The goal of SENSIBLE is to develop a novel integrated decision support mechanism embedding intelligent sensing, communications and data processing methodology for improving sustainability of smart buildings through new insights, approaches and technologies for acquisition, communications, and extraction of useful information from the sheer volume of sensed data.
The work carried out towards achieving this goal comprises a blend of structured research, training, and knowledge sharing activities. In particular, the research objectives have been tackled during the secondments, by hands-on research work by the secondees supported by mentors and hosting research teams, including sharing access to relevant equipment, joint PhD student supervision, information sharing, analysis, testing, and cross-utilization of anonymised/processed data.
At the beginning of the project, five smart building-related applications were identified, namely, smart chair, smart lighting, smart transport, energy, health and wellbeing, all underpinned by the scientific work conducted across technical Work Packages, beneficiaries and partners, to support theoretical, algorithmic and practical implementation aspects of these applications.
The specific research activities include: (a) developing advanced sensing for smart buildings via a combination of integrated micro-spectrometers for local environmental monitoring and wearable, paper-like flexible sensors based on organic electronics; (b) energy-efficient IoT-enabled fusion of data from physical and virtual sensors; (c) data mining and learning algorithms; and (d) integration of the developed sensing, communications and data analysis into an overall decision support system.
The main research achievements so far include:
1) New sensors for smart buildings encompassing environment, occupants, transport and building infrastructure, including SHFT microspectrometers incorporating hardware athermalization and paper-like sensors based on organic electronics and ferroelectric materials
2) Energy-efficient IoT-based architecture for acquiring signals from smart objects
3) Novel near real-time data science approaches for extracting meaningful information from the acquired data, including data representation methods for dimensionality reduction and computationally-efficient, data mining algorithms for large, distributed and heterogeneous datasets.
4) Integration of sensor design, communication and information processing results via predictive data analytics, scalable data management and decision support tools, into a smart chair, smart lighting, smart parking, energy and wellbeing systems.
Two SENSIBLE schools were organised as planned, with a range of courses delivered that contributed to expanding research and transferable skills of researchers. In addition, joint student mentoring and research output co-production supports exchanging best practice and improving teaching and research delivery at individual institutions.
