Design of multifunctional devices for simultaneous sensing, monitoring and energy harvesting: Towards the implementation of artificial intelligence of things (AIoT)

​​The artificial intelligence of things (AIoT) is a new concept which offers tremendous possibilities to enable a smart eco-system. The rise in popularity for building smart eco-systems can be attributed to the revolution in the sensing technologies. The integration of Internet-of-Thing (IoT) technologies with AI can open up opportunities for remote inspection and early detection of potential failure events. 

​Harnessing energy from mechanical vibrations has shown promise to be one of the sustainable alternatives for powering sensors due to abundant presence and high power density. Out of the commonly deployed technologies to convert ambient vibrations into electrical energy, piezoelectric energy harvester is a viable choice, offering low installation costs, high energy density and low space requirement.  

​This research will focus on the design of locally resonant metamaterials (MMs) and leverage from their extraordinary properties, for high-performance multifunctional capabilities of sensing and energy harvesting. This project will address existing challenges in piezoelectric energy harvesting (PEH), such as, off-resonance narrow operating frequency bandwidth (OFB) and scale difference in the high frequency operating range of piezo harvesters and low frequency ambient vibrations by thorough investigation of passive nonlinear strategies like, impact dynamics, mechanical plucking, etc and new concepts in MMs like, interface modes in topological MMs. 

​Combining AI tools is key to enhance the intelligence of data analytics of the IoT sensors. Therefore, the proposed research will tune and adapt existing AI algorithms (i) for better interpretability, and (ii) to have the capability to quantify the model predictive uncertainty. The cloud based AI/ML data analytics platform would not only be crucial for real-time autonomous monitoring but also assist in early prediction of extreme events likely to cause disruptions. This project will be one of the first feasibility studies on intelligent self-powered sensing and is crucial towards the AIoT implementation for future smart infrastructure.

Open to both UK and international candidates.

Up to 30% of our UKRI-funded studentships can be awarded to candidates paying international rate fees. Find out more about eligibility.

You will need to meet the minimum entry requirements for our Engineering Materials PhD programme.

The successful candidate is expected to be highly motivated and must hold a minimum of a 2:1 Bachelor’s level degree (or international equivalent) in Mechanical Engineering, Materials, Physics, AI, or a related field. Proficiency in programming languages like Python, C++, and MATLAB is essential. Practical experience in AI/ML, structural dynamics, and piezoelectric materials is desirable. Experience with commercial software like COMSOL Multiphysics and experimental investigation would be beneficial. The candidate should possess strong analytical and problem-solving abilities, coupled with excellent written and verbal communication skills. Independence in research endeavours and quick adaptation to new technologies are highly valued.