Thesis: Investigating and Building a Low-Power IoT Sensor Platform: Hardware, Power Profiling, and Optimization
Overview: This thesis focuses on developing a low-power IoT sensor platform by investigating different hardware architectures and exploring power optimization strategies. The goal is to design a sensor platform suitable for IoT applications that can operate efficiently with minimal energy consumption, making it ideal for remote or battery-powered environments. Key activities include hardware platform selection, detailed power profiling, and the application of smart power optimization techniques.
Description
Key Components:
1. Hardware Platform Investigation:
- Explore different microcontrollers, sensor modules, and communication protocols (e.g., LoRa, Zigbee, Bluetooth Low Energy) commonly used in IoT devices.
- Assess their energy consumption profiles, performance, and compatibility with low-power designs.
2. Power Profiling:
- Measure and analyze power consumption in different operating modes (active, idle, sleep) across various hardware configurations.
- Use tools such as power meters and oscilloscopes to monitor energy usage and identify bottlenecks in energy efficiency.
3. Smart Power Optimization:
- Implement techniques like sleep modes (e.g., Power Saving Mode, eDRX) to minimize energy consumption during idle periods
- Use duty cycling to schedule sensor activity efficiently, ensuring that sensors operate only when necessary and return to sleep mode immediately after, significantly extending battery life
- Employ hardware-specific solutions such as low-power controllers (e.g., MAX16163) to optimize sleep and shutdown states, reducing energy usage by controlling the power on/off cycles of sensor components.
Challenges:
- Achieving an optimal balance between performance and power consumption, especially for real-time applications.
- Designing a platform that can adapt to varying environmental conditions and energy availability.
This research will contribute to creating sustainable IoT platforms that extend battery life and reduce overall power consumption in sensor networks and embedded systems.