P. Woznowski, A. Burrows, T. Diethe, X. Fafoutis, J. Hall, S. Hannuna, M. Camplani, N. Twomey, M. Kozlowski, B. Tan, N. Zhu, A. Elsts, A. Vafeas, A. Paiement, L. Tao, M. Mirmehdi, T. Burghardt, D. Damen, P. Flach, R. Piechocki, I. Craddock, G. Oikonomou, "SPHERE: A sensor platform for healthcare in a residential environment", in Designing, Developing, and Facilitating Smart Cities, Springer, pp. 315-333, 2017
It can be tempting to think about smart homes like one thinks about smart cities. On the surface, smart homes and smart cities comprise coherent systems enabled by similar sensing and interactive technologies. It can also be argued that both are broadly underpinned by shared goals of sustainable development, inclusive user engagement and improved service delivery. However, the home possesses unique characteristics that must be considered in order to develop effective smart home systems that are adopted in the real world.
P. Woznowski, D. Kaleshi, G. Oikonomou, I. Craddock, "Classification and Suitability of Sensing Technologies for Activity Recognition", Computer Communications, 89-90, pp. 34-50, 2016
Wider availability of sensors and sensing systems has pushed research in the direction of automatic activity recognition (AR) either for medical or other personal benefits e.g. wellness or fitness monitoring. Researchers apply different AR techniques/algorithms and use a wide range of sensors to discover home activities. However, it seems that the AR algorithms are purely technology-driven rather than informing studies on the type and quality of input required. There is an expectation to over-instrument the environment or the subjects and then develop AR algorithms, where instead the problem should be approached from a different angle i.e. what sensors (type, quality and quantity) a given algorithm requires to infer particular activities with a certain confidence? This paper introduces the concept of activity recognition, its taxonomy and familiarises the reader with sub-classes of sensor-based AR. Furthermore, it presents an overview of existing health services Telecare and Telehealth solutions, and introduces the hierarchical taxonomy of human behaviour analysis tasks. This work is a result of a systematic literature review and it presents the reader with a comprehensive set of home-based activities of daily living (ADL) and sensors proven to recognise these activities. Apart from reviewing usefulness of various sensing technologies for home-based AR algorithms, it highlights the problem of technology-driven cycle of development in this area.
M. Haghighi, K. Maraslis, T. Tryfonas, G. Oikonomou, A. Burrows, P. Woznowski, "Game Theoretic Approach Towards Optimal Multi-tasking and Data-distribution in IoT", in Proc. IEEE World Forum on Internet of Things (WF-IoT), pp. 406-411, 2015
Current applications of Internet of Things (IoT) often require nodes to implement logical decision-making on aggregated data, which involves more processing and wider interactions amongst network peers, resulting in higher energy consumption and shorter node lifetime. This paper presents a game theoretic approach used in Sensomax, an agent-based WSN middleware that facilitates seamless integration of mathematical functions in large-scale wireless sensor networks. In this context, we investigate game theoretic and auction-based techniques to optimise task distribution and energy consumption in IoT networks of multiple concurrent WSNs. We also demonstrate how our proposed game theoretic approach affects the performance of WSN applications with different operational paradigms.
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