Development of a holistic methodology for the organization of smart home systems in the framework of the "Internet of Things" paradigm.
Abstract
The modern approaches used in the hardware and software platforms of home automation systems such as smart home in the framework of the general concept of the "Internet of Things" are discussed. To organize the interaction between elements of the home automation platform, it was proposed to use probabilistic-temporal models, in particular, a conditional random field model and neural network prediction algorithms. A universal scheme for the organization, control and management of sensors, controllers and actuators of the smart home system has been built. The proposed basic approaches for the introduction of probability-time models for the construction of home automation neural network algorithms. A mathematical model of the neural network algorithm for classifying of the input information signals patterns received from a network of sensors is constructed.
References
Yoza, A., Uchida, K., Yona, A., & Senju, T. (2018). Optimal Operation Method of Smart House by Controllable Loads based on Smart Grid Topology. Energyo. doi: 10.1515/energyo.0034.00170.
Li, Y., Zhang, F., & Yang, Y. (2019). Smart House Control System Controlled by Brainwave. 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS). doi: 10.1109/icitbs.2019.00134.
Alquthami, T., & Meliopoulos, A. P. S. (2018). Smart House Management and Control Without Customer Inconvenience. IEEE Transactions on Smart Grid, 9(4), 2553–2562. doi: 10.1109/tsg.2016.2614708.
Radu, M. (2016). Reliability analysis of smart house system. 2016 International Energy and Sustainability Conference (IESC). doi: 10.1109/iesc.2016.7569503
Bhaduri, K., & Stolpe, M. (2012). Distributed Data Mining in Sensor Networks. Managing and Mining Sensor Data, 211–236. doi: 10.1007/978-1-4614-6309-2_8.
Yates, D. J., & Xu, J. (2010). Sensor Field Resource Management for Sensor Network Data Mining. Intelligent Techniques for Warehousing and Mining Sensor Network Data, 280–304. doi: 10.4018/978-1-60566-328-9.ch013.
Wu, Y., & Rowe, A. (2011). Logic-Based Programming for Wireless Sensor-Activator Networks. 2011 IEEE/ACM Second International Conference on Cyber-Physical Systems. doi: 10.1109/iccps.2011.31.
Autexier, S., & Hutter, D. (2015). SHIP - A Logic-Based Language and Tool to Program Smart Environments. Logic-Based Program Synthesis and Transformation Lecture Notes in Computer Science, 313-328.
Ahmadi, H., & Bouallegue, R. (2015). Comparative study of learning-based localization algorithms for Wireless Sensor Networks: Support Vector regression, Neural Network and Naïve Bayes. 2015 International Wireless Communications and Mobile Computing Conference (IWCMC). doi: 10.1109/iwcmc.2015.7289314.
Jing, C., & Jingqi, F. (2012). Fire Alarm System Based on Multi-Sensor Bayes Network. Procedia Engineering, 29, 2551–2555.
Qihua, W., Ge, G., Lijie, C., & Xufeng, X. (2015). Scheduling strategy for Hidden Markov Model in wireless sensor network. 2015 34th Chinese Control Conference (CCC). doi: 10.1109/chicc.2015.7260879.
Zhang, C., & Zhang, L. (2013). Activity Recognition in Smart Homes Based on Second-Order Hidden Markov Model. International Journal of Smart Home, 7(6), 237-244. doi:10.14257/ijsh.2013.7.6.23
Luo, R., Min, H., & Lin, S. (2011). Joint Conditional Random Fields for Multi-object Tracking with a Mobile Robot. Robot, 33(3), 279–286. doi: 10.3724/sp.j.1218.2011.00279.
Junejo, I. (2010). Learning Self-Similarities for Action Recognition Using Conditional Random Fields. Bayesian Network. doi: 10.5772/46965.
Liu, X., Jiang, Y., & Zhang, T. (2016). Temperature and Humidity Independent Control (Thic) of Air-conditioning System. Berlin: Springer Berlin.
Bruno, F. (2010). Testing of an Evaporative Cooling System That Supplies Air Near the Dew Point Temperature. Proceedings of the EuroSun 2010 Conference. doi: 10.18086/eurosun.2010.10.09.
Kareem, B. (2018). Experimental and Theoretical Study of Dew Point Evaporative Cooling System Suitable for Erbil Climate. Polytechnic Journal, 8(2), 102–118. doi: 10.25156/ptj.2018.8.2.205.
Dean, J., Herrmann, L., Kozubal, E., Geiger, J., Eastment, M., & Slayzak, S. (2012). Dew Point Evaporative Comfort Cooling: Report and Summary Report. doi: 10.2172/1060597.
Simic, D., Kral, C., & Pirker, F. (2005). Simulation of the cooling circuit with an electrically operated water pump. 2005 IEEE Vehicle Power and Propulsion Conference. doi: 10.1109/vppc.2005.1554567.
Balasubramanian, K., & Cellatoglu, A. (2010). Selected Home Automation and Home Security Realizations: An Improved Architecture. Smart Home Systems. doi: 10.5772/8408.
Domb, M. (2019). Smart Home Systems Based on Internet of Things. IoT and Smart Home Automation [Working Title]. doi: 10.5772/intechopen.84894.
Papadopoulos, H., Andreou, A. S., Iliadis, L., & Maglogiannis, I. (2016). Artificial Intelligence Applications and Innovations 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30 -- October 2, 2013, Proceedings. Berlin: Springer Berlin.
Abstract views: 13 PDF Downloads: 6