## 2 Related Work  
Wireless sensor networks have gained a lot of attention in recent years due to their potential in monitoring people in their homes. In a study, [PERSON] et al. [25] proposed a WSN-based monitoring system that utilizes machine learning algorithms to detect falls and abnormal events in the elderly population. The system consists of sensors placed around the home to detect motion, pressure and temperature changes, which are then analyzed using machine learning algorithms to detect abnormal events. The results showed that the proposed system was effective in detecting falls and abnormal events, with a high accuracy rate of 96%. Implementing and fine-tuning remote monitoring and assistance systems is a highly complex process; building smart homes is an attempt to solve it. Some projects based on these technologies test various methodologies that monitor the activities of residents [26]. Some new principles of monitoring activities of residents are related to the use of new piezoelectric resonators, which have a very low consumption of electricity, as shown in [27]. Through artificial intelligence techniques and system feedback learning from the environment, the aim is to improve the comfort, safety and productivity of the home's inhabitants. Some investigations base their scientific research structure on instrumented adaptations and the dimensions of a real house where environmental and human behavior data are collected [28; 29]. A group of volunteers was monitored and different strategies and technologies were evaluated. Other studies have advanced its adaptation in a group of more vulnerable people, the elderly. In this type of people, there is a subset that does not have widely monitored diseases and can continue to maintain their independence with a remote and periodic review. Researchers implement, test and validate various systems aimed at assisting older adults or people with special needs to enable them to live independently and with a high quality of life [30]. Another study by [PERSON] et al. [31] proposed a WSN-based system for monitoring people with cognitive impairments in their homes. The system uses sensors to detect activities of daily living (ADLs), such as cooking, bathing and dressing, and sends alerts to caregivers if any unusual activities are detected. The study showed that the proposed system was effective in detecting ADLs and could potentially improve the quality of life for people with cognitive impairments [32]. To optimize the energy consumption of WSNs in home applications, routing algorithms have been proposed. In a study, [PERSON] et al. [33] proposed an energy-efficient routing algorithm for WSNs in home applications. The algorithm utilizes a probabilistic model to predict the energy consumption of each node and chooses the route with the lowest predicted energy consumption. The results showed that the proposed algorithm could effectively reduce energy consumption in WSNs in home applications. In another study, [PERSON] et al. [34] proposed a multi-objective optimization algorithm for WSNs in home applications. The algorithm considers both energy consumption and data reliability as optimization objectives and selects the optimal route that balances both objectives. The results showed that the proposed algorithm could effectively optimize energy consumption and data reliability in WSNs in home applications [35]. The use of WSNs for monitoring people in their homes has shown promising results in improving the quality of life for elderly and disabled individuals. To optimize energy consumption in home applications, various routing algorithms have been proposed, such as the energy-efficient routing algorithm and the multi-objective optimization algorithm. These algorithms can effectively reduce energy consumption while ensuring data reliability in WSNs for home applications. These following schemes play an essential role, like the routing protocol.