WSAMLP: Water Strider Algorithm and Artificial Neural Network-based Activity Detection Method in Smart Homes

One of the crucial applications of IoT is to develop smart cities via this technology. Smart cities are made up of smart components such as smart homes. In such homes, a variety of sensors are used in order to make the environment smart, and the smart things in such homes can be used to detect the activities of the people inside them. Detecting the activities of the smart homes‟ users may include the detection of the activities such as making food or watching TV. Detecting the activities of the residents of smart homes can tremendously help the elderly or take care of the kids or even promote the security issues. The information collected by the sensors could be used to detect 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 the water strider algorithm. In the proposed method, this 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 a precision of 97.63%, an accuracy of 97.12%, and an 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 a higher precision while detecting the users‟ activities.