Understanding and Personalising Smart City Services Using Machine Learning, the Internet-of-Things and Big Data

Chin, Jeannette (2017) Understanding and Personalising Smart City Services Using Machine Learning, the Internet-of-Things and Big Data. In: The 26th IEEE International Symposium on Industrial Electronics, 19-21 June 2017 Edinburgh, Scotland, UK, 19-21 June 2017, Edinburgh, Scotland, UK.

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Abstract

This paper explores the potential of Machine Learning (ML) and Artificial Intelligence (AI) to lever Internet of Things (IoT) and Big Data in the development of personalised services in Smart Cities. We do this by studying the performance of four well-known ML classification algorithms (Bayes Network (BN), Naïve Bayesian (NB), J48, and Nearest Neighbour (NN)) in correlating the effects of weather data (especially rainfall and temperature) on short journeys made by cyclists in London. The performance of the algorithms was assessed in terms of accuracy, trustworthy and speed. The data sets were provided by Transport for London (TfL) and the UK MetOffice. We employed a random sample of some 1,800,000 instances, comprising six individual datasets, which we analysed on the WEKA platform. The results revealed that there were a high degree of correlations between weather-based attributes and the Big Data being analysed. Notable observations were that, on average, the decision tree J48 algorithm performed best in terms of accuracy while the kNN IBK algorithm was the fastest to build models. Finally we suggest IoT Smart City applications that may benefit from our work

Item Type: Conference or Workshop Item (Paper)
Keywords: classification, personalisation, machine learning, artificial intelligence, profiling, data mining, recommendation systems, algorithms, internet of things, smart cities, Data Analytics
Faculty: Faculty of Science & Technology
Depositing User: Unnamed user with email jeannette.chin@anglia.ac.uk
Date Deposited: 13 Jun 2017 15:13
Last Modified: 13 Jun 2017 15:16
URI: http://arro.anglia.ac.uk/id/eprint/701851

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