# Power Autonomy Estimation of Low-Power Sensor for Long-Term ECG Monitoring
## 2 Background and Related Work
### Energy Consumption Modeling in Wireless Sensor Networks  
Energy consumption modeling in WSNs is an active research topic. The authors in [14] have proposed a combination of the coordinated duty cycle algorithm (CDCA) and network coding to reduce the energy consumption and improve the transmission reliability in WBAN. They introduced a mathematical model for energy consumption, which was evaluated and compared with the actual implementation using simulations. The authors in [15] proposed a routing approach based on the energy consumption equalization within the sensor device. They developed both realistic and theoretical models for estimating the energy consumption of their proposed routing algorithms and proved its efficiency in minimizing the overall energy consumption. The authors in [5] proposed an extensive analytical energy consumption model and derived the load-balanced optimal routing configuration that maximizes network lifetime. In [16], the authors proposed a simple energy model for WSNs and demonstrated the significant reduction in energy consumption when limiting the frequency of network synchronization events by reducing the duty cycle. The authors in [6] developed a sensor lifetime energy model and evaluated the effects of the duty cycle on the expected energy consumption. They compared their model with the standard STEM-B model. Their model demonstrates that, by increasing the duty cycle, the energy consumption increases proportionally. These publications proposed energy consumption models for complex WSNs where many sensor devices are, in contrast to BLE connections, connected in multihop topologies. One of the widespread technologies for low-power WSNs is LoRaWAN, with its unique narrow-band modulation which enables low-power, low-rate and long-range narrow-band communications. The authors in [7] proposed an extensive energy consumption model for LoRaWAN Class A sensor node devices. They evaluated all aspects of device activity and energy consumption during the signal processing, energy consumption while receiving response, energy consumption while in sleep mode, etc. They also considered the microcontroller unit (MCU) frequency as a parameter in the proposed energy consumption model. By using the proposed energy consumption model, they analyzed the impact of all LoRaWAN communication parameters on the energy consumption. They compared the sensor node autonomy for three hypothetical LoRaWAN scenarios. Next, modeling solution comes in [8], where the authors proposed energy consumption models for LoRaWAN Class A devices for current consumption estimation, device lifetime estimation, and energy cost of data delivery estimation. The models were developed based on measurements made using the actual LoRaWAN devices. The authors in [17] proposed a simple battery lifetime prediction model based on the measurements for LoRaWAN Class A and Class C devices by measuring average power consumption for varying payload sizes and spreading factors. The authors in [18] proposed a simple simulation model for estimating the autonomy of biomedical sensors in body sensor networks (BSN). The models consider different operating modes of MCU and radio. A widespread technology that enables low-power WSNs is the BLE technology that is already available in a vast majority of modern smart devices (smart phones, tablets, smart watches, and computers). The omnipresence of BLE technology enables its quick adoption in consumer-oriented battery-powered low-power wireless devices. The authors in [19] presented BLE communication stack in detail including all communication layers, parameters, and stack behavior. They performed some performance evaluation tasks such as latency, energy consumption, and maximum piconet size and throughput using the CC2540 BLE radio chip. the authors in [9] proposed energy consumption models for opportunistic sensor data collection with BLE technology. They suggested two energy consumption models for different BLE approaches: an advertisement-based approach and connection-based approach.