Project highlights

  • Monitoring microclimate within a forest ecosystem in real time to improve ecological model predictions, such as forest productivity or pest/pathogen risk. 
  • 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 in the project. 
  • The self-powered sensing device will combine a bespoke harvester of ambient energy integrated with sensors and appropriate communication protocol for wireless data transmission. 

Overview

Sustainable Development Goal (SDGs) 15 of the United Nations seek to conserve life on land “to protect and restore terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and stop biodiversity loss’’. To achieve this, better understanding of the impacts of environmental change on biodiversity is needed. Reliable estimation of microclimatic conditions is key to understanding how ecosystems function and is increasingly recognised as essential for predicting ecological consequences of climate change and pest invasions. In forests, which host two-thirds of the world’s terrestrial biota, microclimate conditions vary considerably in space and time. Their 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, offering the potential to improve understanding of the environment. IoT is largely based on the deployment of robust self-powered sensing (SPS) 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 on smart environmental monitoring. 

SPS has two key advantages; (i) it does not require batteries, since it harvests energy from ambient environment  and (ii) data are transmitted wirelessly. Thus, it can enable accurate climate monitoring in difficult geographical terrains with significant reduction of the human intervention. Self-powered sensing can detect crucial parameters for ecosystem monitoring, such as temperature, rainfall, wind speed, humidity, soil moisture, CO2 levels. 

The proposed investigation will develop an SPS node (for microclimate observations within a forest ecosystem) compromising a bespoke energy harvester (electromagnetic or piezoelectric) integrated with sensors. The node will be autonomous, self-sustained with marginal human involvement. A hardware demonstrator (SPS node) assessed in real environmental operating conditions will be the main deliverable. 

image of a self-powered sensing system setup for IoT, showing an energy harvester prototype equipped with diode rectifier, power management board and a sensing unit; a set of graphical plots show a storage capacitor charged from human motion and a temperature sensor being powered as the capacitor is recharged between measurements.

Figure 1: Example of a self-powered sensing setup (a) consisting of the energy harvester, diode rectifier, power management board and a sensing unit; a storage capacitor is charged from human motion (b) and a temperature sensor is being powered as the capacitor is recharged between measurements (c).1 

CENTA Flagship

This is a CENTA Flagship Project

Case funding

This project is suitable for CASE funding

Host

Loughborough University

Theme

  • Climate and Environmental Sustainability

Supervisors

Project investigator

Co-investigators

How to apply

Methodology

Taking advantage of the expected slow motion (low frequency) operation of the self-powered sensing node (feature of the ambient wind energy) will drive the energy balance equation, where the input and output energy amounts are 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 computational modelling (combination of MATLAB and 3D software) and parametric studies will be conducted to predict the most attractive set of EH 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

DRs will be awarded CENTA Training Credits (CTCs) for participation in CENTA-provided and ‘free choice’ external training. One CTC can be earned per 3 hours training, and DRs must accrue 100 CTCs across the three and a half years of their PhD.  

The Researcher will be trained on technical and soft skills. Core skills: (i) energy harvesting (EH) principles (also comprising vibration EH and solar EH in a hybrid mode), (ii) multiphysics numerical modelling of EH, (iii) miniaturised self-powered sensing hardware design, (iv) experimental setup and measurements for validation in the Laboratory, and field testing. Soft skills: (i) Communicating research to wider audiences, (ii) Managing your research project, (iii) Planning for impact from your research, (iv) Developing a personal PhD publication strategy. He/she will be supported by academics and Industry or project partners to develop expertise in self-powered sensing for ecosystems. 

Partners and collaboration

CASE partner Met Office participate with Dr Deborah Hemming (Scientific Manager of Vegetation-Climate Interactions) who has worked extensively on vegetation-climate interactions and climate monitoring. The PhD Researcher will have the opportunity to work with Dr Hemming directly. In addition to in-kind contribution of £8,000, cash contribution is provided for the purposes of a CASE studentship. Botanic Gardens, Kew, have also joined the Supervisory Team, bringing invaluable expertise on the effect of climate change on biodiversity. Furthermore, the project benefits from the participation of Prof Ilya Maclean (University of Exeter), who will provide his extensive expertise in microclimate monitoring and modelling. 

Further details

For further information about this project, please contact Dr Amal Hajjaj ([email protected]) https://www.lboro.ac.uk/departments/meme/staff/amal-hajjaj/, or Prof. Stephanos Theodossiades ([email protected]) https://www.lboro.ac.uk/departments/meme/staff/stephanos-theodossiades/. 

To apply to this project:  

  • 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: https://www.lboro.ac.uk/study/postgraduate/apply/research-applications/   The CENTA Studentship Application Form 2025 and CV, along with other supporting documents required by Loughborough University, 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 2025-LU1 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 8th January 2025.  

Possible timeline

Year 1

The PhD Researcher will conduct a thorough review of the energy harvesting literature for environmental applications and low frequency wind motion. The energy requirements for the self-powered sensing node (i.e. including the available wind input energy to the Vibration EH 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 EH (comprising wind-induced vibration EH and photovoltaic solar panel EH) will be assessed. Reduced order numerical models of the EH 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 EH 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, at a UK forest ecosystem (e.g. in the Birmingham Insitute of Forest Research – BIFoR, where Dr Hemming is a Visiting Fellow) and at a Botanic Garden, Kew, site. Refinements for improved performance will be identified and implemented.

Further reading

Masabi, S, Fu, Hailing, Flint, J, and Theodossiades, S (2024) A multi-stable rotational energy harvester for arbitrary bi-directional horizontal excitation at ultra-low frequencies for self-powered sensing, Smart Materials and Structures, 33, 095017. DOI 10.1088/1361-665X/ad649b. 

Masabi, S, Fu, Hailing, Flint, J, and Theodossiades, S (2024), A pendulum-based rotational energy harvester for self-powered monitoring of rotating systems in the era of industrial digitization, Applied Energy, 365, 123200, https://doi.org/10.1016/j.apenergy.2024.123200. 

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, 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 resonators, Nonlinear Dynamics, ISSN: 0924-090X 

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(4), pp.2131-2143, ISSN: 0924-090X. DOI: 10.1007/s11071-020-05889-9. 

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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, https://doi.org/10.5194/gmd-14-3269-2021, 2021. 

Maclean IMD, Duffy JP, Haesen S, Govaert S, De Frenne P, Vanneste T, Lenoir J, Lembrechts JL, Rhodes MW, Van Meerbeek K (2022) On the measurement of microclimate, Methods Ecol Evol, 12: 1397-1410. 

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