In-cooperation with ACM SIGAPP, SIGCHI and SIGSPATIAL2016
Thank you all and see you in UK 2017!
"Guns of Brixton: which London neighborhoods host gang activity?"Alessandro Venerandi, Giovanni Quattrone and Licia Capra (University College London).
"Urban Anomaly Detection: a Use-Case for Participatory Infra-Structure Monitoring"Julio Borges, Till Riedel and Michael Beigl (Karlsruhe Institute of Technology).
"An Easy Infrastructure Management Method Using On-Board Smartphone Images and Citizen Reports by Deep Neural Network", Hiroya Maeda, Yoshihide Sekimoto and Toshikazu Seto (The University of Tokyo).
"Spatial Behaviors of Individuals in Cities: Case Studies in Data Tracking and Scaling", Lynnette Widder (Columbia University), Joy Ko (Rhode Island School of Design Department of Architecture), Jessie Braden (Pratt Institute Spatial Analysis/Visualization Initiative) and Kyle Steinfeld (University of California, Berkeley).
Conference Photos (You can also upload your photo here)
Urban spaces are the man made microcosms where a number of entities interact with each other to offer citizens a variety of services, for instance, buildings and infrastructure, transportation, utility, public safety, healthcare, education. The interplay between this multitude of connecting entities creates a complex system with dynamic human, material, and digital flows. By 2050 the world's urban population is expected to grow by 72%. This steep growth creates an unprecedented urge for understanding cities to enable planning for the future societal, economical and environmental well being of their citizens. The increasing deployments of Internet of Things (IoT) technologies and the rise of so-called ‚Sensored Cities‚ are opening up new avenues of research opportunities towards that future. Although, there have been a number of deployment of diverse IoT systems in the urban space, our understanding of these systems and their implications has just scratched the surface.
Urb-IoT is a new conference that aims to explore these dynamics within the scope of the Internet of Things (IoT) and the new science of cities.
The event is endorsed by the European Alliance for Innovation, a leading community-based organisation devoted to the advancement of innovation in the field of ICT.
All accepted papers will be submitted for publication in ACM and made available through ACM Digital Library, one of the world's largest scientific libraries.
Proceedings are submitted for inclusion to the leading indexing services: Elsevier (EI), Thomson Scientific (ISI), Scopus, Crossref, Google Scholar, DBLP
The goal of the conference is to solicit original and inspiring research contributions from technology experts, designers, researchers, urban planners, and architects in academia and industry. Bringing together practitioners and researchers to share knowledge, experiences, and best practices, Urb-IoT 2016 seeks multi-disciplinary contributions in the area of
Citizen Awareness and Engagement: Methods and studies for citizen involvement through participatory sensing or crowd-sourcing for urban tasks, as well as behavioural change of the citizen through awareness.
Urban Analytics: Understanding the massive digital traces created by IoT in the urban landscape through big data analytics.
IoT Applications and Services in Urban Context: Urban technologies and applications that challenge the state of the art and benefit citizens, decision and policy makers, and urban planners.
Urb-IoT was started at 2014 with more than 50 participants from various international countries, and with presentations of 15 accepted papers from 58 submissions. You can access the publication in ACM Digital Library: http://dl.acm.org/citation.cfm?id=2694768
In Urb-IoT 2016, we also expect to have great research and experiences papers on this field, and share and discuss our latest research issues and ideas with various participants leading this area.
Topics are themed by urban space and include, but are not limited to:
Monitoring the pulse of the city
Fusion of heterogeneous urban sources
Understanding urban data using machine learning and mining techniques
Visual analytics of urban data
City as a platform
Participatory and crowd sourcing techniques
Incentification and gamification
Citizen and crowd influence and behavioural change
Data-driven urban planning and design
Crowd behaviour capturing and modelling
Urban mobility and intelligent transportation systems
All papers will be reviewed by an international program committee with the appropriate expertise.
At least two members of the Program Committee and a set of external expert reviewers will review submitted papers. At a PC meeting, the committee will select the papers to be presented at Urb-IoT 2016. Research contributions will be selected on the basis of novelty, technical merit, and a clear presentation.
Submissions must clearly articulate how they relate to solve a particular problem in the scope of urban spaces. Contributions describing the role of IoT in understanding urban dynamics as well as real world systems in sensing and interpreting city scale signal are particularly of interest.
Submitted papers for review must not exceed six (6) pages and should be in PDF and formatted in the Double Column format. Urb-IoT 2016 adopts a double-blind process for submitted contributions. Authors' names and their affiliations must not be revealed or mentioned anywhere in the submission. Detailed format and submission instructions including style templates for MS Word and LaTex are provided at the conference website.
In addition to papers, we also have poster/demonstration session in the conference. Notes/Posters/Demonstration are also an important part of Urb-IoT program. They provide researchers with an opportunity to present their latest, cutting-edge research. Notes/Poster/Demonstration papers must not exceed three (3) pages with same format of the regular papers, and also will be reviewed by the program committee.
February 7: Paper/Poster/Demonstration Submission Deadline
February 22: Abstract Registration
February 29: Paper/Note/Poster/Demonstration Submission Deadline(extended)