Your browser does not support JavaScript!

Machine-learning-enabled smart cities

Machine-learning-enabled smart cities

For access to this article, please select a purchase option:

Buy chapter PDF
(plus tax if applicable)
Buy Knowledge Pack
10 chapters for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Communication Technologies for Networked Smart Cities — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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 Smart business
  • 10.8.1 Financial services
  • Loan default prediction
  • Online fraud detection
  • 10.8.2 Marketing
  • Product recommendations
  • Email marketing
  • Online customer support
  • 10.9 Standardising smart cities
  • 10.10 Conclusion
  • References

Inspec keywords: town and country planning; learning (artificial intelligence); Internet of Things; relational databases; sensors; information retrieval; smart cities

Other keywords: machine learning; smart city; raw data; IoT; urban surroundings; unstructured data; relational database; ML-assisted smart cities; city planners; information retrieval

Subjects: Data handling techniques; Public administration; Relational databases; Ubiquitous and pervasive computing; Information retrieval techniques

Preview this chapter:
Zoom in

Machine-learning-enabled smart cities, Page 1 of 2

| /docserver/preview/fulltext/books/te/pbte090e/PBTE090E_ch10-1.gif /docserver/preview/fulltext/books/te/pbte090e/PBTE090E_ch10-2.gif

Related content

This is a required field
Please enter a valid email address