Ubiquitous sensors and applications are driving rapid growth for smart cities, but machine learning not yet advanced to cope with capacity demands.
Ubiquitous sensors in mobile robots, aerial drones, and autonomous vehicles, plus connections to municipal infrastructure through the Internet of Things, promise more efficient delivery of utilities and reduced traffic, among other things. While the variety of sensors and applications for smart cities has grown rapidly in recent years, a lot of work remains, especially in the areas of machine learning to analyze and interpret the data from these sensors, experts and observers said.
“[There is] still a long way to go to achieve a smart city,” said Mateja Kovacic, a visiting research fellow at the Urban Institute at the University of Sheffield, and a postdoctoral research fellow a the Nissan Institute of Japanese Studies, University of Oxford. Kovacic points to a number of notable examples in the smart-cities-in-progress space, including Barcelona, Spain, and Dubai.
In Barcelona, municipal authorities have installed smart solar trash cans, and free Wi-Fi routed via street lighting, as well as “sensors that monitor air quality and parking spaces,” Kovacic said.
In Dubai, which established a blockchain strategy aimed at creating the world’s first blockchain-powered government, the country also has an autonomous transportation strategy that seeks to make 25% of all transportation in the city autonomous by 2030.
“There are also efforts to make policing, security, governance, healthcare and public services more autonomous through artificial intelligence, which I see as an extension and expansion of the ‘smart’ paradigm,” Kovacic said.
Autonomous robotics in the next age
Joshua Meler, senior director of marketing at Hangar Technology, said he believes the world is entering an age in which autonomous robotics “will transform how companies operate, industries evolve, and economic opportunities are uncovered.”
Hangar develops a platform that combines drone hardware, software, and data analytics to enable autonomous drones to collect and interpret visual data. Meler said that up until now, the platform has been employed mainly for business uses – via drones that automate the end-to-end “aerial insight” supply chain. But he said it has also been rolled out for smart city infrastructure applications in controlled environments, and at a limited capacity.
“The next stage for Hangar is an era where computers augment visual insights, automating observations and alerting humans of areas that require attention,” Meler said. “This includes counting traffic, identifying cracks on bridges, recognizing inventory on construction sites and more — without human intervention required. We’re not there today, but as technology advances and as regulations facilitate autonomous operations, the Hangar platform will be capable of facilitating many of the applications of smart cities.”
Partnerships drive applications in Finland
The city of Tampere, Finland, is in the process of establishing several innovative and digital smart city solutions through cooperation between companies, organizations, municipalities, and citizens. Pirkko Laitinen, communications manager for Smart Tampere, said the aim is to “create better services for the citizens, and serve as a partner, a platform, and a reference for the companies on their way to the international markets.”
She said the strategic economic program approaches this in two ways. From the inside, the program is “taking the city’s own services to the digital age through agile testing.” On the outside, the program helps businesses “create new business models and smart city solutions through ecosystem building and platform creating.”
Pirkko Laitinen, Smart Tampere
The program focuses on seven smart city themes that Laitenen said are strong in Tampere:
- Governance and citizens
- Research and education
- Buildings and infrastructure
“One robot-based new business model we have created with companies is the SmartMile delivery service points, which are in shared use among all parcel delivery service providers,” Laitinen said. “[This] means that online store customers can receive all their orders in one place. The robotics inside the machine is done by Konecranes.”
Machine learning needs to increase capacity
With increasing demands placed upon machine learning by smart city applications, Kovacic said she believes it will soon be advanced enough to cope with those demands.
The main challenge, she said, is that current machine learning “does not yet have the capacity to handle the quantity of data, and is not autonomous enough to analyze data without human intervention.” Another challenge is integrating different physical and virtual technologies necessary to make a smart city genuinely smart.
“The existing challenges can be overcome by further work on machine learning technology and nurturing a mindset with a holistic, integrative approach,” Kovacic said. “But there is no leapfrogging here, it simply takes time.”
Automobiles with sensors will provide data to smart cities. Source: Smart Tampere
“Another step toward overcoming existing challenges is being more aware that the physical technology, like robots, is an integral aspect of machine learning and vice versa,” she added. “There is no place for mind-body dualisms – or virtual-physical – there needs to be an attempt at integration. Lastly, cybersecurity and data privacy and protection are among the main issues and will need to be dealt with utmost care and consideration for individual and social rights and needs.”
Meanwhile, Laitinen pointed out that machines can currently learn simple tasks and that the technology is developing as the algorithms get better.
“As a city, we are still learning about what would be the best way to gather data from multiple different areas into one pool,” Laitenen said, “and how to analyze it in order to offer it for the companies to use.”
The skies will get smarter before the ground
Hangar’s Meler said he believes key sensor innovation and smart city trends “will happen in the sky before they happen on the ground.” The path to an autonomous world “must first rise up, in a largely uninhabited space free of children chasing soccer balls across the light, running groups beating the crosswalk light, or distracted drivers listening to the radio and texting a friend,” Meler said.
“The fact is, completely autonomous drones are years away, while cars and robotics will take at least a decade before they prove safe at scale,” he added. “For this reason, I think we’ll see meaningful innovation [in the air] first. Drone hardware will enable heavier payloads and longer flight times. Sensors will get smaller, better and cheaper. Governments and industries will lift regulations and restrictions. And this Solow’s Paradox we’re experiencing with digitization will hit a tipping point, and we’ll enter a new age of productivity.”
Kovacic said she envisions the “full integration of vehicles, drones, and robot-mounted sensors with the city through IoT,” particularly since “the quantity of data a city can produce exceeds human capacity, and needs a sophisticated network of everything.”
“Swarm technology is very promising and can be applied in vehicles, drones and different robots to produce collective action and decision-making,” Kovacic said. “Another key innovation may be decentralization. Unlike the old smart-city paradigm, where stationary sensors and cameras collect data and send it to a centralized system for analysis, the analysis and decision-making will become dispersed, decentralized and more efficient and instantaneous.”
“[A] smart city will no longer be a static accumulator of data but will become extended through mobile technology with capability to interact with each other and instantaneously make decisions based on this interaction without human intervention,” she added.
Even so, Kovacic said she suspects such developments will take more than a few years, and envisions a proliferation of various robots, such as drones for e-commerce, shared autonomous vehicles, service and retail robots, and city maintenance swarm robots. She also said she expects to see more machine learning-enhanced services across a smart city, from governance to the service industry.
“In California, there is currently underway a pilot project where autonomous vehicles pick up passengers, and delivery robots deliver groceries and food,” Kovacic said. “These are just two examples of what we can expect from future smart city applications – but only when these technologies are also connected and interact with the city — which they are currently not — and when there is a feedback loop between them and the city – a truly cybernetic city.”