SUSTAINABILITY

When we talk about the Internet of Things and the high-tech, precision machinery involved, it’s easy to limit our view to things that happen only inside—in factories, hospitals, and other buildings. But plenty of IoT applications are for the built world outside as well, and some of the most compelling involve smart cities. Smart cities provide a new way of thinking about traffic, utilities, transportation, and a host of other complex systems.

But step out into the urban environment, and another factor comes into play, one that we’ve become more and more aware of in recent years—sustainability. Cities are major producers of pollution and carbon emissions, so it’s critical that the transition to smart city can also deliver sustainability solutions, even taking the burden off human efforts to improve city livability.

Experts Jody Cheng, Product Solution Manager, and Manny Hicaro, Application Engineer Supervisor, at Axiomtek, an industrial PC field expert, speak to challenges and rewards of the sustainable smart city (Video 1). They discuss crucial roles of AI and edge computing to urban sustainability solutions, infrastructure needed to implement those solutions, and partnerships that can leverage this technology to make it work for the benefit of everyone who lives or works in a city.

Why is sustainability a goal of smart cities today?

Jody Cheng: First off, more and more people are becoming aware of climate change and its effects. Urban areas can contribute a lot to greenhouse gas emissions, and with the global population becoming increasingly urbanized, the pressure is on cities to tackle these environmental issues. According to a recent study done by the World Bank, 56% of the world population lives in cities, which is about 4.4 billion people. It’s expected that by 2050 seven out of 10 people will be city dwellers.

This has really pushed cities to be more proactive about reducing their carbon footprints, adopting practices to combat climate change and improve air quality that will boost the overall quality of life for their residents. At Axiomtek, we’re really excited to see this trend towards sustainability growing. It’s all about making sure that our environment stays healthy and thrives in the long run.

What are some of the technology solutions that can help sustainable smart cities?

Jody Cheng: Sustainable smart cities are cities that work smarter, not just harder, for both people and the planet. This is achieved by using smart grids and IoT data collection, along with innovations in buildings, transportation, and resource management.

Smart grids are the foundation for sustainable smart cities; an advanced electric grid employs monitoring tools to efficiently manage electricity use. This will also help integrate renewable resources, like solar, and reduce reliance on fossil fuels. For example, traffic lights that can adjust based on the real-time traffic can ease congestion and lower emissions.

But the possibilities are endless. Edge computing and AI make things even better by processing data right where it’s collected—think of a network of IoT sensors around the city tracking air quality, water usage, and traffic. This means quicker decisions and more efficient operations, helping city managers optimize resources and reduce waste and making smart cities even smarter.

Another big driver of sustainability is smart buildings and homes; LEED-certified buildings that follow strict sustainability standards are becoming more common. These buildings have energy-efficient systems—like automated lighting that adjusts based on the occupancy or HVAC systems that optimize heating and cooling.

Transportation is another key focus for cities aiming to reduce emissions. Cities are developing EV-charging infrastructure and promoting public transit systems with passenger tracking and traffic optimization. These innovations help cut emissions, ease congestion, and offer eco-friendly travel options for residents.

Managing resources like waste and water is also key to sustainability. Automated waste collection and energy-to-waste conversion help reduce landfill use and promote recycling. As for water—smart irrigation and advanced treatment processes optimize usage and cut down waste.

How do you see edge AI being implemented across smart cities?

Manny Hicaro: One example that Jody mentioned, and I can talk more about, is transportation. While EV charging and promoting public transportation support sustainability, edge AI can offer many other applications. Automated public transportation can optimize routes and manage passenger flow. In another instance, real-time sensors could optimize public parking. Also, improved autonomous vehicles in the future might even coordinate with each other and with traffic infrastructure to improve efficiency and safety. Edge AI can enhance traffic management, too; more cameras in place can allow for immediate accident detection and traffic rerouting.

Moreover, AI-powered surveillance can boost public safety by detecting unusual activity and predicting incidents like floods or fires. Infrastructure monitoring can detect anomalies that require prompt maintenance, including of road conditions and utility lines. This kind of monitoring can even extend to resources like water and air quality.

Talk more about the contributions of AI and edge computing to these solutions.

Jody Cheng: The advancement of AI and edge computing has elevated these sustainable solutions to a level that we haven’t seen before. AI has the ability to enable energy-efficiency improvements in all city environments, from buildings and factories to transportation systems and more.

“#Sustainable smart cities are cities that work smarter, not just harder, for both people and the planet. This is achieved by using smart grids and #IoT #data collection.” @Axiomtek via @insightdottech

Edge computing processes data locally onto devices such as IoT sensors, routers, or gateways. This proximity to the data source allows for quick decision-making and reduces the need for data transmission to centralized servers. It enhances responsiveness within the system while cutting down on the energy usage and resource consumption associated with transferring large amounts of data.

When combined, edge computing and AI bring together real-time data analysis and decision-making at the network’s edge, minimizing latency and bandwidth constraints. This decentralized approach enhances system responsiveness, reduces network congestion, and cuts costs. For city operations, this can optimize everything from traffic flow to energy-consumption patterns, reducing energy waste and increasing overall efficiency, ultimately improving quality of life while addressing environmental challenges.

What types of infrastructure investment are necessary to make this possible?

Manny Hicaro: Creating smart cities isn’t cheap. IoT sensors and cameras need to be integrated throughout the city in order to gather information on things like traffic, public safety, and environmental conditions. But it’s not just about the devices. Having a solid network infrastructure is crucial, too, and there needs to be a big investment in advanced hardware—powerful GPU-based edge computers that can handle a variety of processors. These machines are essential for real-time data processing and AI tasks.

A network of edge data centers strategically placed around the city can boost the efficiency and reliability of the edge computing while contributing to sustainability goals. These centers not only reduce latency and speed up the processing of real-time information—as Jody mentioned—they also provide redundancy, support the quick deployment of new applications, and improve disaster-recovery capabilities.

So implementing these technologies in existing urban infrastructures isn’t easy. It comes with a high initial cost and the challenge of ensuring compatibility with older legacy systems. This is where collaboration between the public and private sectors is crucial. These partnerships help align technology deployments and public policies, making sure that the solutions are sustainable and effective in the long run.

How has your partnership with Intel and its technology enabled the Axiomtek solutions?

Manny Hicaro: Our partnership with Intel has been key to the success of our smart city solutions; its dedication to advancing technologies aligns perfectly with our goals at Axiomtek. This partnership allows us to use cutting-edge technologies to develop robust and reliable solutions and has helped us stay ahead in AI and edge-computing advancements.

Intel processors provide the high-performance computing power needed to handle complex AI and data-processing tasks. And over time they’ve been fine-tuned to boost efficiency and performance, making sure that they meet the tough demands of city applications.

Another benefit of our partnership is scalability. There’s a wide range of products that Intel lets us customize to our solutions, ensuring that our systems can scale efficiently to meet the growing demands of urban areas. Whether it’s expanding the network of IoT sensors or adding more advanced AI capabilities, Intel technology supports the seamless scaling of our solutions.

Overall, the Intel technology and support have empowered us to develop advanced smart-city solutions and enhanced our ability to implement them effectively. This partnership keeps our systems at the cutting edge of technology and helps to provide reliable and scalable solutions for cities around the world.

Do you have any use cases that highlight the effectiveness of this technology?

Manny Hicaro: Absolutely. One of our standout projects involves an AI-enabled recycling bin that has pretty much transformed waste management in several urban areas so far. These bins use advanced AI algorithms to efficiently sort through the recyclable materials, making the recycling programs much more effective and significantly cutting down the frequency of waste collection.

Here’s how it works. People can easily deposit recyclable waste, like cans and plastic bottles, into these bins. The system then autonomously sorts through those materials, ensuring that they are correctly categorized for optimal resource recovery. This boosts sorting accuracy and overall recycling rates.

Sensors continuously monitor the fill levels in the bins in real time and send notifications when the bin is nearly full. Plus, the system maximizes physical storage space by automatically compressing the waste materials. These AI-powered recycling bins also take a proactive approach to maintenance: They can notify cleaners when they need attention. This streamlines operations and eliminates the need for constant manual monitoring. It results in improved accuracy, higher recycling rates, and greater operational efficiency.

Any final thoughts to inspire people on the road to creating sustainable smart cities?

Jody Cheng: Many people have traditionally viewed economic and environmental concerns as conflicting interests. And in the past, maintaining sustainable operations often demanded significant human resources for management and close oversight. However, we anticipate that the introduction of edge AI will significantly alter this dynamic for urban environments.

We see examples of this in recycling and resource management, where many of those operations that now incorporate edge AI operate more efficiently and autonomously, reducing the need for human intervention. So with edge AI advancement, these activities will become more cost-effective, more achievable, or more efficient.

At Axiomtek, we’re really excited to contribute to the sustainability journey by offering improved methods to fight the greenhouse effect, reduce carbon emissions in the long term, and maybe leave a cleaner environment and brighter future for the next generation.

Manny Hicaro: The possibilities are only limited by our imagination.

Related Content

To learn more about the latest sustainable smart city efforts, listen to Empowering Sustainable Smart Cities with Edge AI and check out our smart cities page. For the latest innovations from Axiomtek, follow them on Twitter/X at @Axiomtek and on LinkedIn.

This article was edited by Erin Noble, copy editor.