Ubiquitous and mobile context-aware computing is an essential component of the smart cities infrastructure. Widely available wireless networks, the maturity level of distributed computing and the increasing number of mobile devices have significantly influenced the human experience with computing. In the present paper, we discuss the need for a model that will be able to represent a formal structure of a context-aware system in a device. The core functionality of the model is expected to expose context-aware behaviour and support dynamic integration of mobile devices and context-aware behaviour. The major contribution of this work is to identify deficiencies of the existing model which is using the notions from sociology such as Role, Ownership and Responsibility.

Abstract:The Heat Energy Assessment Technologies (HEAT) project uses high-resolution airborne thermal imagery, Geographic Object-Based Image Analysis (GEOBIA), and a Geoweb environment to allow the residents of Calgary, Alberta, Canada to visualize the amount and location of waste heat leaving their houses, communities, and the city. To ensure the accuracy of these measures, the correct emissivity of roof materials needs to be known. However, roof material information is not readily available in the Canadian public domain. To overcome this challenge, a unique Volunteered Geographic Information (VGI) application was developed using Google Street View that engages citizens to classify the roof materials of single dwelling residences in a simple and intuitive manner. Since data credibility, quality, and accuracy are major concerns when using VGI, a private Multiple Listing Services (MLS) dataset was used for cross-verification. From May-November 2013, 1244 volunteers from 85 cities and 14 countries classified 1815 roofs in the study area. Results show (I) a 72% match between the VGI and MLS data; and (II) in the majority of cases, roofs with greater than, or equal to five contributions have the same material defined in both datasets. Additionally, this research meets new challenges to the GEOBIA community to incorporate existing GIS vector data within an object-based workflow and engages the public to provide volunteered information for urban objects from which new geo-intelligence is created in support of urban energy efficiency.

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