Data require compression [19] using symbolic aggregate approximation (SAX) techniques and piecewise aggregate approximation (PAA) to save transmission energy. Connecting to networks such as Wi-Fi direct, access points, or Internet gateway consumes more power [20]. To reduce power consumption, developers should keep communication modules in sleep mode and buffer data. When many requests arrive at the gateway at the same time, the gateway can analyze multiple requests and combine them before sending them to the device. Sending data has its own pattern, which can be predicted by using machine learning algorithms, helping to further reduce power consumption. Hidden Markov models can also be used to associate states with battery status and the size of data received to optimize the frequency of wake-up intervals [21]. The work just mentioned [18; 19; 20; 21] analyzed only a few factors in their power saving analyses. In the application of low power devices, there are multiple factors affecting power consumption. [PERSON]. [PERSON] et al. [22] compare the requirements of IP-support, mobility, availability, scalability, energy, and reliability of different available wireless technologies for a smart building. Among all other available wireless technologies, Wi-Fi has the highest IP support, mobility, and availability. Unfortunately, Wi-Fi is listed as having no support for energy saving compared to other wireless technologies. As battery technologies are evolving rapidly, it is difficult to select an appropriate battery size and to determine the optimal replacement or recharging period. Low power device design always involves tradeoffs between system performance and battery life. Extending battery life requires optimizing average energy consumption through design aspects of the system while maintaining expected performance. [PERSON] et al. (2019) compare the energy consumption of IEEE 802.15.4/e, Bluetooth low energy (BLE), IEEE 802.11 power saving mode, IEEE 802.11 ah, LoRa, and SIGFOX. The authors' main contribution is to help designers select the MAC parameters to optimize energy consumption in an application. [PERSON] et al. (2019) review design goals and techniques in different LPWA technologies. Researchers are continuously working on algorithms for optimizing battery life. [PERSON] (2018) compares HTTP REST with MQTT, as an example, highlighting MQTT's low power consumption. The authors conclude that lower overhead is paired with shorter processing time. [PERSON] et al. (2019) compare QoS and power consumption and find that QoS = 0 is the most power efficient setting in the MQTT protocol. [PERSON] et al. (2019) find that QoS = 1 contributes to more packet drops due to the duplication of messages. Moreover, they find that latency and packet drop with QoS = 0 is lower than that of QoS = 1 when the number of clients increases to 10,000 nodes. Throughput remains lower with QoS = 0 compared to QoS = 1 up to 100 clients. The researchers excluded QoS = 2 because its overhead is higher. Network latency is another parameter related to network architecture. [PERSON] et al. (2019) prove that static IP addresses reduce network initialization time compared to DHCP, but DHCP offers flexibility when pairing with new network infrastructure. DHCP clients can stay connected to the network or reconnect when defined events occur. Longer latencies affect sleep time and ultimately contribute towards power consumption. Until now, only a handful of researchers have focused on toilet or bathroom appliances, and even fewer have developed efficient power saving devices in this area. The existing work on IoT systems for bathroom and kitchen environments is inspiring; but as yet, it has not considered bathroom supply consumption, automation of supply ordering, power consumption, and integration with existing facilities. Therefore, in this paper, we propose an IoT-based tissue paper monitoring system that takes steps to fill those gaps.