A fast and accurate approach for inferencing social relationships among IoT objects

Abdulwahab Aljubairy; Ahoud Alhazmi; Wei Emma Zhang; Quan Z. Sheng; Dai Hoang Tran

doi:10.1007/978-3-030-95408-6_7

A fast and accurate approach for inferencing social relationships among IoT objects

Abdulwahab Aljubairy^*, Ahoud Alhazmi, Wei Emma Zhang, Quan Z. Sheng, Dai Hoang Tran

^*Corresponding author for this work

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

The Internet of Things (IoT) has recently moved towards the “object-object” interaction model where things look for other things to provide composite services for the benefit of human beings, leading to the birth of the Social Internet of Things (SIoT) paradigm. Investigating the social dimension in IoT objects offers great opportunities to increase social awareness among IoT objects. To achieve this goal, recurrent spatio-temporal meetings among IoT objects could be exploited to enable smart objects to understand the co-presence with other smart objects. Therefore, we target to explore the social dimension by determining if any two IoT objects have met at a particular place for a period of time. In this paper, we develop a novel approach, named Social Relationships Inference (SociRence), based on computational geometry to calculate the co-presence among IoT objects efficiently. We conduct experimental studies on real-world SIoT datasets to evaluate the efficacy of our approach. The results demonstrate that our approach can calculate the spatio-temporal co-presence at a much higher speed than the baseline computation methods.

Original language	English
Title of host publication	Advanced Data Mining and Applications
Subtitle of host publication	17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, proceedings, part II
Editors	Bohan Li, Lin Yue, Jing Jiang, Weitong Chen, Xue Li, Guodong Long, Fei Fang, Han Yu
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	83-94
Number of pages	12
ISBN (Electronic)	9783030954086
ISBN (Print)	9783030954079
DOIs	https://doi.org/10.1007/978-3-030-95408-6_7
Publication status	Published - 2022
Event	17th International Conference on Advanced Data Mining and Applications, ADMA 2021 - Sydney, Australia Duration: 2 Feb 2022 → 4 Feb 2022

Publication series

Name	Lecture Notes in Artificial Intelligence
Publisher	Springer
Volume	13088
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Advanced Data Mining and Applications, ADMA 2021
Country/Territory	Australia
City	Sydney
Period	2/02/22 → 4/02/22

Keywords

Social Internet of Things
Social structure
Social relationships
Social awareness

Access to Document

10.1007/978-3-030-95408-6_7

Cite this

Aljubairy, A., Alhazmi, A., Zhang, W. E., Sheng, Q. Z., & Tran, D. H. (2022). A fast and accurate approach for inferencing social relationships among IoT objects. In B. Li, L. Yue, J. Jiang, W. Chen, X. Li, G. Long, F. Fang, & H. Yu (Eds.), Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, proceedings, part II (pp. 83-94). (Lecture Notes in Artificial Intelligence; Vol. 13088). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-95408-6_7

Aljubairy, Abdulwahab ; Alhazmi, Ahoud ; Zhang, Wei Emma et al. / A fast and accurate approach for inferencing social relationships among IoT objects. Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, proceedings, part II. editor / Bohan Li ; Lin Yue ; Jing Jiang ; Weitong Chen ; Xue Li ; Guodong Long ; Fei Fang ; Han Yu. Cham : Springer, Springer Nature, 2022. pp. 83-94 (Lecture Notes in Artificial Intelligence).

@inproceedings{9848f7e1abe643949f000981289fa7cf,

title = "A fast and accurate approach for inferencing social relationships among IoT objects",

abstract = "The Internet of Things (IoT) has recently moved towards the “object-object” interaction model where things look for other things to provide composite services for the benefit of human beings, leading to the birth of the Social Internet of Things (SIoT) paradigm. Investigating the social dimension in IoT objects offers great opportunities to increase social awareness among IoT objects. To achieve this goal, recurrent spatio-temporal meetings among IoT objects could be exploited to enable smart objects to understand the co-presence with other smart objects. Therefore, we target to explore the social dimension by determining if any two IoT objects have met at a particular place for a period of time. In this paper, we develop a novel approach, named Social Relationships Inference (SociRence), based on computational geometry to calculate the co-presence among IoT objects efficiently. We conduct experimental studies on real-world SIoT datasets to evaluate the efficacy of our approach. The results demonstrate that our approach can calculate the spatio-temporal co-presence at a much higher speed than the baseline computation methods.",

keywords = "Social Internet of Things, Social structure, Social relationships, Social awareness",

author = "Abdulwahab Aljubairy and Ahoud Alhazmi and Zhang, {Wei Emma} and Sheng, {Quan Z.} and Tran, {Dai Hoang}",

year = "2022",

doi = "10.1007/978-3-030-95408-6_7",

language = "English",

isbn = "9783030954079",

series = "Lecture Notes in Artificial Intelligence",

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booktitle = "Advanced Data Mining and Applications",

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note = "17th International Conference on Advanced Data Mining and Applications, ADMA 2021 ; Conference date: 02-02-2022 Through 04-02-2022",

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Aljubairy, A, Alhazmi, A, Zhang, WE , Sheng, QZ & Tran, DH 2022, A fast and accurate approach for inferencing social relationships among IoT objects. in B Li, L Yue, J Jiang, W Chen, X Li, G Long, F Fang & H Yu (eds), Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, proceedings, part II. Lecture Notes in Artificial Intelligence, vol. 13088, Springer, Springer Nature, Cham, pp. 83-94, 17th International Conference on Advanced Data Mining and Applications, ADMA 2021, Sydney, Australia, 2/02/22. https://doi.org/10.1007/978-3-030-95408-6_7

A fast and accurate approach for inferencing social relationships among IoT objects. / Aljubairy, Abdulwahab; Alhazmi, Ahoud; Zhang, Wei Emma et al.
Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, proceedings, part II. ed. / Bohan Li; Lin Yue; Jing Jiang; Weitong Chen; Xue Li; Guodong Long; Fei Fang; Han Yu. Cham: Springer, Springer Nature, 2022. p. 83-94 (Lecture Notes in Artificial Intelligence; Vol. 13088).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

TY - GEN

T1 - A fast and accurate approach for inferencing social relationships among IoT objects

AU - Aljubairy, Abdulwahab

AU - Alhazmi, Ahoud

AU - Zhang, Wei Emma

AU - Sheng, Quan Z.

AU - Tran, Dai Hoang

PY - 2022

Y1 - 2022

N2 - The Internet of Things (IoT) has recently moved towards the “object-object” interaction model where things look for other things to provide composite services for the benefit of human beings, leading to the birth of the Social Internet of Things (SIoT) paradigm. Investigating the social dimension in IoT objects offers great opportunities to increase social awareness among IoT objects. To achieve this goal, recurrent spatio-temporal meetings among IoT objects could be exploited to enable smart objects to understand the co-presence with other smart objects. Therefore, we target to explore the social dimension by determining if any two IoT objects have met at a particular place for a period of time. In this paper, we develop a novel approach, named Social Relationships Inference (SociRence), based on computational geometry to calculate the co-presence among IoT objects efficiently. We conduct experimental studies on real-world SIoT datasets to evaluate the efficacy of our approach. The results demonstrate that our approach can calculate the spatio-temporal co-presence at a much higher speed than the baseline computation methods.

AB - The Internet of Things (IoT) has recently moved towards the “object-object” interaction model where things look for other things to provide composite services for the benefit of human beings, leading to the birth of the Social Internet of Things (SIoT) paradigm. Investigating the social dimension in IoT objects offers great opportunities to increase social awareness among IoT objects. To achieve this goal, recurrent spatio-temporal meetings among IoT objects could be exploited to enable smart objects to understand the co-presence with other smart objects. Therefore, we target to explore the social dimension by determining if any two IoT objects have met at a particular place for a period of time. In this paper, we develop a novel approach, named Social Relationships Inference (SociRence), based on computational geometry to calculate the co-presence among IoT objects efficiently. We conduct experimental studies on real-world SIoT datasets to evaluate the efficacy of our approach. The results demonstrate that our approach can calculate the spatio-temporal co-presence at a much higher speed than the baseline computation methods.

KW - Social Internet of Things

KW - Social structure

KW - Social relationships

KW - Social awareness

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DO - 10.1007/978-3-030-95408-6_7

M3 - Conference proceeding contribution

AN - SCOPUS:85125391669

SN - 9783030954079

T3 - Lecture Notes in Artificial Intelligence

SP - 83

EP - 94

BT - Advanced Data Mining and Applications

A2 - Li, Bohan

A2 - Yue, Lin

A2 - Jiang, Jing

A2 - Chen, Weitong

A2 - Li, Xue

A2 - Long, Guodong

A2 - Fang, Fei

A2 - Yu, Han

PB - Springer, Springer Nature

CY - Cham

T2 - 17th International Conference on Advanced Data Mining and Applications, ADMA 2021

Y2 - 2 February 2022 through 4 February 2022

ER -

Aljubairy A, Alhazmi A, Zhang WE , Sheng QZ, Tran DH. A fast and accurate approach for inferencing social relationships among IoT objects. In Li B, Yue L, Jiang J, Chen W, Li X, Long G, Fang F, Yu H, editors, Advanced Data Mining and Applications: 17th International Conference, ADMA 2021, Sydney, NSW, Australia, February 2–4, 2022, proceedings, part II. Cham: Springer, Springer Nature. 2022. p. 83-94. (Lecture Notes in Artificial Intelligence). doi: 10.1007/978-3-030-95408-6_7

A fast and accurate approach for inferencing social relationships among IoT objects

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Keywords

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