Power Management Techniques for Low-Energy IoT Devices

With the rise of the Internet of Things (IoT), embedded designers are, more than ever, focusing their attention and efforts on system energy usage. A prime example is a wireless sensor node—a relatively simple device from a functional point of view that is required to do its job for an extended period (in some cases, years) while powered by a battery.

Design considerations include major system elements such as the microcontroller (MCU), wireless interface, sensor and system power management.

Figure 1 shows a typical wireless sensor node.

Typical Wireless Sensor Node Architecture

The MCU will need to be extremely energy efficient. Computational requirements will likely dictate the selection of a 32-bit or 8-bit MCU, yet low energy requirements remain regardless of the MCU choice. Energy consumption in low-power and active modes, as well as the need to quickly wake up from low-power modes to full-speed operation, will make a significant difference in conserving battery power.

Consider how much the chosen MCU can do without actually leveraging the CPU core itself. For example, significant power savings can be achieved through autonomous handling of sensor interfaces and other peripheral functions. Being able to generate the stimulus signal, or power supply, for the sensor from the MCU and read back and interpret the results without waking the MCU until “useful” data is obtained can go a long way toward maximizing the system’s battery life.

Let’s consider the wireless connectivity. The network topology (Figure 2) and the choice of protocols will both have an impact on the power budget required to maintain the wireless link. In some cases, a simple point-to-point link using a proprietary sub-GHz protocol may seem like an appropriate choice to yield the lowest demand on power from the battery. However, this configuration can limit the scope of where and how the sensor can be deployed.

“Design considerations include major system elements such as the microcontroller (MCU), wireless interface, sensor and system power management.”

A star configuration built on either 2.4 GHz or sub-GHz technologies increases the flexibility for multiple sensor deployment, but this would likely increase the complexity of the protocol, therefore increasing the amount of RF traffic and system power.

A third option to consider is a mesh configuration based on a protocol such as ZigBee. While a mesh network imposes the biggest drain on the sensor node battery, it also provides the greatest level of flexibility. Depending upon the wireless stack, a mesh network can also provide the most reliable deployment option with a self-healing network.

Network Topology Examples

In a sensor node, the amount of data to be sent over the wireless link should be relatively small. As such, ZigBee provides an optimal mesh networking solution; Bluetooth Smart is an excellent choice for standards-based, power-sensitive point-to-point configurations, and proprietary sub-GHz solutions provide maximum flexibility for network size, bandwidth and data payloads in star or point-to-point configurations. Table 1 summarizes many of the key features and benefits of leading RF technologies used in IoT applications

Table 1: Primary Differences between RF Protocols

For very wide areas, long-range technologies and platforms such as LoRa and Sigfox, enable high node-count networks reaching up to tens of kilometers and with low-power systems. Data security is becoming more important. If the MCU used to run the stack does not have encryption hardware, it will have to burn multiple cycles to run the algorithm in software impacting the overall power consumption.

Numerous sensor choices are available and can range from discrete to fully integrated solutions. Discrete solutions may be power efficient, but place additional processing requirements on the MCU.

Building signal conditioning into the sensor provides some significant advantages. The data that is sent to the MCU will be relevant data that can be quickly and easily interpreted by the application, which means the MCU can stay asleep as long as possible. Having preconditioned data sent over a digital interface, such as SPI or I2C, also means the MCU can gather the data more efficiently than if it were using its ADC.

“If the MCU used to run the stack does not have encryption hardware, it will have to burn multiple cycles to run the algorithm in software impacting the overall power consumption.”

A final design consideration for low-energy applications is powering the system itself. Depending upon the type of battery used in the application, there is often a requirement for boost converters or boost-switching regulators. A careful choice can have a big impact on the system’s overall power consumption as solutions range from 1 uA – 7 uA consumption.

For more complex systems, a power management integrated circuit (PMIC) gives more precise control over the whole system. From a single power source, you can generate multiple voltage rails to drive different elements of the embedded system, tuning each voltage rail to provide just enough power for the application. A PMIC may also offer additional functionality for general system control, such as watchdog timers and reset capability.

Ultimately, there are many different system design aspects involved in designing low-energy, battery-powered applications. In addition to low-power semiconductor components, the approach to software, including wireless stacks, encryption and data processing, are important considerations. Each of these design elements can have a significant effect on the system’s overall power budget, while enabling developers to create low-energy IoT devices that maximize useful battery life.

Written By: Matt Saunders

Director of Field Marketing, Microcontroller and Wireless Products, Silicon Labs


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