Accepted papers will be published in WWW companion proceedings on ACM digital library. Page length is limited to 6 pages, the same format as WWW submissions, i.e., ACM SIG Proceedings template (http://www.acm.org/sigs/publications/proceedings-templates).
Cities are experiencing significant challenges in many aspects such as efficient energy management, economic growth and development, security and quality of life of their citizens. In the era of big data, mobile internet and cloud computing, we are now provided with good opportunities to leverage the crowd intelligence to better sense and manage the city. The meaning of the word “crowd” is mainly two-fold, in terms of the data collection and fusion.
– From the perspective of data collection, participatory sensing (people-centric sensing/mobile crowdsensing) presents a new paradigm based on the power of mobile devices. The sheer number of user-companioned devices, including mobile phones, wearable devices, and smart vehicles, and their inherent mobility enables that we can acquire local knowledge (e.g., location, personal and surrounding context, noise level, traffic conditions) through sensor-enhanced mobile devices.
– From the perspective of data fusion, a variety of open datasets from multiple domains are available nowadays, from social media to public transportation, from health care to wireless communication networks. When addressing a specific problem in smart cities, we usually need to harness multiple disparate datasets. For example, to create a fine-grained air pollution monitoring map in a city, we need to explore air quality data reported by monitor stations, together with meteorological data, emissions from vehicles and factories, as well as the dispersion condition of a place.
Topics of CISC include but not limited to:
– Smart City Big Data Collection and Analysis
– Crowd Intelligence based Smart City Systems, Middleware and Framework
– Crowd Intelligence based Smart City Application
– Security, Trust, and Privacy Issues in Crowd Intelligence
– Incentive Mechanism for Crowd Intelligence based Smart City Applications
– Missing Data Inference Techniques in Smart Cities
– Optimizing Crowd Intelligence based Systems for Smart Cities
– Leye Wang (firstname.lastname@example.org), Hong Kong University of Science and Technology, Hong Kong
– Jiangtao Wang (email@example.com), Peking University, China
– Haoyi Xiong (firstname.lastname@example.org), Missouri University of Science and Technology, USA