Community Detection in the Social Internet of Things Based on
Movement, Preference and Social Similarity
A. Meena KOWSHALYA , M. L. VALARMATHI
Department of CSE, Government College of Technology,
Abstract: Internet of Things (IoT) is one paradigm many visions technology. One of the many visions of Internet of Things is to make Things sociable. This is achieved by integrating IoT and Social networking which may lead to a new paradigm called Social Internet of Things (SIoT). SIoT is defined as collection of intelligent objects that can autonomously interact with its peers via owners. In a SIoT scenario, detecting and characterizing a network structure is very important. In this paper, we propose a new community detection algorithm that detects communities in SIoT using three metrics namely social similarity, preference similarity and movement similarity. To the best of our knowledge this is the first work that detects communities in large scale Social Internet of Things using social, preference and movement similarity. The experimental results show that the proposed community detection scheme achieves higher quality results in terms of detection rate and execution time when compared to existing methods.
Keywords: Social Internet of Things (SIoT), Internet of Things (IoT), Community Detection, Preference Similarity, Social Similarity, Movement Similarity.
CITE THIS PAPER AS:
A. Meena KOWSHALYA , M. L. VALARMATHI, Community Detection in the Social Internet of Things Based on Movement, Preference and Social Similarity, Studies in Informatics and Control, ISSN 1220-1766, vol. 25(4), pp. 499-506, 2016.
The world around us is composed of electrical and electronic gadgets such as sensors, actuators, RFIDs, etc., collaborating with humans and things. These objects have become a part of our fabric. Social Internet of Things is turning to be a pioneer successful paradigm for collaboration among peer communities. SIoT is composed of objects that are not only smarter but also socially conscious . Relationship between smart objects can be classified into four types . Parental object relationship, Co-work / Co-location object relationship, Ownership object relationship and Social object relationship. Parental object relationship is established when objects of the same manufacturer tend to collaborate with each other. Co-work or Co-location object relationship are established when objects meet each other at work place of owners or if an owner moves to a different location, their objects interact with other(s) object in that location. Ownership object relationship is defined as a relationship established by objects belonging to the same owner. Social object relationship is established when owners engage in social networking activities and objects tend to interact socially via owners. Many assumptions can be made from the concept of smart objects and their relationships. There must exists community of objects belonging to owners having common interest. Such objects tend to meet each other frequently leading to social similarity. Such objects exhibit similarity in behavioral pattern called preference similarity. Also these objects tend to move to similar places of interests exhibiting movement similarity. This paper takes into account movement, preference and social similarities to detect communities across SIoT environments. The rest of the paper is organized as follows. Section 2 presents the state of art in community detection of large scale social networks. Section 3 describes how movement, preference and social similarity is constructed. Section 4 presents the experimental results followed by Conclusion and future work.
- AN, J., X. GUI, W. ZHANG, J. JIANG, Nodes Social Relations Cognition for Mobility-aware in the Internet of Things, In Internet of Things (iThings/CPSCom), 2011 International Conference on and 4th International Conference on Cyber, Physical and Social Computing 2011 Oct 19, pp. 687-691.
- ATZORI, L., A. IERA, G. MORABITO, Siot: Giving a Social Structure to the Internet of Things, IEEE Communications Letters. 2011 Nov; vol. 15(11), pp. 1193-5.
- BAO, J., Y. ZHENG, M. F. MOKBEL, Location-based and Preference-aware Recommendation using Sparse Geo-social Networking Data, In Proceedings of the 20th International Conference on Advances in Geographic Information Systems 2012 Nov. 6, pp. 199-208.
- CHEN, K., H. SHEN, H. ZHANG, Leveraging Social Networks for P2P Content-based File Sharing in Disconnected MANETs, IEEE Trans. on Mobile Computing. 2014 Feb; vol. 13(2), pp. 235-49.
- GONZALEZ, M. C., C. A. HIDALGO, A. L. BARABASI, Understanding Individual Human Mobility Patterns, Nature, vol. 453(7196), 2008, pp. 779-82.
- GUO, B, Z. YU, X. ZHOU, D. ZHANG, Opportunistic IoT: Exploring the Social Side of the Internet of Things, In Computer Supported Cooperative Work in Design (CSCWD), 2012 IEEE 16th International Conference on 2012 May 23 pp. 925-929.
- Kowshalya, A. M., m. l. Valarmathi, Detection of Sybil’s across Communities over Social Internet of Things, Journal of Applied Engineering Science (Istrazivanja i projektovanja za privredu)1 (2016).
- LI, F., J. WU, MOPS: Providing Content-based Service in Disruption-tolerant Networks, In Distributed Computing Systems, 2009. ICDCS’09. 29th IEEE International Conference on 2009 Jun 22 (pp. 526-533). IEEE.
- LI, Z., R. CHEN, L. LIU, G. MIN, Dynamic Resource Discovery based on Preference and Movement Pattern Similarity for Large-Scale Social Internet-of-Things, accepted for publication in IEEE IoT 2015.
- LU, H., Q. ZHAO, Z. GAN, A Community Detection Algorithm Based on the Similarity Sequence, International Conference on Web Information Systems Engineering 2014 Oct 12 (pp. 63-78), Springer International Publishing.
- MISRA, S., R. BARTHWAL, M. S. OBAIDAT, Community Detection in an Integrated Internet of Things and Social Network Architecture, In Global Communications Conference (GLOBECOM), 2012 IEEE 2012 Dec 3 pp. 1647-1652, IEEE.
- NITTI, M., R. GIRAU, L. ATZORI, Trustworthiness Management in the Social Internet of Things, IEEE Transactions on Knowledge and Data Engineering. vol. 26(5), 2014, pp. 1253-66.
- PAGANI, E., L. VALERIO, G. P. ROSSI, Weak Social Ties Improve Content Delivery in Behavior-aware Opportunistic Networks, Ad Hoc Networks, vol. 25, 2015, pp. 314-29.
- QURESHI, B., G. MIN, D. KOUVATSOS, M. ILYAS, An Adaptive Content Sharing Protocol for P2P Mobile Social Networks, In Advanced Information Networking and Applications Workshops (WAINA), 2010 IEEE 24th International Conference on 2010 Apr 20, pp. 413-418.
- YOU, L., J. LI, C. WEI, L. HU, MPAR: A Movement Pattern-aware Optimal Routing for Social Delay Tolerant Networks, Ad Hoc Networks. 2015 Jan 31, vol. 24, pp. 228-49.