Project highlights

  • Monitoring microclimate within a forest ecosystem in real time to improve ecological model predictions, such as forest productivity or pest/pathogen risk.
  • The parameters of interest (e.g. temperature, rainfall, wind speed, humidity, soil moisture, CO2 levels) will be captured by a self-powered sensing device that will be developed.
  • The self-powered sensing device will combine a bespoke harvester of ambient energy integrated with sensors and appropriate communication protocol for wireless data transmission.


Many organisms live in environment where microclimatic conditions differ substantially from those measured by weather stations, as close to the ground or the surface of vegetation, temperatures are influenced strongly 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 pest invasions. This has led to a paradigm shift towards microclimate ecology, yet researchers are hampered by difficulties associated with measuring microclimate. 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 appropriate hardware that will enable the implementation of the Internet of Things (IoT) within forest ecosystems. 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. 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.

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, they can enable accurate climate monitoring in difficult geographical terrains, leading to significant reduction of the human intervention, since it is no longer necessary to physically visit inhospitable environments to obtain measurements. Self-powered sensing can detect present climate details, such as temperature, rainfall, wind speed, humidity, soil moisture, CO2 levels, and other data crucial for ecosystem monitoring.

The proposed investigation 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, self-sustained with marginal human involvement. A hardware demonstrator (self-powered sensing node) assessed in real environmental operating conditions will be the main deliverable.

Two panels showing a photograph of a vibration energy harvester (VEH) prototype and a schematic diagram of the VEH circuit.
Figure 1: Example of a self-powered sensing system (a) Vibration energy harvester (VEH) and (b) Schematic diagram of the VEH circuit.


Loughborough University


  • Organisms and Ecosystems


Project investigator



How to apply


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.

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 Researcher will be trained on:

i) energy harvesting principles (comprising vibration energy harvester (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.

They will be supported by a Group of Academics (Loughborough – Exeter) and Industry (Met Office) in order to develop expertise in self-powered sensing for ecosystems (forests).

Partners and collaboration

The external partner for this project is 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 will benefit from the participation of Dr Ilya Maclean (Associate Professor of Global Change Biology, University of Exeter), who will provide his extensive expertise in microclimate monitoring and modelling.

Further details

For further information about this project, please contact Prof Stephanos Theodossiades ([email protected]), Dr Hailing Fu ([email protected]) or Dr Amal Hajjej-Ep-Zemni ([email protected]). For general information about CENTA and the application process, please visit the CENTA website: For enquiries about the application process, please contact the Wolfson School of Mechanical, Electrical and Manufacturing Engineering ([email protected]).

If you wish to apply to the project, applications should include:

  • A CV with the names of at least two referees (preferably three and who can comment on your academic abilities)

Applications to be received by the end of the day on Wednesday 11th January 2023. 

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 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 forest ecosystem (either in Leicestershire 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 oscillatorNonlinear Dynamics, 101(4), pp.2131-2143, ISSN: 0924-090X. 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 sensingJoule, ISSN: 2542-4351. DOI: 10.1016/j.joule.2021.03.006

Gardner, A.S., Maclean, I.M.D., Gaston, K.J. & Bütikofer, L. Forecasting future crop suitability with microclimate data, Agricultural Systems, 190, 2021, 103084,

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 powertrainsApplied Energy, 302, 117479, ISSN: 0306-2619. DOI: 10.1016/j.apenergy.2021.117479.

Hajjaj, A, Ruzziconi, L, Alfosail, F, Theodossiades, S (2022) Combined internal resonances at crossover of slacked micromachined resonatorsNonlinear Dynamics, ISSN: 0924-090X

Harper, A. B., Williams, K. E., McGuire, P. C., Duran Rojas, M. C., Hemming, D., Verhoef, A., Huntingford, C., Rowland, L., Marthews, T., Breder Eller, C., Mathison, C., Nobrega, R. L. B., Gedney, N., Vidale, P. L., Otu-Larbi, F., Pandey, D., Garrigues, S., Wright, A., Slevin, D., De Kauwe, M. G., Blyth, E., Ardö, J., Black, A., Bonal, D., Buchmann, N., Burban, B., Fuchs, K., de Grandcourt, A., Mammarella, I., Merbold, L., Montagnani, L., Nouvellon, Y., Restrepo-Coupe, N., and Wohlfahrt, G.: Improvement of modeling plant responses to low soil moisture in JULESvn4.9 and evaluation against flux tower measurements, Geosci. Model Dev., 14, 3269–3294,, 2021.

Klinges, D. H., Duffy, J. P., Kearney, M. R., & Maclean, I. M. (2022). mcera5: Driving microclimate models with ERA5 global gridded climate data. Methods in Ecology and Evolution, 13, 1402– 1411.

Maclean, I.M., Duffy, J.P., Haesen, S., Govaert, S., De Frenne, P., Vanneste, T., Lenoir, J., Lembrechts, J.J., Rhodes, M.W. and Van Meerbeek, K., 2021. On the measurement of microclimate. Methods in Ecology and Evolution, 12: 1397-1410.

Suggitt, A.J., Platts, P.J., Barata, I.M., Bennie, J.J., Burgess, M.D., Bystriakova, N., Duffield, S., Ewing, S.R., Gillingham, P.K., Harper, A.B., Hartley, A.J., Hemming, D.L., Maclean, I.M.D., Maltby, K., Marshall, H.H., Morecroft, M.D., Pearce-Higgins, J.W., Pearce-Kelly, P., Phillimore, A.B., Price, J.T., Pyke, A., Stewart, J.E., Warren, R. and Hill, J.K. (2017), Conducting robust ecological analyses with climate data. Oikos, 126: 1533-1541.


The project involves a mixture of numerical modelling, analysis, hardware design and experimental measurements. Remote access to software and design tools can be provided if UK national lockdowns occur in the future. The hardware design and experimental work will start as early as possible in the project so that there are sufficient time margins ahead for activities to be shifted without affecting the project aim and objectives. Access to the University labs was provided during covid pandemic for most of the time via a booking system in order to limit the number of people working in the same room.