## 3 Related Works
The work differs from the work in this paper in the sense that the model proposed in this paper is based on a shallow neural network, while the latter was a large CNN model. Training such a model is computationally expensive, and from the power analysis of the model when deployed, the energy consumption of the device increases when Wi-Fi is enabled. Therefore, it requires a frequent recharge of the batteries, increasing the overall energy demand of the system. On the other hand, our proposed model is based on knowledge distillation which does not require Internet access at all, and has energy harvesting technology to take care of intermitted battery recharge. Moreover, [PERSON] et al. [37] proposed a multi-exit neural network to overcome the limitation of a deep neural network by filtering out data without objects of interest, thereby avoiding computing the entire neural network. In their paper they implement a multi-exit convolutional neural network on the ESP32-CAM embedded platform as an image-sensing system with an energy harvesting technology to serve as a power source, the system achieved an accuracy of 74% with a reduced power consumption of about 42.5%. Others such as [PERSON]. [PERSON] et al. [38] deviates from using deep learning entirely. As the computational cost is too high, they proposed a low-power system for vegetation monitoring using a system-based monitoring algorithm for agricultural fields. The solution is based on an electronic architecture of a CPU-FPGA SoC system and the OpenCL parallel programming paradigm. Interestingly, the effort to minimize power consumption of edge devices can be from both hardware and software perspectives. Knowledge distillation reduces the number of parameters in the model, which reduced the number of computations, which in turn reduced the number of memory accesses, thus reducing power consumption. From the hardware point of view, different techniques are available which can be used to reduce the power consumption of the processing unit of an edge device or a sensor node. An example of such techniques is Dynamic Voltage and Frequency Scaling (DVFS). According to [PERSON] et al. [39], DVFS is designed to minimize the power consumption during the execution of tasks by adjusting the operating voltage and frequency dynamically depending on the system performance requirement at a fixed time. It is also possible to investigate the power consumption of the wireless node during different activities. Depending on the design, IoT devices can be designed to have various operating modes, such as standby, battery saver, fully operational, and so on. In each state, the amount of energy consumes differs [40]. For example, in standby, the system reserves the energy without performing any heavy task. Under a power saver, the power has dropped below a certain threshold, and there, some of the system functionalities are turned off automatically to minimize the power consumption. This second aspect can also be regarded as energy management. Energy management is a way of using the available energy in edge devices carefully to maximize operation time and minimize power consumption, it is a critical part of a system that is powered by energy harvesting technology. An efficient energy management solution enables long-lasting devices and IoT systems. It minimizes the failure rate by preventing frequent system downtime as well as reducing maintenance costs [41]. From the software aspect as well, due to the rise in these edge devices and the popularity of IoT systems, researchers are investigating ways of building and efficient CNN models with good accuracy, as well as low power consumption. The reason behind this shift was that CNN achieves significant breakthroughs in term of image classification at the cost of computation. These models contain millions of parameters and perform hundreds of thousands of operations during training, which is why the models can only be trained on a high-performance computer with GPUs. This limits the possibilities of these models to be deployed on embedded devices.