# Energy Consumption Analysis of LPWAN Technologies and Lifetime Estimation for IoT Application
## 5 Potential Challenges in Estimating Energy Efficiency For End-Device  
Estimating the actual energy consumption of a low power end-device exhibits many challenges in real-life scenarios. There are many factors which determine the overall energy consumption of the end-device. A slight variation in these parameters can have a significant impact on the energy consumption of the end-device; for instance, the sleep mode current. As seen from the current consumption of different LPWAN technologies, the sleep mode current has a significant impact on the over all energy consumption of the device. As most of the time, end-device is in an in-active state so it should be optimized to achieve energy-efficiency. Battery characteristics also impact the end-device lifetime [37] and task oriented power consumption. There has been research from a quantitative standpoint to predict and model the battery lifetime. The complexity is mainly due to the nonlinear battery discharge because of factors such as battery electrode, rate of diffusion of active materials in electrolyte, etc. Apart from the energy consumption required in the process of computation and communication from the end-device; aspects such as chemical changes, thermal effects and discharge rate of the battery also impact the battery lifetime. Also, the inevitable leakage current from electronics components that results from the temperature variation can lead to an uncertainty in determining the actual energy consumption of a device. This is of more importance in the case of outdoor devices where the temperature variation can be significant. As a result, the dormant mode energy draw of the device from the battery is dependent on both the hardware and the environmental conditions where the device is installed. Firmware and hardware are the backbone for the end-device and directly contributes in energy consumption. With each new release of LPWAN commodity hardware implementations, they are expected to optimize the power consumption which brings the added value for the deployment. There are techniques to reduce power consumption by writing better software [38]. Efficient implementation of firmware in microcontroller helps in efficiently managing the resources. There has to be careful coordination between hardware and software for the critical role of initializing and configuring modules, controlling the task and other power management features. There are several LPWAN hardwares and batteries available in the market so it depends on the application to pick the right choice in contrast to, aforementioned factors responsible in energy consumption. Applications with multiple integrated devices need far more than battery management, such as keeping the pack of battery cells healthy. Interfacing the battery load needs a safe operation. There are multiple sensors operating at different voltage and speed involved in the operation of such devices. This generates varying workloads and heat on the battery which impacts the expected lifetime. This involves multi-directional research scope such as protecting batteries from cyber attacks using wireless networks and accurate battery management. Energy management is one of the biggest challenges in portable applications having heterogeneous components with dynamic processing. There are various intelligent energy management systems such as fuzzy-logic-approach to reduce energy consumption. Thereby, the type of application and its complexity has an impact in estimating energy efficiency. This needs to be evaluated for picking the right battery, its capacity, and wireless technology for the smooth working of the application. Moreover, applications that has an autonomous power source such as an energy harvesting system can have an extended algorithm and energy management technique to map the number of message transmissions to the available onboard energy.