According to the World Bank, around 56% of the world’s population lives in cities. By 2050, this figure is predicted to rise to 70%. This acceleration towards urbanisation is putting city infrastructure under enormous strain. Congested highways, crammed public transport, overworked waste management systems and sporadic urban planning are just a few consequences of our collective influx into cities.
Some cities in the Asia Pacific, such as Singapore, Jakarta and Osaka, have turned to technology to deal with the challenges of growing urban populations. These have become known as “smart cities”, leveraging advancements in sensor technologies, cloud computing and data infrastructure to serve residents, visitors and businesses better.
Data is at the core of all smart city applications. With the proliferation of new sensor technologies to capture data and new ways to process and transmit that data, a myriad of smart city applications has emerged to improve various aspects of city life.
Open, real-time data usage sits at the heart of Singapore’s Smart Nation vision. In 2011, data.gov.sg was launched as a data repository collected by 70 public agencies and made accessible openly and freely to citizens and developers. The platform also provides application programming interfaces and real-time public data to co-create useful solutions and digital services.
However, as smart city technology grows and data volumes increase exponentially, the ability to manage and gain faster insight from data has become critical to enabling the next generation of smart city applications.
Transportation
The fluid transportation of people and goods is fundamental to the well-being of a city, and data plays an increasingly important role in facilitating it. An accurate, real-time view of traffic flows can be used as the basis of smart traffic light systems or route optimisation platforms. These tools respond to traffic conditions as they occur, providing alternative route options to ensure travel flows as seamlessly as possible. Similar technologies have also been applied to parking (such as barrierless parking lots), toll road management (like automated payments), and public transportation management (such as real-time fleet tracking and route planning).
In Singapore, the Land Transport Authority (LTA) uses sensors to gather information on buses’ real-time location and arrival times at various stops to improve transport planning. As a result, LTA has seen a 92% reduction in bus services with crowding issues despite a year-on-year increase in average daily bus ridership.
In addition to developing intelligent transport systems, Singapore has seen advancements in A*Star’s development of a “large-scale fleet management solution” for automated guided vehicles and ST Engineering’s self-driving vehicle Auto Rider, which enhances visitor accessibility around local attraction Gardens by the Bay. Smart city applications such as these make urban travel faster, cheaper and safer, while reducing environmental impact.
Energy
The same data-centric advances have also helped reduce the energy consumption of public infrastructure. An example is smart street lighting, which reduces the demand for electricity by activating only when a person or vehicle is detected in the vicinity and in proportion to the luminosity of natural light (such as the intensity of light changing according to bright or cloudy conditions). It achieves this by collecting data via light, motion and weather sensors on each lamp, and processing that data at the edge to respond to current conditions.
The implications of such systems are significant. In Jakarta, 150,000 street lights were upgraded to energy-efficient LED and connected to remote management software. Performance data is sent to the city’s lighting office, enabling city officials to efficiently monitor the lighting infrastructure and remotely manage illumination levels to match different needs by district. For example, when traffic is lower during the evening, Jakarta’s lighting office can dim the lighting by 50%, resulting in large energy savings.
Waste management
Similar efficiency gains are achieved in waste management organisations. Smart sensors based on the Long Range Wide Area Network, for instance, can measure the fill level and content weight of individual waste bins and communicate this to a centralised database. This data can then be processed to automatically optimise the routes of waste collection trucks, ensuring that they only collect bins that are full. This reduces unnecessary bin collections and the chance of overflowing bins on streets.
In Osaka, city officials are turning to smart bins as tourist numbers — and the amount of waste — surge. The tourist-heavy Dotonbori district has installed around 20 new solar-powered rubbish bins, which automatically sense when they are full and compress the garbage by around 20%. The bins are also connected to a smartphone app that analyses data on waste volume and sends alerts to workers before they fill up.
The Internet of Things (IoT) innovation is also being applied downstream at waste sortation centres. Technology makes it possible by combining terahertz and infrared sensors with high-definition camera feeds that correctly identify waste items' material, quality, and age, as degraded materials have less recyclable value.
Once items are identified, they are physically moved by pressured air hoses to the relevant waste sortation belt. This process is enabled by the flow of real-time data from different devices, significantly reducing the time and costs of manual intervention in sortation while increasing the overall amount of recycled materials.
Maintaining the pace of smart city innovation
In all of the examples above, massive amounts of data are generated at a rapid pace, which needs to be stored, analysed and distributed instantly to different parts of the network. To continue the pace of smart city innovation in Asia Pacific, there is a need to quickly mine value from ever-increasing quantities of data.
While it is clear that IoT devices and the data captured are fundamental to smart city applications, the required capabilities of the underlying data infrastructure are less clear. An organisation's choice of message broker and data processing framework, in particular, can significantly impact the functionality of a smart city application.
Smart city applications rely on large volumes of heterogeneous data sources processed in real-time to function. However, the generation of data within smart city applications can fluctuate greatly. During peak commute times, smart sensors connected to transport systems generate significantly more data than during non-peak times.
Message brokers need to be able to scale elastically to meet this variable demand. When they fail, there needs to be a solid disaster recovery mechanism in place to ensure the applications continue without disruption. Smart sensors also need to be deployable in several environments to prevent data silos across organisations and ensure the flow of data to the right environment.
To navigate these issues, businesses and public-sector organisations can adopt data streaming, continuously processing data as it is generated or received and making it available for real-time triggers and analysis.
As the population grows in urban areas, the role of smart city applications will only become more important as the demand for responsiveness intensifies. The increased data generated by the growing range of sensors and AI applications will require real-time processing enabled by data streaming to help power the applications which regulate urban life. Data streaming platforms will help establish the foundation for the next generation of smart city applications, making the lives of citizens simpler, greener and more efficient.
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Autor(en)/Author(s): Suvig Sharma
Quelle/Source: The Edge, 31.07.2024
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