2017
[3]
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.
[2]
X. Fafoutis, A. Elsts, A. Vafeas, G. Oikonomou, R. Piechocki, "Demo: SPES-2 – A Sensing Platform for Maintenance-Free Residential Monitoring", in Proc. EWSN 2017, 2017 (accepted, to appear)
SPES-2 is a sensing board for room-level monitoring in a home environment. It constitutes a vital modality of the SPHERE architecture: a multi-modal sensing platform for healthcare in a residential environment. SPES-2 uses an optimised implementation of the IEEE 802.15.4-2015 TSCH (Time-Slotted Channel Hopping) standard to operate efficiently and reliably in unknown environments for more than one year without battery replacement, providing continuous information about the ambient characteristics of the room (such as temperature, humidity and light levels), as well as presence information captured through a motion sensor.
[1]
P. Cooper, K. Maraslis, T. Tryfonas, G. Oikonomou, "An intelligent hot-desking model harnessing the power of occupancy sensing", Journal of Facilities, Emerald Group Publishing Limited, 2017 (in press)
In this paper a model is developed to harness the power of occupancy sensing in an Intelligent Hot-Desking system utilizing experimental data from a commercial office in central London. To achieve that, the model uses that data as an input in order to undertake the task of allocating the office desks to the employees in a way that will maximise their productivity based on the type of project that each employee is working on each time. In this way, and by taking into account other parameters that are involved as well, the synergy that this situation can create, can increase productivity significantly compared to the situation where employees have their desks fixed under any circumstances and also allow for expenses cut since the desks can now be less than the employees. Not only is this approach able to optimize desk utilization based on quality occupancy data, but also speculates how and by how much overall productivity increases, while proving that its benefits outweigh the costs of adopting such a system. Furthermore, this paper explores the barriers towards Intelligent Hot-Desking, including how an increase in occupancy data collection in the private sector could have key advantages for the business as an organization and the city as a whole. Ultimately, it provides a valuable and feasible use case for the use of occupancy data in smart buildings, a dataset that is perceived to be valuable yet underexplored.
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