With the introduction of our platform for Augmented Agentic Intelligence (AAI) into a city, the future resembles a cinematic narrative where individuals assume the role of protagonists. NanoAi, also known as the Internet of Me (IoM), will profoundly influence the experiences that cities offer, buildings provide to residents, civil engineers manage infrastructure, and all while presenting a Cognitive Artificial Intelligence (CAI) environment. The fundamental question arises: can we comprehend Artificial Intelligence (AI) in the manner that AI comprehends us? This concept serves as the core paradigm for the research and products we develop, and it is this IoM-driven future that enhances the impact of autonomous systems. Let us delve deeper into the concept of the city of the future in relation to the city of today.

Leveraging the AAI platform is a key digital infrastructure technology, the overall market impact of IoT in the cities setting could reach $1 trillion to $1.7 trillion by 2030 according to our research.

The largest use case in cities is decentralized and adaptive traffic control, with an estimated economic value of $100 billion to $390 billion. This value comes from reduced travel time and CO2 emissions. Autonomous vehicles, broadly defined to include L2 autonomy and above, could capture $240 billion to $300 billion in economic value, driven by reduced accidents and vehicular pollution. Together, these account for 35 to 41% of total economic value from the IoT in the cities setting in 2030.

Tailwinds

The debate on the economic impact of the IoT in cities is over. The focus is now on delivering its value. For instance, a trial in Singapore showed a 92% reduction in overcrowded buses by optimizing public transport using open data, fare card data, sensors in over 5,000 vehicles, and real-time bus tracking.

The widespread build-out of fixed fiber and 4G wireless infrastructure over the past five years has connected almost every city in the world to high-speed internet, minimizing connectivity barriers to 5G. By 2026, 5G is expected to cover about 60% of the global population, and by 2030, up to 90% of the global population will have some level of 5G coverage, deploying and realizing benefits from IoT use cases.

Headwinds

Cybersecurity: Governments worldwide are seeking comprehensive security measures to integrate safeguards across all digital layers due to rising concerns about cybersecurity and the transition of cities from simple use cases to those requiring intricate and sensitive data handling.

Privacy: Privacy concerns vary across regions as cities explore complex use cases involving sensitive data like facial recognition. The absence of clear guidelines for data storage and usage leaves cities navigating an uncharted territory.

Scope: IoT projects typically span multiple years, may not yield immediate benefits, and face protracted public procurement cycles, delaying or preventing transformative multiyear projects.

Interoperability: Single-use-case solutions for IoT applications in urban settings lack standardized architecture and sensors, hindering interoperability for operators. AAI is designed to enable solutions that are not designed to work with each-other.

Autonomous vehicles, poised to revolutionize personal transportation, could generate $240 billion to $300 billion in potential economic value in urban settings by 2030. Even at lower levels of autonomy, they could reduce accident frequency and yield substantial health benefits. At higher levels, they could facilitate commuters.

Currently, Level 4 (high) and Level 5 (full) autonomous vehicles are not in commercial use. However, if technological advancements and regulatory approvals continue, adoption in cities could reach 1% of cars by 2030. Improvements in technology and cost reductions will drive this increase.

Decentralized cognitive Ai traffic management systems enhance traffic flow and throughput with the ability to adapt to local conditions, this is a Nano Ai use case that is built to run on small memory and processor footprints. Cognitive Ai traffic optimization shortens commutes and improves emergency response times by 50-85%. Emerging markets have a lower adoption rate than developed markets and China is expected to catch up, reaching 34-71% in the high scenario. Traffic optimization requires diverse technologies. A distributed network of sensors, including motion, weight, and video, is the initial step. Enhanced connectivity, particularly 5G, is needed for vehicle-to-vehicle and vehicle-to-grid communications. Advanced analytics overlay generates first-order, second-order, and third-order insights, including root causes of congestion.

Cognitive Ai with adaptive traffic control holds the potential to generate substantial savings for households and the urban economy. It reduces traffic congestion, saving commuters time and fuel. Optimizing traffic could reduce commuting time by 15 to 20%, decreasing wasted time, fuel consumption, emissions, and accidents. In total, decentralized and adaptive traffic control could create $100 billion to $390 billion in annual potential economic value by 2030.