Internet Enabled Consumer Devices

6 non-sensor data gathering technologies for smart cities

22 November 2022
While sensors in smart cities will be essential, such as those mounted on streetlights or at intersections, non-sensor data gathering technologies will be equally as important. Source: Takashi Watanabe/Unsplash

Technology has enabled humans to work smarter and more efficiently. In theory, this allows them to be more productive. It’s an even taller order for developers in smart city environments: to harness technologies like artificial intelligence (AI) to enable urban environments to operate more efficiently, utilize resources more intelligently, reduce crime and pollution, improve mobility, rid cities of traffic backlogs, enhance community safety, encourage social inclusivity, attract and support business, provide more infrastructural services, support the vulnerable, make city information available to citizens at the click of a button, and offer ordinary people a sustainable, eco-friendly lifestyle.

Smart cities are made possible by the intelligent gathering and utilization of data from numerous sources. But development is something of a moving target as technology matures. Whereas the internet of things (IoT) used to be the buzzword when the concept of smart cities was popularized, blockchain looks to be the future of intelligent, sustainable communities.

Data gathering technologies for smart cities

To build smart cities, big and small data from IoT devices, people, infrastructural assets and smart sensors must be collected and analyzed. Some of the data gathered includes inputs from traffic, parking and commuter mobility sensors; weather reports and environmental data; population statistics and census information; historical data from technical studies; hospital triage statuses; crime statistics; predictive analytic reports, library data; home energy consumption data, social media information and small data from IoT-enabled devices like wearables.

The process of data gathering involves a combination of methods and techniques, including the use of Node-RED blocks, extract, transform and load (ETL) processes, semantic modeling, AI, web crawling and blockchain technology.

Node-RED

Node-RED is a tool that allows developers to visualize and integrate flows from hardware devices, APIs, and online services and manage these nodes through a graphical user interface (GUI). In smart city applications, node-RED connects the dots between system components. Node-RED provides blocks of functionality, for instance, geospatial information, gathering historical data, device discovery, creating custom dashboards and data storage. With node-RED, organizations can mix and match features. Distributed node-RED (DNR) supports the use of fog computing to deploy smart city applications on a large scale across a variety of devices, from traffic cameras to data storage servers.

ETL

ETL combines data from multiple sources into a single data store that is stored in a data warehouse, data lake, or other database system. It enables IT departments to obtain a holistic view of data across an organization, supporting the decision-making process undertaken by different stakeholders, from developers to managers. Large organizations use ETL to analyze historical data, such as traffic or purchasing patterns to better manage assets, synchronize the sharing of data with different companies and departments, automate data integration and reporting, and provide city residents with access to one-stop, interactive services.

Semantic modeling

According to Gartner, semantic modeling is a "method of organizing data that reflects the basic meaning of data items and the relationships among them. This organization makes it easier to develop application programs and to maintain the consistency of data when it is updated." Semantic modeling enables the sharing and merging of data between systems using a common language and addresses the issue of enabling syntactic interoperability where there are large volumes of heterogeneous data.

Artificial intelligence

AI sifts through large quantities of big data to generate input data and predict events for smart city technologies. For example, AI applications are used to track suspicious behavior in feeds from CCTV cameras, to assess the state of city infrastructure and make suggestions about maintenance programs, and to track resident behaviors like recycling habits come up with more eco-friendly solutions.

Web crawling

Web crawling automatically searches web pages for particular keywords and creates an index of information. Web scraping involves extracting this data. In smart city applications, web crawlers may harvest information from sources as diverse as social media websites and live traffic streams.

Blockchain

Blockchain technology is harnessed in smart cities to store and exchange data in a decentralized way without reliance on third parties. What makes it unique is that it manages digital identities using digital signatures based on public key cryptography instead of unsecure password systems. Blockchain technology can help organizations redesign the way services are provided and reduce centralized administration. By its nature, blockchain ensures the transparency of, and accountability for, transactions.

(Read more about smart city applications for blockchain on Globalspec)

Two real-world smart city data gathering use cases

At the Philadelphia Water Department (PWD), curb side reading of water meters meant the utility was losing money and the data was often inaccurate. In 1997, the PWD installed water meters that automatically sent readings to the utility, with a significant improvement in customer satisfaction, accuracy of data, and cost savings. Twenty years later, the PWD implemented smarter city changes. It installed an advanced metering infrastructure (AMI) that provides customers with real-time information about their accounts, like abnormally high water usage, and allows the PWD to more easily maintain its infrastructure, for instance by remotely monitoring for leaks.

In Boston, when potholes had become a problem and the workflow to fix them untenable, the city launched a mobile app that residents could use to report potholes instead of phoning the council. The city calls it “participatory urbanism” – an example of the future of people-focused smart cities.

While data-driven smart cities are well under development, future cities will integrate socio-economic capital and citizen participation. “To be successful, future cities would need to combine integrated social, environmental, and economic aspects with digital, smart, intelligent, networked, and resilient with aesthetically pleasing concepts supported by an overarching governance system. It is recognized that improving digital literacy and digital culture of urban communities forms a key role in achieving these new paradigms of sustainable future cities."

Conclusion

According to Juniper Research, the number one rated smart city for 2022 is Shanghai, offering over 1,000 different services to residents, but Oslo, Singapore, New York, London, Barcelona, Beijing, Seoul and Copenhagen are not lagging behind. Smart cities are a billion-dollar industry and, when combined with four-day work weeks where people go shopping on their day off, may see the end of the COVID-19 recession. But it can’t be done without citizen participation as some big cities are realizing, where city residents have a say in how data is used to address their real everyday needs.

To contact the author of this article, email engineering360editors@globalspec.com


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