Tom Lovett

Mobile Context Awareness

Mobile context-awareness is a popular research trend in the field of ubiquitous computing. Advances in mobile device sensory hardware and the rise of virtual sensors such as web application programming interfaces (APIs) mean that the mobile user is exposed to a vast range of data that can be used for new advanced applications. Mobile Context Awareness presents work from industrial and academic researchers, focusing on novel methods of context acquisition in the mobile environment particularly through the use of physical and virtual sensors along with research into new applications utilising this context. In addition, the book provides insights into the technical and usability challenges involved in mobile context-awareness, as well as observations on current and future trends in the field.

Overheating in vulnerable and non-vulnerable households

ABSTRACT As the 2003 European heatwave demonstrated, overheating in homes can cause wide-scale fatalities. With temperatures and heatwave frequency predicted to increase due to climate change, such events can be expected to become more common. Thus, investigating the risk of overheating in buildings is key to understanding the scale of the problem and in designing solutions. Most work on this topic has been theoretical and based on lightweight dwellings that might be expected to overheat. By contrast, this study collects temperature and air quality data over two years for vulnerable and non-vulnerable UK homes where overheating would not be expected to be common. Overheating was found to occur, particularly and disproportionately in households with vulnerable occupants. As the summers in question were not extreme and contained no prolonged heatwaves, this is a significant and worrying finding. The vulnerable homes were also found to have worse indoor air quality. This suggests that some of the problem might be solved by enhancing indoor ventilation. The collected thermal comfort survey data were also validated against the European adaptive model. Results suggest that the model underestimates discomfort in warm conditions, having implications for both vulnerable and non-vulnerable homes.

Designing sensor sets for capturing energy events in buildings

We study the problem of designing sensor sets for capturing energy events in buildings. In addition to direct energy sensing methods, e.g. electricity and gas, it is often desirable to monitor energy use and occupant activity through other sensors such as temperature and motion. However, practical constraints such as cost and deployment requirements can limit the choice, quantity and quality of sensors that can be distributed within each building, especially for large-scale deployments. In this paper, we present an approach to select a set of sensors for capturing energy events, using a measure of each candidate sensors ability to predict energy events within a building. We use constrained optimisation -- specifically, a bounded knapsack problem (BKP) -- to choose the best sensors for the set given each sensors predictive value and specified cost constraints. We present the results from a field study of 4 UK homes with temperature, light, motion, humidity, sound and CO2 sensors, showing how valuable yet expensive sensors are often not chosen in the optimal set.

Capturing transitions between users' semantically meaningful places using mobile devices

Due to their ubiquity and ever-increasing technical capabilities, mobile devices are often used as data collection tools by researchers in multiple fields, notably HCI and Ubicomp. Although the data gathered by mobile devices can be generated from sources such as the device users, it is difficult for researchers to capture ground truth and verify data integrity beyond controlled laboratory studies. This lack of knowledge about data integrity may, in turn, affect the quality of higher-level inferences made using the data. In this paper, we report on the experience and results of a hybrid laboratory/field study in which we use mobile devices to infer the moment at which users transition between self-defined semantically meaningful personal places. The results show that filtered device motion does appear to reflect these moments of transition well, but the nature of the research question makes verification difficult in a field study.

The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings

The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.