Abstract
Human existence is getting more sophisticated and better in many areas due to remarkable advances in the fields of automation. Automated systems are favored over manual ones in the current environment. Home Automation is becoming more popular in this scenario, as people are drawn to the concept of a home environment that can automatically satisfy users’ requirements. The key challenges in an intelligent home are intelligent decision making, location-aware service, and compatibility for all users of different ages and physical conditions. Existing solutions address just one or two of these challenges, but smart home automation that is robust, intelligent, location-aware, and predictive is needed to satisfy the user's demand. This paper presents a location-aware intelligent Received Signal Strength Indicator (RSSI) based home automation system (IRHA) that uses Wi-Fi signals to detect the user's location and control the appliances automatically. The fingerprinting method is used to map the Wi-Fi signals for different rooms, and the machine learning method, such as Decision Tree, is used to classify the signals for different rooms. The machine learning models are then implemented in the ESP32 microcontroller board to classify the rooms based on the real-time Wi-Fi signal, and then the result is sent to the main control board through the ESP32 MAC communication protocol to control the appliances automatically. The proposed method has achieved 97% accuracy in classifying the users’ location.
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References
A. Alheraish, Design and implementation of home automation system. IEEE Trans. Consum. Electron. 50(4), 1087–1092 (2004). https://doi.org/10.1109/tce.2004.1362503
H. Jiang, Z. Han, P. Scucces, S. Robidoux, Y. Sun, Voice-activated environmental control system for persons with disabilities, in Proceedings of the IEEE 26th Annual Northeast Bioengineering Conference (Cat. No.00CH37114) (n.d.). https://doi.org/10.1109/nebc.2000.842432
K. Baraka, M. Ghobril, S. Malek, R. Kanj, A. Kayssi, Low cost arduino/android-based energy-efficient home automation system with smart task scheduling, in 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks (2013). https://doi.org/10.1109/cicsyn.2013.47
M. Zamora-Izquierdo, J. Santa, A. Gomez-Skarmeta, An integral and networked home automation solution for indoor ambient intelligence. IEEE Pervasive Comput. 9(4), 66–77 (2010). https://doi.org/10.1109/mprv.2010.20
I. Froiz-Míguez, T. Fernández-Caramés, P. Fraga-Lamas, L. Castedo, Design, implementation and practical evaluation of an IoT home automation system for fog computing applications based on MQTT and ZigBee-WiFi sensor nodes. Sensors 18(8), 2660 (2018). https://doi.org/10.3390/s18082660
Z. Li, M. Song, L. Gao, Design of smart home system based on Zigbee. Appl. Mech. Mater. 635–637, 1086–1089 (2014). https://doi.org/10.4028/www.scientific.net/amm.635-637.1086
Structuring and design of home automation system using IOT 4(5), 200–206 (2018). https://doi.org/10.23883/ijrter.2018.4368.gjcfn
M. Helo, A. Shaker, L. Abdul-Rahaim, Design and Implementation a cloud computing system for smart home automation. Webology 18(SI05), 879–893 (2021). https://doi.org/10.14704/web/v18si05/web18269
D. Choudhury, Real time and low cost smart home automation system using ınternet of things environment. Int. J. Comput. Sci. Eng. 7(4), 225–229 (2019). https://doi.org/10.26438/ijcse/v7i4.225229
B. Davidovic, A. Labus, A smart home system based on sensor technology. Facta Univ. Ser. Electron. Energ. 29(3), 451–460 (2016). https://doi.org/10.2298/fuee1603451d
W. Jabbar, T. Kian, R. Ramli, S. Zubir, N. Zamrizaman, M. Balfaqih et al., Design and fabrication of smart home with internet of things enabled automation system. IEEE Access 7, 144059–144074 (2019). https://doi.org/10.1109/access.2019.2942846
Census.gov (2022). Retrieved 12 Jan 2022, from https://www.census.gov/prod/2002pubs/censr-4.pdf
Un.org (2022). Retrieved 12 Jan 2022, from https://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013.pdf
J. Sosa, J. Joglekar, Smart home automation using fuzzy logic and ınternet of things technologies, in International Conference on Inventive Computation Technologies (Springer, Cham, 2019), pp. 174–182
S. Sanket, J. Thakor, P. Kapoor, K. Pande, S.V. Shrivastav, R. Maheswari, Relative hand movement and voice control based home automation and PC, in International Conference on Inventive Computation Technologies (Springer, Cham, 2019), pp. 232–239
M.R. Reddy, P. Sai Siddartha Reddy, S.A.S. Harsha, D. Vishnu Vashista, Voice controlled home automation using Blynk, IFTTT with live feedback, in Inventive Communication and Computational Technologies (Springer, Singapore, 2020), pp. 619–634
S. Shakya, L.N. Pulchowk, Intelligent and adaptive multi-objective optimization in WANET using bio inspired algorithms. J. Soft Comput. Paradigm (JSCP) 2(01), 13–23 (2020)
S. Smys, C. Vijesh Joe, Metric routing protocol for detecting untrustworthy nodes for packet transmission. J. Inf. Technol. 3(02), 67–76 (2021)
I.J. Jacob, P. Ebby Darney, Artificial bee colony optimization algorithm for enhancing routing in wireless networks. J. Artif. Intell. 3(01), 62–71 (2021)
P. Munihanumaiah, H. Sarojadevi, Design and development of network-based consumer applications on Android, in 2014 International Conference on Computing for Sustainable Global Development (INDIACom) (2014). https://doi.org/10.1109/indiacom.2014.6828089
M. Asadullah, K. Ullah, Smart home automation system using bluetooth technology, in 2017 International Conference on Innovations in Electrical Engineering and Computational Technologies (ICIEECT) (2017). https://doi.org/10.1109/icieect.2017.7916544
Smart home automation using Arduino integrated with bluetooth and GSM. Int. J. Innov. Technol. Explor. Eng. 8(11S), 1140–1143 (2019). https://doi.org/10.35940/ijitee.k1230.09811s19
M. Brunato, C.K. Kallo, Transparent location fingerprinting for wireless services (2002)
H. Oh, W. Seo, Development of a decision tree analysis model that predicts recovery from acute brain injury. Jpn. J. Nurs. Sci. 10(1), 89–97 (2012). https://doi.org/10.1111/j.1742-7924.2012.00215.x
G. Zhou, L. Wang, Co-location decision tree for enhancing decision-making of pavement maintenance and rehabilitation. Transp. Res. Part C: Emerg. Technol. 21(1), 287–305 (2012). https://doi.org/10.1016/j.trc.2011.10.007
S. Sohn, J. Kim, Decision tree-based technology credit scoring for start-up firms: Korean case. Expert Syst. Appl. 39(4), 4007–4012 (2012). https://doi.org/10.1016/j.eswa.2011.09.075
J. Cho, P. Kurup, Decision tree approach for classification and dimensionality reduction of electronic nose data. Sens. Actuators B: Chem. 160(1), 542–548 (2011). https://doi.org/10.1016/j.snb.2011.08.027
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Mozumder, S.A., Sharifuzzaman Sagar, A.S.M. (2022). IRHA: An Intelligent RSSI Based Home Automation System. In: Karuppusamy, P., García Márquez, F.P., Nguyen, T.N. (eds) Ubiquitous Intelligent Systems. ICUIS 2021. Smart Innovation, Systems and Technologies, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-2541-2_14
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DOI: https://doi.org/10.1007/978-981-19-2541-2_14
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