Abstract
The proliferation of the Internet into every household has provided more opportunities for residents to become closer to each other than before. However, solid structural barrier is raised and social relationships within such neighborhoods are weak compared to those in traditional towns. Accordingly, activating communities and ultimately enhancing a sense of community through constructive participation and communal sharing of labor among residents has currently emerged as a challenging issue in a contemporary housing complex. In an effort to activate those communities, a notion of smart community is presented in which multiple smart homes are equipped with Internet of Things and interconnected with each other. Beyond the unadorned smart community composed by physical proximity, it is essential to discover a human-centric community that achieves communal benefits and enables residents to maximize individual economic gain by leveraging collective intelligence. In this article, we present a multi-dimensional smart community discovery scheme that enables householders to find human-centric community considering multi-dimensional factors in terms of physical, social, and economical aspects. We conduct experiments with 30 real households by applying a community-based energy saving scenario. Experiment results show that the proposed scheme performs better when compared to the physical proximity-based one in energy consumption and user satisfaction.
- D. Ahn, T. Kim, S. J. Hyun, and D. Lee. 2012. Inferring user interest using familiarity and topic similarity with social neighbors in facebook. In Proceedings of the 2012 IEEE/WIC/ACM International Conference on Web Intelligence (WI’12). 196--200. Google ScholarCross Ref
- T. K. Anandalaskhmi, S. Sathiakumar, and N. Parameswaran. 2013. Peak reduction algorithms for a smart community. In Proceedings of the 2013 International Conference on Energy Efficient Technologies for Sustainability (ICEETS’13). IEEE, 1113--1119. Google ScholarCross Ref
- D. R. Avery, P. F. McKay, and D. C. Wilson. 2007. Engaging the aging workforce: The relationship between perceived age similarity, satisfaction with coworkers, and employee engagement. J. Appl. Psychol. 92, 6 (2007), 1542--1556. Google ScholarCross Ref
- B. Bahmani, B. Moseley, A. Vattani, R. Kumar, and S. Vassilvitskii. 2012. Scalable k-means++. Proc. VLDB Endow. 5, 7 (2012), 622--633.Google ScholarDigital Library
- J. A. Bargh, K. Y. A. McKenna, and G. M. Fitzsimons. 2002. Can you see the real me? activation and expression of the “true self” on the internet. J. Soc. Issues 58, 1 (2002), 33--48. Google ScholarCross Ref
- P. M. Blau, T. C. Blum, and J. E. Schwartz. 1982. Heterogeneity and intermarriage. Am. Sociol. Rev. 47, 1 (1982), 45--62. Google ScholarCross Ref
- A. H. Cheetham and J. E. Hazel. 1969. Binary (presence-absence) similarity coefficients. J. Paleontol. 43, 5 (1969), 1130--1136.Google Scholar
- F. Cicirelli, G. Fortino, A. Giordano, A. Guerrieri, G. Spezzano, and A. Vinci. 2016. On the design of smart homes: A framework for activity recognition in home environment. J. Med. Syst. 40, 9 (2016), 200. Google ScholarDigital Library
- D. Davis. 1981. Implications for interaction versus effectance as mediators of the similarity-attraction relationship. J. Exp. Soc. Psychol. 17, 1 (1981), 96--117. Google ScholarCross Ref
- J. M. Eger. 1997. The Smart Communities Guidebook, Report to the california department of transportation, SDSU International Center for Communications.Google Scholar
- G. Fortino, A. Guerrieri, W. Russo, and C. Savaglio. 2014. Integration of agent-based and cloud computing for the smart objects-oriented IoT. In Proceedings of the 2014 IEEE 18th International Conference on Computer Supported Cooperative Work in Design (CSCWD’14). 493--498. Google ScholarCross Ref
- Z. Huang. 1998. Extensions to the k-means algorithm for clustering large data sets with categorical values. Data Min. Knowl. Discov. 2, 3 (1998), 283--304. Google ScholarDigital Library
- G. Huerta-Canepa, S. Han, D. Lee, and B. Kim. 2013. A place-aware stereotypical trust supporting scheme. In Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom’13). IEEE, 821--828. Google ScholarDigital Library
- S. D. O. Ilboudo, I. Sombié, A. K. Soubeiga, and T. Dræbel. 2016. Facteurs influençant le refus de consulter au centre de santé dans la région rurale Ouest du Burkina Faso. Sante Publ. 28, 3 (2016), 391--397. Google ScholarCross Ref
- International Labour Office. 2012. International standard classification of occupations: ISCO-08. structure, group definitions and correspondence tables. Geneva.Google Scholar
- A. K. Jain. 2010. Data clustering: 50 years beyond K-means. Pattern Recogn. Lett. 31, 8 (2010), 651--666. Google ScholarDigital Library
- M. J. Kim, M. E. Cho, and H. H. Chae. 2014. A smart community for placemaking in housing complexes. J. Asian Arch. Build. Eng. 13, 3 (2014), 539--546. Google ScholarCross Ref
- R. Kraut, M. Patterson, V. Lundmark, S. Kiesler, T. Mukopadhyay, and W. Scherlis. 1998. Internet paradox: A social technology that reduces social involvement and psychological well-being? Am. Psychol. 53, 9 (1998), 1017--1031. Google ScholarCross Ref
- X. Li, R. Lu, X. Liang, X. Shen, J. Chen, and X. Lin. 2011. Smart community: An internet of things application. IEEE Commun. Mag. 49, 11 (2011), 68--75. Google ScholarCross Ref
- X. Liang, K. Zhang, R. Lu, X. Lin, and X. Shen, 2013. EPS: An efficient and privacy-preserving service searching scheme for smart community. IEEE Sens. J. 13, 10 (2013), 3702--3710. Google ScholarCross Ref
- H. Lindskog. 2004. Smart communities initiatives. In Proceedings of the 3rd ISOneWorld Conference. 16.Google Scholar
- D. H. McKnight, L. L. Cummings, and N. L. Chervany. 1998. Initial trust formation in new organizational relationships R. M. Kramer, ed. Acad. Manage. Rev. 23, 3 (1998), 473--490.Google ScholarCross Ref
- R. Miller. 2010. Intimate Relationships.Google Scholar
- C. J. Morgan, 1979. Eskimo hunting groups, social kinship, and the possibility of kin selection in humans. Ethol. Sociobiol. 1, 1 (1979), 83--86. Google ScholarCross Ref
- J. Phelps, G. Nowak, and E. Ferrell. 2000. Privacy concerns and consumer willingness to provide personal information. J. Publ. Policy Market. 19, 1 (2000), 27--41. Google ScholarCross Ref
- D. Quercia and L. Capra. 2009. FriendSensing. In Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys’09). ACM, New York, NY, 273. Google ScholarDigital Library
- D. Sculley. 2010. Web-scale k-means clustering. Proceedings of the 19th International Conference on World wide web WWW 10, 1177. Google ScholarDigital Library
- C. E. Shannon. 2001. A mathematical theory of communication, P. L. Luisi and P. Stano, eds. ACM SIGMOBILE Mobile Comput. Commun. Rev. 5, 1, 3.Google ScholarDigital Library
- H. Son et al. 2015. A distributed middleware for a smart home with autonomous appliances. In Proceedings of theInternational Computer Software and Applications Conference. IEEE, 23--32. Google ScholarDigital Library
- H. Sundaram, Y.-R. Lin, M. De Choudhury, and A. Kelliher. 2012. Understanding community dynamics in online social networks: A multidisciplinary review. IEEE Sign. Process. Mag. 29, 2 (2012), 33--40. Google ScholarCross Ref
- Z. Wang, J. Liao, Q. Cao, H. Qi, and Z. Wang, 2015. Friendbook: A semantic-based friend recommendation system for social networks. IEEE Trans. Mobile Comput. 14, 3 (2015), 538--551. Google ScholarDigital Library
- F. Xia and J. Ma. 2011. Building smart communities with cyber-physical systems. In Proceedings of the 1st International Symposium on From Digital Footprints to Social and Community Intelligence (SCI’11). 1. Google ScholarDigital Library
- C. Zhou, D. Frankowski, P. Ludford, S. Shekhar, and L. Terveen. 2007. Discovering personally meaningful places. ACM Trans. Inf. Syst. 25, 3 (2007), 12. Google ScholarDigital Library
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