Context-Aware Reasoning Framework for Multi-user Recommendations in Smart Home
This paper introduces a context-aware reasoning framework that adapts to the needs and preferences of inhabitants continuously to provide contextually relevant recommendations to the group of users in a smart home environment. User’s activity and mobility plays a crucial role in defining various contexts in and around the home. The observation data acquired from disparate sensors, called user’s context, is interpreted semantically to implicitly disambiguate the users that are being recommended to. The recommendations are provided based on the relationship that exist among multiple users and the decision is made as per the preference or priority. The proposed approach makes extensive use of multimedia ontology in the life cycle of situation recognition to explicitly model and represent user’s context in smart home. Further, dynamic reasoning is exploited to facilitate context-aware situation tracking and intelligently recommending appropriate actions which suit the situation. We illustrate use of the proposed framework for Smart Home use-case.
KeywordsContext-aware Multimedia ontology Dynamic Bayesian Network (DBN) Internet of Things (IoT) Recommendations
- 2.De Paola, A., Ferraro, P., Gaglio, S., Lo Re, G.: Context-awareness for multi-sensor data fusion in smart environments. In: Adorni, G., Cagnoni, S., Gori, M., Maratea, M. (eds.) AI*IA 2016. LNCS (LNAI), vol. 10037, pp. 377–391. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-49130-1_28CrossRefGoogle Scholar
- 3.Mallik, A., Tripathi, A., Kumar, R., Chaudhury, S., Sinha, K.: Ontology based context aware situation tracking. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 687–692. IEEE (2015)Google Scholar
- 4.Mihajlovic, V., Petkovic, M.: Dynamic Bayesian networks: a state of the art. University of Twente Document Repository (2001)Google Scholar
- 5.Nguyen, N., Venkatesh, S., Bui, H.: Recognising behaviours of multiple people with hierarchical probabilistic model and statistical data association. In: BMVC 2006: Proceedings of the 17th British Machine Vision Conference, pp. 1239–1248. British Machine Vision Association (2006)Google Scholar
- 6.Nguyen, N.T., Phung, D.Q., Venkatesh, S., Bui, H.: Learning and detecting activities from movement trajectories using the hierarchical hidden Markov model. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005), vol. 2, pp. 955–960. IEEE (2005)Google Scholar