Building Smart Cities: Successful Technology Integration

SMART CITIES AND TECHNOLOGY INTEGRATION

Back in 1999 British technology pioneer and serial entrepreneur Kevin Ashton coined the term “Internet of Things” to describe a network of wired and wireless sensors and actuators to capture and exchange data without relying directly on human intervention. That same year, Ashton joined the Media Lab at MIT, where he founded the Auto-ID Center, initially to create a smart package tracking system (RFID) for sponsor Proctor & Gamble. At the Media Lab he was heavily influenced by MIT physicist Neil Gershenfeld and his book When Things Start to Think.1

Today the Internet of Things (IoT) is dramatically changing people’s lives and shaping the design of communities that serve them. The global market for IoT in Smart Cities is projected to reach \$967 billion by 2032, compared with \$179 billion in 2023, with a projected compound annual growth rate (CAGR) of 20.6 percent between 2024 and 2032, attracting investors from around the world.2

Because IoT networks require ongoing configuration and optimization in order to manage wildly disparate devices and process vast amounts of data, IoT has been increasingly coupled with Generative Artificial Intelligence (Gen AI), which includes all AI systems with the ability to create new content, such as text, images, videos, or other forms of data.

Gen AI techniques can automate processes to make IoT networks more intelligent and self-managing. The combination of the two technologies is transforming how people live and work. McKinsey Global Institute predicts that Generative AI as an emerging technology will add \$2.6 to \$4.4 trillion in annual value to our global economy.3

IoT and Gen AI are now considered a crucial part of the Fourth Industrial Revolution, or Industry 4.0, that is evolving at an accelerating pace and helping cities become Smart Cities by optimizing infrastructure design, from energy management to public education to mass transit to safeguarding public safety and security. In general, a Smart City is an urban area that uses digital technology to improve quality of life and efficiency through data-driven services and citizen engagement.

Along with the rapid adoption of emerging technologies, from wearables to smart buildings, particularly in growing urban populations, there is a corresponding market need to grow responsibly, ensuring innovative solutions harmonize quality of life with sustainability goals.

With homes representing one of the largest assets people acquire, can Smart Cities use technology in ways that significantly reduce a home’s lifetime ownership costs, through less energy consumption, less vulnerability to earthquakes, wildfires and hurricanes, and correspondingly lower insurance rates?

Will people migrate to Smart Cities in a quest for improved quality of life, a healthier and safer environment, better access to a good education for their children, or to gain a sense of community?

Correspondingly, can Smart Cities be designed to attract new sustainable jobs that pay a living wage? According to the Bureau of Labor Statistics, small businesses (firms with 249 or fewer employees) contributed 55 percent of total net job creation during the period 2013 to 2023.4 Designing a Smart City to attract small businesses as well as large businesses likely will require a deeper understanding of how business needs can be integrated with a broader socio-technical system to jointly optimize people, technology and governance.

THE IMPORTANCE OF COMPLEMENTARITY TO MEASURE SUCCESS

The concept of Complementarity was used initially in the 1920’s to describe the wave-particle duality of quantum mechanics. In the 1980’s economist Masahiko Aoki used the term to describe the economic impact of institutional change and how agencies of change complete each other to form a whole greater than the sum of its parts.5

Today, complementarity is quickly becoming a critical success factor for planners and architects of Smart Cities as they create plans to maximize social and economic impact, integrate advanced technologies such as IoT devices, decentralized edge networks, and data centers, among myriad components of a complex system of change.

In this regard, as each resident in a city learns about change efforts such as broadband adoption, municipal Smart City planning and overall digital equity, the community as a whole learns to appreciate how these technologies complement each other in a sustainable way to improve the quality of life for all.

In this environment, optimizing energy usage and reducing a community’s carbon footprint, the interconnected relationships allow cities to adapt quickly to changing demands and human-centered design. Just as the human body reacts dynamically to its surroundings, Smart Cities could begin to respond intelligently to their own needs.

The latest direction in AI research is in Neuromorphic Computing, complemented by quantum computing, in an attempt to mimic the human brain’s neural circuits. Autonomous vehicles represent a rudimentary example, where large quantities of data are processed simultaneously, and decisions are made according to circumstances, much like a human driver.

EDGE NETWORKS: THE LOCAL BRAIN

In a Smart City, an edge network is a distributed computing system that functions as a "local brain," processing data close to the source while reducing latency. As part of IoT integration, edge networks take care of less data-intensive chores at the “edge” of the network, such as monitoring street sensors and devices, enabling quick responses and automated actions.

Many tasks can be distributed across multiple smaller computing units, or edge nodes, spread throughout various locations in the network. By breaking down complex tasks into appropriate sub-tasks, edge networks enable faster analysis and response times. For example, an edge network can be designed to optimize public transportation, by creating predictive maintenance schedules for buses and modifying schedules based on passenger needs and peak loads.

In this setting, foremost are needs to accommodate passenger patterns, safeguard security, enhance durability and minimize lifetime costs of buses, making the overall system more resilient and resistant to failure. This setup allows localized decisions to be made quickly without needing to send every request to a centralized system.

DATA CENTER: THE CENTRAL HUB

As a complement to edge networks, data centers handle more resource-intensive, centralized operations such as long-term data storage, large-scale machine learning model training, and high-performance computing. They also act as backup or aggregation points for data processed at the edge, enabling hosting, comprehensive analysis, and strategic planning.

In the case of disaster recovery, such as following a hurricane, data centers come into play where large amounts of data have to be archived and processed, and AI tools are used to understand the effectiveness of mitigation strategies. Designed properly, a hybrid or integrated model allows for the division of work between edge networks and data centers based primarily on how much data has to be processed and how quickly decisions have to be made.

INTEGRATED APPROACH: EMPOWERING CITIES THROUGH INFRASTRUCTURE AND AI

The hybrid model described above is particularly well-suited for Smart Cities, where real-time data processing and localized decision-making are critical for addressing urban challenges. By leveraging both edge networks and data centers, a city can create safer, responsive and sustainable infrastructure – an integrated design that can respond appropriately and quickly to changing demands.

AI MODELS DRIVING ECONOMIC GROWTH FOR A SMART CITY

The hybrid system of decentralized edge networks and centralized data centers enables deployment of AI models to undergird economic development and enhance public services. Trained on large datasets, a hybrid system can include incentives to attract small business, forecast population growth, identify optimal business locations, and guide investments toward underserved neighborhoods, promoting equitable economic growth.

AI tools can be employed to anticipate labor market trends and equip residents with skills needed to succeed in evolving job markets by recommending workforce training programs aligned with as well as spawning emerging industries.

As data flows continuously from new developments and public services, AI models can refine their predictions, sustaining a dynamic feedback loop to support long-term economic expansion and improved quality of life.

In addition, Smart City technologies can significantly improve environmental outcomes by utilizing renewable energy sources to reduce energy consumption, powering smart waste collection systems to minimize waste-related emissions, and smart water management systems decreasing water consumption, among other smart solutions.7

ENABLING AUTONOMY: ORCHESTRATION LAYERS AND MIDDLEWARE

To manage data flow and tasks between edge networks and data centers, a combination of orchestration layers and middleware is employed. Technologies such as containers (Docker, Kubernetes), serverless architectures, and edge orchestrators enable dynamic task allocation between edge nodes and centralized data centers based on resource needs and the use case.

For instance, if a task on an edge node requires more powerful resources, it can be handled centrally by the data center. This seamless handoff enables edge networks to operate autonomously for real-time tasks, while the data center takes care of heavy computation or storage in the background. This creates a decentralized yet connected system, where both components are leveraged for optimal efficiency.

LATENCY AND SCALABILITY

Edge networks process data near the source, reducing latency for tasks such as traffic management or autonomous vehicles. This enables faster decisions and allows data centers to focus on complex, long-term processes like analytics and machine learning. In the end, the hybrid model enables horizontal scalability through additional edge nodes for demand spikes, while centralized data centers provide vertical scalability for resource-intensive tasks, ensuring seamless workload adaptation.

PROBLEM MITIGATION BY DESIGN

Distributing tasks across edge networks ensures service continuity during failure of an individual node – especially important for areas prone to natural disasters, such as earthquakes, wildfires, hurricanes or tornados. Data centers typically offer backup and recovery, ensuring uninterrupted critical services and preserving data integrity.

GRID AND BANDWIDTH OPTIMIZATION TO IMPROVE COST EFFICIENCY

Local processing at the edge reduces the need for centralized servers, minimizing data transmission and energy consumption. By distributing computing tasks across multiple locations, a Smart City can reduce carbon footprint and alleviate grid strain. This approach enables more efficient use of resources, promoting a sustainable digital infrastructure.

Distributed computing reduces costs for lighter tasks, while data centers handle resource-heavy operations. This hybrid model optimizes resource allocation and balances costs.

For investors, a hybrid model is likely more attractive because it can ensure a higher valuation and annual financial returns.

For hybrid system developers and for communities that want to attract them, more job opportunities will tend to attract a steady source of prospective workers interested in securing high-tech jobs.

ENHANCED SECURITY AND USER PROTECTION

Decentralized systems encrypt and distribute data, reducing security risks, while centralized data centers ensure long-term storage, compliance, and governance, in compliance with regulations such as General Data Protection Regulation (GDPR), a European Union law setting strict standards for how companies handle personal data of EU residents.

Whether or not the U.S. adopts the GDPR or establishes a similar version is yet to be seen, but as IoT continues to grow worldwide, user demand for protection can be expected to grow as well.

SCALABILITY THROUGH INTEGRATION: A USE CASE FOR SMART ENERGY MANAGEMENT

Our Brooklyn Microgrid Use Case focusing on the energy infrastructure of a Smart City, which design began in 2016, demonstrates how integrating data centers and decentralized edge networks supports smart energy management systems.8 Distributed edge nodes (for example, in homes, renewable energy sources, and EV charging stations) manage energy consumption and production in real time.

In the Brooklyn Microgrid, edge nodes optimize local energy distribution and enable peer-to-peer energy trading using blockchain technology, and allowing residents to exchange excess renewable energy, thus creating an eco-system wherein residents and business owners contribute to the community’s resilience.

BATTERY STORAGE: POWERING THE COMMUNITY FUTURE

To ensure the functionality and stability of the community-driven system, advanced energy storage systems are critical to energy resource management. These batteries store excess energy generated by local renewable sources, such as microgrids and wind turbines, during periods of low demand or at night.

Stored energy ensures continuous operation of edge nodes, and excess energy can be returned to the grid for the community’s use during peak hours or outages, providing a stable and sustainable source of power.

EDGE NODES: THE COMMUNITY'S EYES AND EARS

Edge nodes gather data from smart homes and infrastructure, continuously monitoring energy usage, generation, and storage. For example, smart home devices track appliance energy consumption, dynamically adjusting based on grid demand or peak hours. These devices autonomously optimize energy use.

DATA CENTERS: THE COMMUNITY'S KNOWLEDGE HUB

Data centers act as the centralized knowledge hub, receiving and analyzing data from edge nodes with the ability to monitor city-wide energy trends, manage grid performance, and predict demand surges. Machine learning algorithms within the data centers forecast energy needs based on factors like weather patterns. Moreover, smart grid integrations allow data centers to manage peak loads and contribute to demand-response programs, which help stabilize the grid during high-demand periods.

At a community level, as witnessed during the global COVID-19 pandemic, when students could not attend classes in person, access to broadband became a lifesaver to ensure our young could continue to advance their education through remote classes. An important lesson for all of us is that we need smart students to contribute to Smart Cities for their generation and beyond.

POWERING PROSPERITY BY PARTNERING WITH THE COMMUNITY

Decentralized edge networks, when integrated with distributed energy systems, enable all users young and old to actively participate in real-time energy management.

Through solar cooperatives, local microgrids, and community-supported energy systems, edge nodes process data closer to the source, reducing the strain on centralized grids.

These systems empower residents and local businesses to take ownership and to become both producers and consumers of energy, facilitating peer-to-peer trading of surplus power to optimize local energy distribution.

Involvement in such initiatives not only promotes sustainability but also offers enticing economic benefits, including lower energy costs and greater energy autonomy, lessening a community’s vulnerability in the event of natural or man-made disasters.9

CONCLUSION

The complementarity of IoT, Gen AI, decentralized edge networks and centralized data centers serves as the key to building resilient Smart Cities. However, success depends on effective management of constraints such as grid reliability and community engagement.

To achieve true transformation, developers and communities must work together to create a collaborative ecosystem where technology, public input, and sustainability intersect. By embracing a harmonized approach that balances technology growth with social and environmental impact, along with involvement of engaged residents, architects and planners can create sustainable Smart Cities that benefit all constituents.

END NOTES:

1. Gershenfeld, Neil. When Things Start to Think. New York: Henry Holt, 1999

2. Zion Market Research. “IoT in Smart Cities Market Trend, Share Growth, Size, Analysis and Forecast 2030.” September 2024

3. McKinsey & Company. “The economic potential of generative AI.” June 14, 2023

4. U.S. Bureau of Labor Statistics. Business Employment Dynamics. May 2024

5. Aoki, Masahiko, Toward a Comparative Institutional Analysis, MIT Press, 2001

6. National Institute of Standards and Technology (NIST). Malcolm Baldrige National Quality Award. 2023

7. Nature Research Customer Media, Tohoku University. “Intelligent Energy Grids for Smart Cities.” 2022

8. Catharina, Noa. “Brooklyn Microgrid Use Case: A Community-Focused Approach to Smart Energy Development.” 2024. Available upon request.

9. Kojonaari, AR., Palm, J. “Distributed Energy Systems and Energy Communities Under Negotiation.” Technology and Economics of Smart Grids and Sustainable Energy, Volume 6, Article 17, September 15, 2021

ABOUT THE AUTHORS:

Noa Catharina is a technologist and author committed to building Smart Cities and sustainable infrastructure. Born and raised in the Netherlands, Noa combines technical expertise with user-centered insights to build sustainable data centers amidst the changing AI, IoT and energy landscape, driving meaningful and enduring social and economic impact and promoting a better understanding of complementarity. ▶ Contact: noa@scalarconsultancy.com

Former U.S. Marine Jim Wong earned his graduate degree at MIT, studying Organization Development and Systems Dynamics, after which he was employed as a turnaround manager at General Motors and collaterally assigned as GM Executive on Loan to Ohio Governor James Rhodes to support community development. Subsequently Jim was recruited by PepsiCo in training and development, later becoming a Pizza Hut franchisee. He has been an entrepreneur since 1988 and supports Vallejo’s Smart City initiatives. Currently chair of nonprofit National Veterans Transition Services Inc., Jim continues to support his fellow veterans in their successful reintegration. ▶ Contact: jimwong88@mac.com