Document Type : Technical Paper


Computer Engineering Department, Imam Reza International University, Mashhad, Iran.



One of the crucial applications of IoT is developing smart cities via this technology. Smart cities are made up of smart components such as smart homes. In smart homes, a variety of sensors are used for making the environment smart, and the smart things, in such homes, can be used for detecting the activities of the people inside them. Detecting the activities of the smart homes’ users may include the detection of activities such as making food or watching TV. Detecting the activities of residents of smart homes can tremendously help the elderly or take care of the kids or, even, promote security issues. The information collected by the sensors could be used for detecting the kind of activities; however, the main challenge is the poor precision of most of the activity detection methods. In the proposed method, for reducing the clustering error of the data mining techniques, a hybrid learning approach is presented using Water Strider Algorithm. In the proposed method, Water Strider Algorithm can be used in the feature extraction phase and exclusively extract the main features for machine learning. The analysis of the proposed method shows that it has precision of 97.63 %, accuracy of 97. 12 %, and F1 index of 97.45 %. It, in comparison with similar algorithms (such as Butterfly Optimization Algorithm, Harris Hawks Optimization Algorithm, and Black Widow Optimization Algorithm), has higher precision while detecting the users’ activities.


[1] M. Lupión, J. L. Redondo, J. F. Sanjuan, and P. M. Ortigosa, “Deployment of an IoT Platform for Activity Recognition at the UAL’s Smart Home,” in International Symposium on Ambient Intelligence, 2020, pp. 82–92.
[2] M.-D. González-Zamar, E. Abad-Segura, E. Vázquez-Cano, and E. López-Meneses, “IoT technology applications-based smart cities: Research analysis,” Electronics, vol. 9, no. 8, p. 1246, 2020.
[3] P. Agarwal and M. Alam, “Investigating IoT middleware platforms for smart application development,” Smart Cities—Opportunities and Challenges; Springer: Singapore, pp. 231–244, 2020.
[4] N. Hossein Motlagh, M. Mohammadrezaei, J. Hunt, and B. Zakeri, “Internet of Things (IoT) and the energy sector,” Energies, vol. 13, no. 2, p. 494, 2020.
[5] A. Mousavi, A. Sheikh Mohammad Zadeh, M. Akbari, and A. Hunter, “A New Ontology-Based Approach for Human Activity Recognition from GPS Data,” J. AI Data Min., vol. 5, no. 2, pp. 197–210, 2017.
[6] K. S. Gayathri, K. S. Easwarakumar, and S. Elias, “Fuzzy ontology based activity recognition for assistive health care using smart home,” Int. J. Intell. Inf. Technol., vol. 16, no. 1, pp. 17–31, 2020.
[7] B. Reeder, J. Chung, K. Lyden, J. Winters, and C. M. Jankowski, “Older women’s perceptions of wearable and smart home activity sensors,” Informatics Heal. Soc. Care, vol. 45, no. 1, pp. 96–109, 2020.
[8] H. M. Do, M. Pham, W. Sheng, D. Yang, and M. Liu, “RiSH: A robot-integrated smart home for elderly care,” Rob. Auton. Syst., vol. 101, pp. 74–92, 2018.
[9] N. E. Kogan et al., “An early warning approach to monitor COVID-19 activity with multiple digital traces in near real time,” Sci. Adv., vol. 7, no. 10, p. eabd6989, 2021.
[10] D. Agrawal et al., “Enhancing smart home security using co-monitoring of iot devices,” in Companion of the 2020 ACM International Conference on Supporting Group Work, 2020, pp. 99–102.
[11] J. Kang, M. Kim, and J. H. Park, “A reliable TTP-based infrastructure with low sensor resource consumption for the smart home multi-platform,” Sensors, vol. 16, no. 7, p. 1036, 2016.
[12] A. A. Alani, G. Cosma, and A. Taherkhani, “Classifying imbalanced multi-modal sensor data for human activity recognition in a smart home using deep learning,” in 2020 International Joint Conference on Neural Networks (IJCNN), 2020, pp. 1–8.
[13] P. N. Huu, Q. T. Minh, and others, “An ANN-based gesture recognition algorithm for smart-home applications,” KSII Trans. Internet Inf. Syst., vol. 14, no. 5, pp. 1967–1983, 2020.
[14] S. B. ud din Tahir, A. Jalal, and M. Batool, “Wearable sensors for activity analysis using SMO-based random forest over smart home and sports datasets,” in 2020 3rd International Conference on Advancements in Computational Sciences (ICACS), 2020, pp. 1–6.
[15] X. Guo, Z. Shen, Y. Zhang, and T. Wu, “Review on the application of artificial intelligence in smart homes,” Smart Cities, vol. 2, no. 3, pp. 402–420, 2019.
[16] V. Ghasemi, A. Pouyan, and M. Sharifi, “A sensor-based scheme for activity recognition in smart homes using dempster-shafer theory of evidence,” J. AI Data Min., vol. 5, no. 2, pp. 245–258, 2017.
[17] M. Kaur, G. Kaur, P. K. Sharma, A. Jolfaei, and D. Singh, “Binary cuckoo search metaheuristic-based supercomputing framework for human behavior analysis in smart home,” J. Supercomput., vol. 76, no. 4, pp. 2479–2502, 2020.
[18] A. Kaveh and A. D. Eslamlou, “Water strider algorithm: A new metaheuristic and applications,” in Structures, 2020, vol. 25, pp. 520–541.
[19] L. G. Fahad and S. F. Tahir, “Activity recognition and anomaly detection in smart homes,” Neurocomputing, vol. 423, pp. 362–372, 2021.
[20] Y. Zhang, G. Tian, S. Zhang, and C. Li, “A knowledge-based approach for multiagent collaboration in smart home: From activity recognition to guidance service,” IEEE Trans. Instrum. Meas., vol. 69, no. 2, pp. 317–329, 2019.
[21] R. A. Hamad, A. S. Hidalgo, M.-R. Bouguelia, M. E. Estevez, and J. M. Quero, “Efficient activity recognition in smart homes using delayed fuzzy temporal windows on binary sensors,” IEEE J. Biomed. Heal. informatics, vol. 24, no. 2, pp. 387–395, 2019.
[22] S. Yan, K.-J. Lin, X. Zheng, and W. Zhang, “Using latent knowledge to improve real-time activity recognition for smart IoT,” IEEE Trans. Knowl. Data Eng., vol. 32, no. 3, pp. 574–587, 2019.
[23] M. Jethanandani, A. Sharma, T. Perumal, and J.-R. Chang, “Multi-label classification based ensemble learning for human activity recognition in smart home,” Internet of Things, vol. 12, p. 100324, 2020.
[24] Y. Hu, B. Wang, Y. Sun, J. An, and Z. Wang, “Graph-Based Semi-Supervised Learning for Activity Labeling in Health Smart Home,” IEEE Access, vol. 8, pp. 193655–193664, 2020.
[25] J. Guo, Y. Li, M. Hou, S. Han, and J. Ren, “Recognition of daily activities of two residents in a smart home based on time clustering,” Sensors, vol. 20, no. 5, p. 1457, 2020.
[26] H. Yang, S. Gong, Y. Liu, Z. Lin, and Y. Qu, “A multi-task learning model for daily activity forecast in smart home,” Sensors, vol. 20, no. 7, p. 1933, 2020.
[27] Y. Du, Y. Lim, and Y. Tan, “A Novel Human Activity Recognition and Prediction in Smart Home Based on Interaction,” Sensors, vol. 19, no. 20, 2019.
[28] Z. Fu, X. He, E. Wang, J. Huo, J. Huang, and D. Wu, “Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning,” Sensors, vol. 21, no. 3, p. 885, 2021.
[29] A. R. Javed, R. Faheem, M. Asim, T. Baker, and M. O. Beg, “A smartphone sensors-based personalized human activity recognition system for sustainable smart cities,” Sustain. Cities Soc., vol. 71, p. 102970, 2021.
[30] L. G. Fahad and S. F. Tahir, “Activity recognition in a smart home using local feature weighting and variants of nearest-neighbors classifiers,” J. Ambient Intell. Humaniz. Comput., vol. 12, no. 2, pp. 2355–2364, 2021.
[31] D. Das et al., “Explainable Activity Recognition for Smart Home Systems,” arXiv Prepr. arXiv2105.09787, 2021.