Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities
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Self-Building Artificial Intelligence and Machine Learning to Empower Big Data Analytics in Smart Cities Damminda Alahakoon 1 & Rashmika Nawaratne 1 & Yan Xu 2 & Daswin De Silva 1 & Uthayasankar Sivarajah 3 Bhumika Gupta 4
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# The Author(s) 2020
Abstract The emerging information revolution makes it necessary to manage vast amounts of unstructured data rapidly. As the world is increasingly populated by IoT devices and sensors that can sense their surroundings and communicate with each other, a digital environment has been created with vast volumes of volatile and diverse data. Traditional AI and machine learning techniques designed for deterministic situations are not suitable for such environments. With a large number of parameters required by each device in this digital environment, it is desirable that the AI is able to be adaptive and self-build (i.e. self-structure, self-configure, self-learn), rather than be structurally and parameter-wise pre-defined. This study explores the benefits of self-building AI and machine learning with unsupervised learning for empowering big data analytics for smart city environments. By using the growing self-organizing map, a new suite of self-building AI is proposed. The self-building AI overcomes the limitations of traditional AI and enables data processing in dynamic smart city environments. With cloud computing platforms, the selfbuilding AI can integrate the data analytics applications that currently work in silos. The new paradigm of the self-building AI and its value are demonstrated using the IoT, video surveillance, and action recognition applications. Keywords Big data analytics . Self-building AI . Machine learning . Smart cities . Self-organizing maps
1 Introduction Urban migration is an increasing trend in the twenty-first century and it is estimated that more than 68% of the worlds’ population will live in urban environments by 2050 (World population projection by UN 2018). Such migration will strain the abilities of cities to cope and this situation has created an urgent need for finding smarter ways to manage the challenges such as congestion, traffic and transport, increased crime rates, social disorder, higher need and distribution of utilities and
resources, etc. with smart cities being proposed as the solution (Gupta et al. 2019). Using technological advancement as the base, smart cities are expected not only to cater to the needs of a huge increase in population, but also provide improved living environments, business functions, utilize resources more efficiently and responsibly as well as be environmentally sustainable (Kar et al. 2019; Pappas et al. 2018). In such environments, the city and home infrastructures, human behaviors and the technology which captures such behaviors in digital form develop in to an eco-system with dependencies and
* Uthayasankar Sivarajah [email protected]
Bhumika Gupta [email protected] 1
Research Centre for Data Analytics and Cognition, La Trobe Business School, La Trobe University, Bundoora, Victoria 3086
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