# Energy-Aware System Design for Autonomous Wireless Sensor Nodes: A Comprehensive Review
## 4 Energy Management
### Energy Budget Estimation and Benchmarking  
The energy constraint is a challenging feature for WSNs. Generally, sensor nodes consume energy while sensing, processing, transmitting, or receiving data. This can be defined as useful power consumption. As indicated in [69], more than 50% of energy is devoted to the radio part. The increasing demand for computing and memory, maintaining connectivity, meeting design constraints, low power consumption, low cost and real-time constraints on WSNs applications are leading to the development of novel approaches for efficient hardware architectures. A significant challenge is to design applications with high computing potential with limited resources. To enable the end-to-end performance, the processing complexity is raised at the end nodes, leading to an excessive power consumption that constrains their large-scale deployment. Thus, a compromise between the processing performance and energy consumption needs to be considered. The choice of  
Figure 5: Possible coils positions: (**a**) ideal case; (**b**) big lift-off distance; (**c**) lateral misalignment; and (**d**) angular misalignment. an energy-efficient microcontroller is crucial in the design of any wireless sensor node. In this context, a benchmarking-based investigation on energy efficiency of low-power microcontroller for WSNs is developed to select the appropriate MCU [70]. The benchmarking presents a method for the characterization of power consumption of microcontrollers to satisfy the performance requirements. The method is based on data sheet comparison and deep comparison, namely real measurements of power consumption in the selected microcontrollers. The power consumption of various microcontrollers in different operation modes for potential use in applications with low-power requirements are analyzed. Considering the data logging operation, the power consumption of the PIC16 LF, MSP430 FR and STM32L microcontrollers are, respectively, 1.52, 0.965 and 1.135 mW. Different techniques are implemented to reduce the power consumption of the processing unit of a wireless sensor node including the Dynamic Voltage and Frequency Scaling (DVFS) [71]. The DVFS is designed to minimize the power consumption during the execution of tasks by adjusting dynamically the operating voltage and frequency depending on the system performance requirement at a fixed time. Once the MCU is slightly charged, the frequency and voltage are gradually reduced to ensure that the MCU runtime latency is not affected. The MCU running at maximum frequency is loaded beyond a certain threshold. Therefore, investigating the power consumption of the wireless node during different activities is critical. In fact, a sensor node can have various operating modes, where, in each state, the MCU consumes a certain amount of energy, dependent on the executed function. Five main activity modes are identified for each wireless sensor node: off, idle, sleep, transmission and reception and processing. When the node is turned off, the microcontroller still idles but consumes a minimal amount of energy compared to other modes, which allows it to switch to sleep mode when necessary. Once the node is turned on, it goes to sleep mode to save energy and waits for the next step according to its predefined schedule. When the node is only listening, idle mode is activated. Next is the complete activity mode, where the node is either a receiver or transmitter, which in both cases consumes more energy than previous stages. The energy consumption in each step is relative to the electronic components of the node itself. Table 2 presents an overview of current consumption of wireless sensor nodes of well-known platforms in different activity modes. A precise estimation of the energy consumption within each activity mode is related to the transmitted payload, mainly during reception and transmission modes.