This thesis aims to develop a low-power IoT sensor platform by examining various hardware architectures and implementing power optimization strategies. The objective is to create a sensor platform that is energy-efficient and suitable for IoT applications, particularly in remote or battery-powered environments. Key activities include selecting appropriate hardware platforms, conducting detailed power profiling, and applying advanced power optimization techniques to enhance energy efficiency.

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.
   • Evaluate their energy consumption profiles, performance, and compatibility with low-power designs.
2. Power Profiling
   • Measure and analyze power consumption across various operating modes (active, idle, sleep) for different hardware configurations.
   • Utilize tools like power meters and oscilloscopes to monitor energy usage and identify areas for improvement in energy efficiency.
3. Smart Power Optimization
   • Implement power-saving techniques such as sleep modes (e.g., Power Saving Mode, eDRX) to minimize energy consumption during idle periods.
   • Use duty cycling to efficiently schedule sensor activity, ensuring sensors are active only when needed, which significantly extends battery life.
   • Apply hardware-specific solutions like low-power controllers (e.g., MAX16163) to optimize sleep and shutdown states by managing power cycles of sensor components.

Challenges

• Balancing performance and power consumption, particularly for real-time applications.
• Designing a platform capable of adapting to varying environmental conditions and energy availability.