Research Article
BibTex RIS Cite

Artificial Intelligence Helps Protect Smart Homes against Thieves

Year 2020, Volume: 11 Issue: 3, 945 - 952, 30.09.2020
https://doi.org/10.24012/dumf.700311

Abstract

Interaction with the environments in which humans live is increasing more and more, and Artificial Intelligence (AI) offers significant contributions to this. Although the topic of smart homes has attracted a great deal of attention from researchers, the AI-based application in this area is still in its infancy. In this study, a home security automation system, which is quite simple, but smart and AI-based, is proposed. When the home-dwellers were not at home, the home lighting system tried to be managed with AI at night, as if life was still there. The AI-based smart home physical design was done using Arduino equipment and was tried to be adapted to the real-life environment with software support. As if there was someone at home, a special dataset, which was consisted of nine inputs, one output vector and about 5500 samples was created to turn on/off the home lights in a manner suitable for night life. The home lighting system was successfully managed using an AI-based system that learns nightlife lighting habits. The proposed system performance was tested in support of commonly used machine learning classification algorithms such as Multi-layer perceptron (MLP), Linear support vector machine (L-SVM), Gaussian Naive Bayes (NB), and linear discriminant analysis (LDA). The accuracy values of MLP, L-SVM and NB algorithms were 96.69%, 94.98% and 91.23%, respectively. Our results show that a home with AI could be safer and more secure against theft.

References

  • 1. Benjamin K., Sovacool, Dylan D., Furszyfer DR. Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies, Renewable and Sustainable Energy Reviews, Volume 120, 2020, pp.1-20.
  • 2. Skn, H., Kalkan, H., Cetili, B. Classification of physical activities using accelerometer signals. In: Signal Processing and Communications Applications Conference, Mugla,Turkey,2012, pp.1-4.
  • 3. Gne, H., Orta, E., Akda, D. Developing synthetic data generation software for artificial intelligence techniques used in smart home systems. Journal of Balikesir University Institute of Science and Technology, 18(2):1-11,2016.
  • 4. Gariba, D., Pipaliya, B. Modelling human behaviour in smart home energy management systems via machine learning techniques. In: Proceddings of 2016 International automatic control conference,Taichung, Taiwan, 2016, pp. 53-58.
  • 5. Dixit, A., Naik, A. Use of prediction algorithms in smart homes. International Journal of Machine Learning and Computing, 4:157-162, 2014.
  • 6. Collado-Villaverde, A., R-Moreno, MD., Barrero, DF., Rodriguez, D. Machine learning approach to detect falls on elderly people using sound. In: Benferhat S., Tabia K., Ali M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE. Lecture Notes in Computer Science, Springer, Cham, 2017, pp. 149-158.
  • 7. Alshammari, T., Alshammari, N., Sedky, M., Howard, C. Evaluating machine learning techniques for activity classification in smart home environments. World Academy of Science, Engineering and Technology International Journal of Information and Communication Engineering, 12 (2):58-64, 2018.
  • 8. Alhafidh, BAH., Allen, WH. Comparison and Performance Analysis of Machine Learning Algorithms for the Prediction of Human Actions in a Smart Home Environment. In:Proceedings of the International Conference on Compute and Data Analysis, New York, USA, 2017, pp. 54-59.
  • 9. Dahmen, J., B. Thomas, DJ. Cook,X.Wang, Activity Learning as a Foundation for Security Monitoring in Smart Homes. Sensors ,17(4):pp. 1-17, 2017.
  • 10. Cumin, J., Lefebvre, G., Ramparany, F., Crowley, FL. Human activity recognition using place-based decision fusion in smart homes. In:International and Interdisciplinary Conference on Modeling and Using Context, Paris, France, 2017, pp.137-150.
  • 11. Singh, D., Merdiva, E., Hanke, S., Kropf, J., Geist, M., Holzinger, A. Convolutional and recurrent neural networks for activity recognition in smart environment. In:Towards integrative machine learning and knowledge extraction, Banff, AB, Canada, 2015, pp.194-205.
  • 12. Jebakumari, VS., Shanthi, D., Sridevi, S., Meha, P. Performance evaluation of various classification algorithms for the diagnosis of Parkinson’s disease. In:Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputhur, India, 2017, pp.1-7.
  • 13. Cover, TM., Hart, PE. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, IT13(1):2127, 1967.
  • 14. Bhatia, N. Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security, 8(2):302-305, 2010.
  • 15. Mohana, TK., Lalitha, V., Kusuma, L., Rahul, N., Mohan, M. Various Distance Metric Methods for Query Based Image Retrieva. International Journal of Engineering Science and Computing, 7(3):5818-5821, 2017.
  • 16. Zhou, ZH. Ensemble methods foundations and algorithms, Boca Raton, FL,2012.
  • 17. Marshald, S. Machine learning an algorithmic perspectives, Boca Raton, FL, 2015.
  • 18. Rogers, S., Girolami, M. A first course in machine learnings, Boca Raton, FL, 2012.
  • 19. Sarkar, D., Bali, R., Sharma, T. Practical Machine Learning with Python, Bangalore, Karnataka, India, 2018.
  • 20. Vijayarani, S., Dhayanand, S. Data mining classification algorithms for kidney disease prediction, International Journal on Cybernetics & Informatics (IJCI), 4(4):13-25, 2015.
  • 21. Mazzolenia, M., Previdia, F., Bonfiglio, NS. Classification algorithms analysis for brain computer interface in drug craving therapy. Biomedical Signal Processing and Control 2017, https://doi.org/10.1016/j.bspc.2017.01.011.
  • 22. Takci, H. Improvement of heart attack prediction by the feature selection methods, Turk J Elec Eng & Comp Sci,26:1-10, 2018.

Artificial Intelligence Helps Protect Smart Homes against Thieves

Year 2020, Volume: 11 Issue: 3, 945 - 952, 30.09.2020
https://doi.org/10.24012/dumf.700311

Abstract

Interaction with the environments in which humans live is increasing more and more, and Artificial Intelligence (AI) offers significant contributions to this. Although the topic of smart homes has attracted a great deal of attention from researchers, the AI-based application in this area is still in its infancy. In this study, a home security automation system, which is quite simple, but smart and AI-based, is proposed. When the home-dwellers were not at home, the home lighting system tried to be managed with AI at night, as if life was still there. The AI-based smart home physical design was done using Arduino equipment and was tried to be adapted to the real-life environment with software support. As if there was someone at home, a special dataset, which was consisted of nine inputs, one output vector and about 5500 samples was created to turn on/off the home lights in a manner suitable for night life. The home lighting system was successfully managed using an AI-based system that learns nightlife lighting habits.
The proposed system performance was tested in support of commonly used machine learning classification algorithms such as Multi-layer perceptron (MLP), Linear support vector machine (L-SVM), Gaussian Naive Bayes (NB), and linear discriminant analysis (LDA). The accuracy values of MLP, L-SVM and NB algorithms were 96.69%, 94.98% and 91.23%, respectively. Our results show that a home with AI could be safer and more secure against theft.

References

  • 1. Benjamin K., Sovacool, Dylan D., Furszyfer DR. Smart home technologies in Europe: A critical review of concepts, benefits, risks and policies, Renewable and Sustainable Energy Reviews, Volume 120, 2020, pp.1-20.
  • 2. Skn, H., Kalkan, H., Cetili, B. Classification of physical activities using accelerometer signals. In: Signal Processing and Communications Applications Conference, Mugla,Turkey,2012, pp.1-4.
  • 3. Gne, H., Orta, E., Akda, D. Developing synthetic data generation software for artificial intelligence techniques used in smart home systems. Journal of Balikesir University Institute of Science and Technology, 18(2):1-11,2016.
  • 4. Gariba, D., Pipaliya, B. Modelling human behaviour in smart home energy management systems via machine learning techniques. In: Proceddings of 2016 International automatic control conference,Taichung, Taiwan, 2016, pp. 53-58.
  • 5. Dixit, A., Naik, A. Use of prediction algorithms in smart homes. International Journal of Machine Learning and Computing, 4:157-162, 2014.
  • 6. Collado-Villaverde, A., R-Moreno, MD., Barrero, DF., Rodriguez, D. Machine learning approach to detect falls on elderly people using sound. In: Benferhat S., Tabia K., Ali M. (eds) Advances in Artificial Intelligence: From Theory to Practice. IEA/AIE. Lecture Notes in Computer Science, Springer, Cham, 2017, pp. 149-158.
  • 7. Alshammari, T., Alshammari, N., Sedky, M., Howard, C. Evaluating machine learning techniques for activity classification in smart home environments. World Academy of Science, Engineering and Technology International Journal of Information and Communication Engineering, 12 (2):58-64, 2018.
  • 8. Alhafidh, BAH., Allen, WH. Comparison and Performance Analysis of Machine Learning Algorithms for the Prediction of Human Actions in a Smart Home Environment. In:Proceedings of the International Conference on Compute and Data Analysis, New York, USA, 2017, pp. 54-59.
  • 9. Dahmen, J., B. Thomas, DJ. Cook,X.Wang, Activity Learning as a Foundation for Security Monitoring in Smart Homes. Sensors ,17(4):pp. 1-17, 2017.
  • 10. Cumin, J., Lefebvre, G., Ramparany, F., Crowley, FL. Human activity recognition using place-based decision fusion in smart homes. In:International and Interdisciplinary Conference on Modeling and Using Context, Paris, France, 2017, pp.137-150.
  • 11. Singh, D., Merdiva, E., Hanke, S., Kropf, J., Geist, M., Holzinger, A. Convolutional and recurrent neural networks for activity recognition in smart environment. In:Towards integrative machine learning and knowledge extraction, Banff, AB, Canada, 2015, pp.194-205.
  • 12. Jebakumari, VS., Shanthi, D., Sridevi, S., Meha, P. Performance evaluation of various classification algorithms for the diagnosis of Parkinson’s disease. In:Intelligent Techniques in Control, Optimization and Signal Processing (INCOS), Srivilliputhur, India, 2017, pp.1-7.
  • 13. Cover, TM., Hart, PE. Nearest neighbor pattern classification. IEEE Transactions on Information Theory, IT13(1):2127, 1967.
  • 14. Bhatia, N. Survey of nearest neighbor techniques. International Journal of Computer Science and Information Security, 8(2):302-305, 2010.
  • 15. Mohana, TK., Lalitha, V., Kusuma, L., Rahul, N., Mohan, M. Various Distance Metric Methods for Query Based Image Retrieva. International Journal of Engineering Science and Computing, 7(3):5818-5821, 2017.
  • 16. Zhou, ZH. Ensemble methods foundations and algorithms, Boca Raton, FL,2012.
  • 17. Marshald, S. Machine learning an algorithmic perspectives, Boca Raton, FL, 2015.
  • 18. Rogers, S., Girolami, M. A first course in machine learnings, Boca Raton, FL, 2012.
  • 19. Sarkar, D., Bali, R., Sharma, T. Practical Machine Learning with Python, Bangalore, Karnataka, India, 2018.
  • 20. Vijayarani, S., Dhayanand, S. Data mining classification algorithms for kidney disease prediction, International Journal on Cybernetics & Informatics (IJCI), 4(4):13-25, 2015.
  • 21. Mazzolenia, M., Previdia, F., Bonfiglio, NS. Classification algorithms analysis for brain computer interface in drug craving therapy. Biomedical Signal Processing and Control 2017, https://doi.org/10.1016/j.bspc.2017.01.011.
  • 22. Takci, H. Improvement of heart attack prediction by the feature selection methods, Turk J Elec Eng & Comp Sci,26:1-10, 2018.
There are 22 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Zeydin Pala

Orhan Özkan This is me 0000-0001-9502-8222

Publication Date September 30, 2020
Submission Date March 7, 2020
Published in Issue Year 2020 Volume: 11 Issue: 3

Cite

IEEE Z. Pala and O. Özkan, “Artificial Intelligence Helps Protect Smart Homes against Thieves”, DUJE, vol. 11, no. 3, pp. 945–952, 2020, doi: 10.24012/dumf.700311.
DUJE tarafından yayınlanan tüm makaleler, Creative Commons Atıf 4.0 Uluslararası Lisansı ile lisanslanmıştır. Bu, orijinal eser ve kaynağın uygun şekilde belirtilmesi koşuluyla, herkesin eseri kopyalamasına, yeniden dağıtmasına, yeniden düzenlemesine, iletmesine ve uyarlamasına izin verir. 24456