## 2 Related Work
By integrating multiple parameters, the proposed system can offer more accurate and personalized insights into sleep quality, generating personalized sleep recommendations. This non-wearable WSN in homes ensures a seamless and convenient monitoring experience, offering valuable improvements over existing mechanisms. Overall, the proposed system represents a promising and innovative approach to sleep monitoring, enhancing user comfort and providing actionable recommendations for better sleep quality. Table 3 provides a comprehensive comparison of various state-of-the-art algorithms for energy-saving in remote monitoring of older adults at home. It highlights the different types of algorithms, their impacting metrics, and the percentage of global consumption reduction they can achieve. The algorithms are classified based on their type, which includes sleep-based, tree-based, collaborative, multi-objective, cluster-based, threshold-based, and reactive mode based. The impacting metrics of the algorithms include energy efficiency, network lifetime, latency, QoS, and routing efficiency. The percentage of global consumption reduction ranges from 15% to 60%. Overall, the table aims to provide insights into the different algorithms that can be used to enhance energy efficiency in remote monitoring systems, and their effectiveness in achieving energy consumption reduction. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Metrics** & **System Configuration** & **Application Domain** & **Brief Description** \\ \hline
[34] & Sleep efficiency, sleep latency, wake after sleep onset, total sleep time & Wearabletigraphy device + polysomography & Independent living for older adults & A comparison of using wearable activity devices and polysomography to track sleep patterns in older adults living independently. \\ \hline
[35] & Sleep efficiency, sleep latency, wake after sleep onset, total sleep time & Non-contact sleep monitoring system & Independent living for older adults & An assessment of using a non-contact sleep monitoring system to track sleep patterns in older adults living independently. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._ \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Mechanism** & **Differences** & **Improvements** & **System Type** \\ \hline Roll-over detection [36] & Measures body movement. & Detects sleep stages. & Wearable \\ Wearable heart rate sensor [37] & Tracks heart rate. & Assesses stress levels. & Wearable \\ Real-time wearable system [38] & Measures blood oxygen levels. & Monitors sleep apnea. & Wearable \\ IoT-assisted wearable sensor [39] & Records brain activity. & Analyzes sleep patterns. & Wearable \\ Non-wearable IoT-based smart ambient [40] & Captures visual information. & Observes sleep behavior. & Non-wearable \\ Internet of Intelligent Things [41] & Records ambient sounds. & Monitors snoring. & Non-wearable \\ \hline
**Our proposed system** & \(\bullet\) & Monitors luminosity, noise, movement, etc. & \(\bullet\) & Non-invasive, no need for wearables. \\
**(Non-invasive WSN)** & \(\bullet\) & Generates personalized sleep recommendations. & \(\bullet\) & Comprehensive monitoring of sleep environment and body parameters. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of Sleep Monitoring Mechanisms.