Harnessing Crowd-Sourced Big Data for Driving Next Generation Location-Based Services in Future Cities
The project will study how to deliver next-generation location-based services in smart buildings. AI and big data algorithms will be fused in order to process user-generated automatic crowdsourced data from sensors on registered users’ smartphones and/or wearable devices as well as installed Internet of Things (IoT) units in buildings to build a dynamic knowledge base on users’ location to deliver targeted location-based services. The developed algorithms will be universal for any smart building although a modern museum will be used as a first case study.
Project Team and where the student will be based
Professor Tughrul Arslan holds the Chair of Integrated Electronic Systems in the School of Engineering, University of Edinburgh. He is a member of the Integrated Micro and Nano Systems (IMNS) Institute and leads the Embedded Mobile and Wireless Sensor Systems (EWireless) Group in the University (ewireless.eng.ed.ac.uk). His current research focuses on enhancing personal mobility and the design of high performance embedded wireless systems. An important issue in his research is investigation techniques based on crowdsourcing and big data techniques for enabling location and location-based services in areas of poor GPS visibility. He has supervised over 40 successful PhD research thesis and is the author of over 400 refereed research papers and the inventor of over 20 patents in these areas.
He has led a number of successful projects in the design of low power embedded wireless systems. These have resulted in new patented technologies such as the design of algorithms, including Machine learning and AI-based, for the exploitation of wireless signals for personal navigation indoors and areas of poor GPS signal visibility. Most of his technologies have been licensed to spin-out companies or bought by Tier 1 companies. He has co-founded a number of spin-out companies including Sensewhere, which is the world leader in indoor location and location-based services. Prof. Arslan is a member of the research committee of Bays Centre for innovation in Big Data and AI in Edinburgh.
Dr Qunshan Zhao’s research interest focuses on creating a sustainable urban future and tackling related social, economic, and environment problems by using new forms of urban big data and advanced analytical approaches including GIScience (geographic information systems, remote sensing and spatial analysis), machine learning/statistics, operations research, sensor networks, and urban climate modelling and instrumentation.
The PhD student would be based primarily in the University of Edinburgh. They will also be a part of the Urban Big Data Centre (UBDC), part of the Urban Studies Group within the School of Social & Political Sciences in The University of Glasgow.
The Urban Big Data Centre is an ESRC-funded research centre and national data service. UBDC promotes the use of big data and innovative research methods to improve social, economic and environmental well-being in cities. The Centre publishes world-leading research in the social sciences and other disciplines that is distinguished by its critical engagement with debates about new forms of data (big data) and data-driven urban analytics. The data service enhances the quality and accessibility of urban big data, and the methods for urban analytics, supporting a wide range of applications and users. Through its research and our data service, UBDC delivers a wide range of positive impacts on society, the economy and the environment. It works closely with a wide range of government, industry and third sector partners to ensure a proper understanding of the challenges they face and to identify opportunities for its work to influence policy and practice.
Urban Studies is an internationally-leading centre for urban research. It was rated joint-first in the last Research Excellence Framework (REF) exercise within its Unit of Assessment (Architecture and the Built Environment). It comprises an academic group of around 35 urban scholars from multiple disciplines. In addition to UBDC, there are two further major research centres: the ESRC-funded CaCHE (the UK Collaborative Centre for Housing Evidence) and GCRF-funded SHLC (the Centre for Sustainable, Health and Learning Cities and Neighbourhoods). The department has a vibrant research culture, with regular internal and external seminar series and thematic research groups which bring early, mid and late-career staff together with PhD students. There is an extensive community of early-career scholars as well as the next generation being developed through the PhD programme. There is a very vibrant programme of Masters teaching, much of it validated by one of three different professional bodies, ensuring close engagement with professional practice and policy.
All the existing research network and activities will be extremely helpful for enhancing the research programme and career development of the applicant.
- Academic qualifications - A Master’s Degree at 2:1 level or above in either Electrical Engineering or Computer Science (or a related discipline), Geographic Information Science/Remote Sensing, or Urban Analytics.
- Experience – Experience of urban modelling and indoor location-based modelling and analysis is desirable but not essential.
- Skills/Attributes – Skills in the use of programmable software (e.g. Python, R or MatLab) for data analysis are essential. Skills in the use of Java Mobile programming and Geographical Information Systems are desirable but not essential.
Enquiries about this project should be directed to Professor Tughrul Arslan - email@example.com.