Social Internet of Things (SIoT) has opened up a myriad of research opportunities. One of the most fundamental research challenges posed by SIoT and its objective is to allow smart objects to create and maintain their own social networks. In this paper, we are interested in constructing these social networks, which are built upon social relationships between smart objects, and quantitatively estimating the social strength values of the relations. In particular, we propose a model named social strength prediction model, which infers social connections among smart objects and predicts the strength of the connections using the co-usage data of the objects. The proposed model is divided into two major components: 1) entropy-based and 2) distance-based social strength computations. The two components capture different properties of co-usages of objects, namely, the diversity and spatiotemporal features, which are all essential factors that contribute to the values of social strength. In order to test the feasibility of the proposed model, we conducted extensive sets of experiments with real-world datasets, which include the history data of various object usages. Based on the results, we show that the proposed model is in fact capable of inferring social connections and predicting their corresponding social strength with relatively high precision and recall.
Bibliographical noteFunding Information:
Manuscript received December 23, 2017; revised May 26, 2018, July 20, 2018, and August 29, 2018; accepted September 5, 2018. Date of publication September 13, 2018; date of current version November 14, 2018. This work was supported by a National Research Foundation of Korea (NRF) grant funded by the Korea Government (Ministry of Science, ICT and Future Planning) (No. NRF-2016R1A2B4015873). (Corresponding author: Kyong-Ho Lee.) The authors are with the Department of Computer Science, Yonsei University, Seoul 03722, South Korea (e-mail: email@example.com; firstname.lastname@example.org; email@example.com; firstname.lastname@example.org). Digital Object Identifier 10.1109/JIOT.2018.2869933
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All Science Journal Classification (ASJC) codes
- Signal Processing
- Information Systems
- Hardware and Architecture
- Computer Science Applications
- Computer Networks and Communications