Energy-Aware Ambient IoT DP00BE73-3001

Ambient IoT is a new class of battery-free, self-powered, and low-cost wireless devices that harvest energy from the environment (e.g., RF, solar, thermal, or motion) to operate. Unlike traditional Internet of Things (IoT), these devices communicate continuously without requiring battery replacements or maintenance.

After completing this course, students will have a broad knowledge of Ambient IoT concepts and principles, including energy harvesting, power management, low-power wireless communications, intermittent and energy-aware computing, embedded and distributed AI, and emerging and advanced techniques.

The course consists of 3 modules, each of which consist of 4 contact hours (including theory and hands-on exercise) as well as some homework assignments. These modules are:

·         Module 1: Basics of Ambient IoT: history, overview, and enabling technologies

·         Module 2: Low-power wireless communications and networking: backscatter, active communications, standards

·         Module 3: Energy-aware computing: intermittent computing techniques, tinyML, distributed ML, and edge-cloud offloading strategies

Timing: September 8 – 10, 2026 (alternatively 9 – 11, 2026)

Target group: M.Sc. & Ph.D. students + postdoctoral researchers with a background in computer science, information technology, or electrical engineering

Objectives/topics:

·         What is Ambient IoT (history and overview)?

·         Energy harvesting, wireless power transfer, and power management

·         Low-power wireless communications and networking technologies

·         Intermittent and energy aware computing strategies

·         Ambient IoT in the device-edge-cloud continuum

·         Embedded AI and tinyML

·         Distributed AI and offloading strategies for ambient IoT

·         Over-the-air computing and advanced topics

·         Real-world examples, applications, and proof of concepts

Prerequisites:

·         Basic (bachelor-level) course on computer networks and/or wireless communications

·         Basic (bachelor-level) course on machine learning and/or artificial intelligence

·         Knowledge of C++ and Python

Literature:

·         Lecture slides + notes

Study methods and assessment:

·         All lectures will be face to face using power point presentations (12 hours)

·         After attending the course, students should solve homework assignments (20 hours)

·         Evaluation scale: Pass/fail