City Data Fusion: Sensor Data Fusion in the Internet of Things

City Data Fusion: Sensor Data Fusion in the Internet of Things

Meisong Wang (Research School of Computer Science, Australian National University, Canberra, Australia), Charith Perera (The Open University, Milton Keynes, UK), Prem Prakash Jayaraman (RMIT University, Melbourne, Australia), Miranda Zhang (Research School of Computer Science, Australian National University, Canberra, Australia), Peter Strazdins (Research School of Computer Science, Australian National University, Canberra, Australia), R.K. Shyamsundar (School of Technology & Computer Science, Tata Institute of Fundamental Research, Navy Nagar, India) and Rajiv Ranjan (CSIRO, Canberra, Australia and Newcastle University, Newcastle, UK)
Copyright: © 2016 |Pages: 22
DOI: 10.4018/IJDST.2016010102
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Abstract

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. The authors introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. They then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. The authors' main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.
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2. Motivation: Sensor Data Fusion For Smart City Application

Data from citizens, systems, and general things flow through our cities thanks to the wide spread adoption of smart phones, sensor networks, social media and growing open release of datasets (Antonelli et al., March 19-20, Athens, Greece, 2014). The data from Smart cities present a grand challenge to researchers and smart cities promoters, as we need to take advantage of these streams of information to build new services and define a clear return of investment for the benefit of the society (Jara, Genoud, & Bocchi, 2014).

The challenge in smart city is not to build a single generic model e.g. weather model based on temperature and humidity, complex models about noise pollution, traffic etc., but to combine all these together to build a good predictive contextually rich model. This model will help understand the dynamics of the society, and most importantly provide vital knowledge back to the citizens in order to enhance their quality of life.

A recent work from a group of researchers from MIT (Sobolevsky et al., 2015) demonstrate the potential of fusing data from disparate data sources in smart city to understand a city’s attractiveness. The work focuses on cities in Spain and shows how the fusion of big data sets can provide insights into the way people visit cities. Such a correlation of data from a variety of data sources play a vital role in delivering services successfully in smart cities of the future.

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