Elena Gaura

Professor of Pervasive Computing | Coventry University

Coventry, UNITED KINGDOM

Professor Elena Gaura is Professor of Pervasive Computing at Coventry University.

Areas of Expertise (5)

Advanced Measurement Systems

Technology for Displaced Communities

Computing

Wireless Sensor Networks

Energy Access

Biography

Professor Elena Gaura is Professor of Pervasive Computing at Coventry University. She leads the Humanitarian Engineering and Energy Displacement (HEED) project that is responding to the need for improved access to energy (particularly renewable energy sources) for populations displaced by conflict and natural disasters. She is also the lead on the cyber-physical infrastructure theme for the UK’s Prospering From the Energy Revolution (PFER) programme. Elena works with communities in the UK, the Amazon, Philippines, Nepal and Rwanda on energy-for-all solutions enabled by sensing and the Internet.

Elena has a PhD in Intelligent Sensor Systems. She chaired (from 2007 to 2013) the UK Wireless Intelligent Sensing Interest Group (WiSIG) within the Electronics, Sensors, Photonics Knowledge Transfer Network. She is an expert reviewer and assessor for the European Commission (EC), Leverhulme Trust, UK Research Councils, Finland Academy of Science and other international funders. She is a full member of the UK’s Engineering and Physical Sciences Research Council College of Peers and the UKRI Future Leaders Fellowships Peer Review College and serves on the British Council Newton Fund Panels. She is an EPSRC affiliated member of the Women’s Engineering Society. She is actively involved with the European Commission and regional government organisations to promote the knowledge transfer from academia to industry and society at large. She is particularly focusing on the use of sensing technologies for reducing poverty, increasing health, enabling social mobility, and working towards the adoption of wireless technologies, Artificial Intelligence and the Internet to tackle global energy challenges. Her work is sponsored by the EPSRC, Innovate UK, Royal Society, European Programmes, British Council, Singapore- MIT Alliance and benefitted from direct sponsorship from industry (such as Jaguar Land Rover, Orbit Housing Association, NP Aerospace, Meggitt Ltd, and others).

Media Mentions (3)

Nikola Tesla: 5G network could realise his dream of wireless electricity, a century after experiments failed

The Conversation online

2021-04-08

The architects of 5G may have unwittingly built what Tesla failed to construct at the turn of the twentieth century: a “wireless power grid”.

How can we tackle energy poverty in refugee camps?

Futurum online

2020-08-31

Professor Elena Gaura, Associate Dean of Research at Coventry University in the UK, believes that, “engineering can change lives because it gives people hope”. She is principal investigator of the HEED Project, a humanitarian endeavour focused on increasing access to affordable and sustainable energy for displaced people.

Training for nearly 100 Vietnamese researchers on research supplementary skills in three big cities

British Council online

2019-06-30

Training for Vietnamese researchers was organised in partnership with experienced experts from Coventry University. The initative from Coventry's side was led by Professor Elena Gaura.

Multimedia Appearances

Publications Photos Videos Documents Audio/Podcasts

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Accomplishments (3)

Most Innovative Consultant (professional)

2013 Housing Innovation Awards

Coventry University International Women's Day Award (professional)

2018-03-01

For services to Women in Science in India

Membership of Women's Engineering Society to celebrate centenary (professional)

2019-06-12

Elena was one of nine scientists that were awarded a year’s membership of the Women’s Engineering Society (WES) to celebrate the organisation’s centenary.

Education (3)

Coventry University: PhD 2000

Technical University of Cluj: BSc, Applied Electronics 1991

Saïd Business School, University of Oxford: The Women Transforming Leadership Programme 2018

Executive Development

Affiliations (5)

EPSRC College of Peers : Member
IEEE Sensors : Associate Editor
Honorary and Adjunct Professor, Macquarie University
Honorary Professor, Deakin University
Member, UKRI Future Leaders Fellowships Peer Review College

Links (4)

Event Appearances (4)

Keynote Speech: Sustainable Energy Futures for All

World Engineering Day Coventry University

2021-03-04

Data: the Power to Empower. Sensors, Sensing and IOT in Infrastructure-less Environments.

Royal Engineering ‘Frontiers of Engineering for Development’ Interdisciplinary symposia

2021-02-01

Balancing the Present with the Future: The Key to Sustainable Microgrid Design

Global Plan of Action: Working Group V – Data and Evidence

2020-09-29

Refugees and Technology

Panel Discussion and Photo Exhibition Chatham House

2019-10-02

Articles (5)

The meanings of energy: a case study of student air conditioning activity

Energy Efficiency

There has been much work on interventions aimed at changing consumer energy consumption behaviour, but the progress on emissions reductions is limited. We argue that further gains can be made through new examinations and developments of the theory that informs interventions, offering a critique of two major areas of theory in energy reduction: behaviour change and social practice theory (SPT). Behaviour change focusses on the individual consumer, but powerful social influences on energy consumption are often neglected, and results can be ambiguous.

A New Hardware Approach to Self-Organizing Maps

IEEE

Self-Organizing Maps (SOMs) are widely used as a data mining technique for applications that require data dimensionality reduction and clustering. Given the complexity of the SOM learning phase and the massive dimensionality of many data sets as well as their sample size in Big Data applications, high-speed processing is critical when implementing SOM approaches. This paper proposes a new hardware approach to SOM implementation, exploiting parallelization, to optimize the system’s processing time. Unlike most implementations in the literature, this proposed approach allows the parallelization of the data dimensions instead of the map, ensuring high processing speed regardless of data dimensions.

Understanding Household Fuel Choice Behaviour in the Amazonas State, Brazil: Effects of Validation and Feature Selection

Energies

Since 2003, Brazil has striven to provide energy access to all, in rural areas, in an effort to economically empower the communities. Unpacking fuel stacking behaviour can shed light onto the speed of transition toward the exclusive use of advanced fuel types. This paper presents findings from surveys that were carried out with 14 non-electrified communities in a rural area of Rio Negro, Amazonas State, Brazil. We identify the fuel choice determinants in these communities using a multinomial logistic regression model and more generally discuss the validity and robustness of such models in the context of statistical validation and evaluation metrics.

Heartbeat design for energy-aware IoT: Are your sensors alive?

Expert Systems with Applications

A number of algorithms now exist for using model-based prediction at the sensor node of a wireless sensor network (WSN) to enable a dramatic reduction in transmission rates, and thus save energy at the sensor node. These approaches, however, sometimes reduce the rate so substantially as to make the health state of the network opaque. One solution is to include a regular heartbeat transmission whose receipt or otherwise informs the sink about the health state of the node.

A dynamic linear model for heteroscedastic LDA under class imbalance

Neurocomputing

Linear Discriminant Analysis (LDA) yields the optimal Bayes classifier for binary classification for normally distributed classes with equal covariance. To improve the performance of LDA, heteroscedastic LDA (HLDA) that removes the equal covariance assumption has been developed. In this paper, we show using first and second-order optimality conditions that the existing approaches either have no principled computational procedure for optimal parameter selection, or underperform in terms of the accuracy of classification and the area under the receiver operating characteristics curve (AUC) under class imbalance.