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## Machine-learning-enabled smart cities

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Communication Technologies for Networked Smart Cities — Recommend this title to your library

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A smart city leverages the Internet of Things (IoT) and sensors to collect the available wealth of raw data from various urban surroundings. This huge volume of unstructured data gleaned in real time needs to be effectively analysed and utilised to spot trends, which can give city planners the information that is highly responsive to the needs of the citizens. The massive amount of information presented in this data is difficult to be viewed and processed by humans. Here, the information retrieval via machine learning (ML) helps in extracting knowledge or structured data from the unstructured form by recognising the underlying pattern. It produces a summarised tabular output in a relational database, which helps one to optimise the given set of services for enhanced functioning and sustainability of the city, such as predicting parking spots for drivers, helping first responders, and locating dangerous intersections. The factors responsible for surging interest in ML are powerful computational processing and cost-effective data storage options, which allow training models that gain experience by quickly and accurately analysing huge chunks of complex data. ML combined with the IoT helps to realise the vision of a more livable and resilient city that is capable of quickly responding to the critical challenges prompted by an outrageous urban population, encompassing traffic congestion, environment deterioration, sanitation issues, energy crises, thwart crime, healthcare, and many more. It can automate municipal operations and advance smart city initiatives at large. In this chapter, a comprehensive list of applications is curated to understand the nuts-and-bolts of ML in the domain of the smart city. The chapter walks through the recent applied examples alongside familiarising with the key research developments in the context of ML-assisted smart cities. Ultimately, the chapter concludes by mentioning the major challenges faced by the implication of ML as a smart city use case. On that account, we are focusing on various examples of ML in a smart city.

Chapter Contents:

• 10.1 Machine learning in the context of smart city
• 10.1.1 Supervised learning
• 10.1.2 Unsupervised learning
• 10.2 Smart grid
• 10.2.1 Smart grid operation
• 10.2.2 Smart grid security
• 10.2.3 Renewable energy systems
• 10.3 City mobility
• 10.3.1 Traffic prediction
• 10.3.2 Online transportation networks
• 10.3.3 Self-driving vehicles
• 10.3.4 Efficient parking garages
• 10.3.5 Traffic management
• 10.4 City security and safety
• 10.5 Smart healthcare
• 10.6 Smart environment
• 10.6.1 Smart air monitoring
• 10.6.2 Smart waste management
• 10.7 Smart home automation
• 10.7.1 Device management
• 10.7.2 Energy management
• 10.7.3 Home security
• 10.7.4 Home organisation
• 10.8.1 Financial services
• 10.8.1.1 Loan default prediction
• 10.8.1.2 Online fraud detection
• 10.8.2 Marketing
• 10.8.2.1 Product recommendations
• 10.8.2.2 Email marketing
• 10.8.2.3 Online customer support
• 10.9 Standardising smart cities
• 10.10 Conclusion
• References

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