Project highlights

  • Exciting opportunity to monitor microclimate within a forest ecosystem in real time to improve ecological model predictions, such as forest productivity or pest/pathogen risk. 
  • Novel development of a self-powered sensing device to capture parameters of interest (e.g. temperature, rainfall, wind speed, humidity, soil moisture, CO2 levels). 
  • The self-powered sensing device will combine a bespoke ambient energy harvester integrated with sensors for wireless data transmission, with widespread environmental and ecological application in real-world settings.  


Many organisms live in environments where microclimatic conditions differ substantially from those measured by weather stations, as near-ground temperatures are strongly influenced by radiative fluxes. Reliable estimation of microclimatic conditions is key to understanding how ecosystems function and is increasingly recognised as essential for predicting the ecological consequences of climate change and pathogen/pest invasions. In forests, which host two-thirds of the world’s terrestrial biota, microclimate conditions vary considerably in space and time. Its measurement requires the deployment of a high-density of sensors recording at frequent time-intervals, and adequate means of retrieving data from these sensors.  

The key aim in this project is to design hardware that enables the deployment of Internet of Things (IoT) sensors within forest ecosystems, which is integral to environmental research and effective environmental management. It is envisaged that a combination of traditional forecasts and IoT will form an accurate and low-cost solution for continuous and long-term monitoring of forest ecosystems and offer the potential to improve understanding of the environment. IoT is largely based on the deployment of robust self-powered sensing nodes, forming a network for data collection and transmission to establish a detailed picture of the microclimate within the ecosystem, to comply with the ambitious legal obligations of statutory authorities for innovation using smart environmental monitoring. 

Self-powered sensing has two key advantages: (i) it does not require batteries, since it harvests energy from the environment (ambient or a host structure) to power sensing and data transmission and (ii) it does not require harnessing, since data are transmitted wirelessly. Thus, such devices can enable accurate climate monitoring in difficult geographical terrain, leading to significant reduction of human intervention, since it is no longer necessary to physically visit inhospitable or logistically challenging environments to obtain measurements. Self-powered sensing can detect parameters crucial for ecosystem monitoring, such as temperature, rainfall, wind speed, humidity, soil moisture and CO2 levels. 

The proposed project will develop a self-powered sensing node (for microclimate observations within a forest ecosystem) that comprises a bespoke energy harvester (electromagnetic or piezoelectric) integrated with sensors. The node will be autonomous and self-sustained with minimal human involvement. A hardware demonstrator (self-powered sensing node) assessed in real-world environmental operating conditions (e.g., woodlands, forests) will be the main deliverable. 

Image shows a photograph of an electrical device at the top and a flow diagram at the bottom incorporating photographs of electronic circuit boards.

Figure 1: Example of a self-powered sensing system for IoT purposes (a) Vibration Energy Harvester (VEH) and (b) Schematic diagram of the VEH circuit. 


CENTA Flagship

This is a CENTA Flagship Project

Case funding

This project is suitable for CASE funding


Loughborough University


  • Climate and Environmental Sustainability


Project investigator

Dr Amal Hajjaj-Ep-Zemni, Loughborough University: [email protected] 


Prof Stephanos Theodossiades, Loughborough University: [email protected]  

Dr Ilya Maclean, University of Exeter: [email protected] 

Dr Deborah Hemming, Met Office UK: [email protected]  

How to apply


The successful applicant will work with experts within the Wolfson School of Mechanical, Electrical and Manufacturing Engineering and the Met Office. Understanding of the expected low frequency operating conditions of the self-powered sensing node (features of the ambient energy) will drive the energy balance equation, where the input and output energy amounts will be considered. The Energy Harvester (EH) principle (piezoelectric versus electromagnetic) will be explored in combination with solar panel EH (hybrid mode). 

The EH will be modelled using numerical analysis (combination of Matlab and 3D software) and parametric studies will be conducted to predict the most attractive set of design parameters. This will lead to the design and manufacture of the self-powered sensing node, considering all the components (EH, capacitor, power management, micro-controller, data communication protocol and sensor types). Component level testing of the EH and data communication will be done in the laboratory for validation. The self-powered sensing node will be assembled and will be tested in the laboratory, as well as at an actual forest ecosystem (such as Loughborough University’s Research Forest, Holywell Woods; 

Training and skills

Students will be awarded CENTA2 Training Credits (CTCs) for participation in CENTA2-provided and ‘free choice’ external training. One CTC equates to 1⁄2 day session and students must accrue 100 CTCs across the three years of their PhD.  

The successful applicant will be trained in: (i) energy harvesting principles (comprising VEH and solar EH in a hybrid mode), (ii) multiphysics numerical modelling of EH using commercial software, (iii) miniaturised self-powered sensing hardware design, (iv) experimental setup and measurements for validation of the design in the laboratory, as well as field testing in the University’s Research Forest (Holywell Woods). The researcher will be supported by a group of academics who are expert in the field (at Loughborough University and the University of Exeter) and industry (Met Office) in order to develop expertise in self-powered sensing for forest ecosystems. 

Partners and collaboration

The Met Office is a CASE partner on this project (providing an additional £3500 of support for the PhD researcher), with co-supervision from Dr Deborah Hemming (Scientific Manager of Vegetation-Climate Interactions at the Met Office) who has worked extensively on vegetation-climate interactions and climate monitoring. The PhD researcher will have the opportunity to work with Dr Hemming directly. Furthermore, the project benefits from the participation of Dr Ilya Maclean (Associate Professor of Global Change Biology, University of Exeter), who will provide extensive expertise in microclimate monitoring and modelling. 

Further details

Further details on how to contact the supervisor for this project and how to apply for this project can be found here: 

For any enquiries related to this project please contact Dr Amal Hajjaj-Ep-Zemni ([email protected]) or Prof Stephanos Theodossiades ([email protected]).

To apply to this project: 

  • You must include a CENTA studentship application form, downloadable from: CENTA Studentship Application Form 2024. 
  • You must include a CV with the names of at least two referees (preferably three) who can comment on your academic abilities. 
  • Please submit your application and complete the host institution application process via: The CENTA application form 2024 and CV can be uploaded at Section 10 “Supporting Documents” of the online portal. Under Section 4 “Programme Selection” the proposed study centre is Central England NERC Training Alliance. Please quote CENTA 2024-LU10 when completing the application form. 
  • For further enquiries about the application process, please contact the School of Social Sciences & Humanities ([email protected]). 

Applications must be submitted by 23:59 GMT on Wednesday 10th January 2024. 

Possible timeline

Year 1

The PhD Researcher will conduct a thorough review of the energy harvesting literature for environmental applications and low frequency oscillations. The energy requirements for the self-powered sensing node (i.e., including the available wind input energy to the VEH in the forest and the frequency and duration of measurements for forest microclimate monitoring) will be estimated and the energy balance equation will be drafted. The potential setup of a hybrid energy harvester (comprising wind-induced vibration energy harvester and photovoltaic solar panel energy harvester) will be assessed. Reduced order numerical models of the energy harvester will be developed using commercial software (Matlab and/or 3D electromagnetics).

Year 2

Parametric studies of the energy harvesting models will be conducted to select the key design features favouring energy harvesting. The energy harvester will be designed, and the prototype will be manufactured and tested in the laboratory for validation purposes. The other components of the node will be selected off-the shelf (sensor(s), power management board, capacitor, micro-controller and communication protocol). The protocol will be tested in the Lab and any discrepancies will be refined.

Year 3

The self-powered sensing node will be assembled combining all the necessary components selected in Year 2. The complete system will be validated in the Laboratory and at a UK forest ecosystem (either in Leicestershire, such as Loughborough University’s Research Forest, or in the Birmingham Insitute of Forest Research – BIFoR, where Dr Hemming is a Visiting Fellow). Refinements will be identified and implemented.

Further reading

Fu, H, Theodossiades, S, Gunn, B, Abdallah, I, Chatzi, E (2020) Ultra-low frequency energy harvesting using bi-stability and rotary-translational motion in a magnet-tethered oscillator, Nonlinear Dynamics, 101, 2131-2143, doi: 10.1007/s11071-020-05889-9. 

Fu, H, Mei, X, Yurchenko, D, Zhou, S, Theodossiades, S, Nakano, K, Yeatman, EM (2021) Rotational energy harvesting for self-powered sensing, Joule, 5, 1074-1118, doi: 10.1016/j.joule.2021.03.006 

Gunn, BE, Alevras, P, Flint, J, Fu, H, Rothberg, S, Theodossiades, S (2021) A self-tuned rotational vibration energy harvester for self-powered wireless sensing in powertrains, Applied Energy, 302, 117479, doi: 10.1016/j.apenergy.2021.117479. 

Hajjaj, A, Ruzziconi, L, Alfosail, F, Theodossiades, S (2022) Combined internal resonances at crossover of slacked micromachined resonators, Nonlinear Dynamics, 110, 2033–2048, doi: 10.1007/s11071-022-07764-1. 

Harper, AB. et al. (including D. Hemming) (2021) Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements, Geoscientific Model Development, 14, 3269–3294, doi: 10.5194/gmd-14-3269-2021. 

Kemppinen, J et al. (2023) Microclimate, an inseparable part of ecology and biogeography. Global Ecology and Biogeography, in press. Available at: 

Maclean, IM et al. (2021) On the measurement of microclimate. Methods in Ecology and Evolution, 12, 1397–1410, doi: 10.1111/2041-210X.13627. 

Suggitt, AJ et al. (2017), Conducting robust ecological analyses with climate data. Oikos, 126, 1533–1541. doi: 10.1111/oik.04203.