Over the past decade, cities have invested heavily in connected infrastructure. As of 2025, the Smart Cities Mission has completed over 90% of its 8,067 projects, with nearly all 100 participating cities operating Integrated Command and Control Centres (ICCCs) that aggregate data from thousands of sensors, cameras, and connected assets.
Yet, as deployments scale, a fundamental shift is underway. The question is no longer how many devices are connected; it is how intelligently they operate. In a country defined by scale, diversity, and real-time complexity, the success of smart cities will depend not only on connectivity but also on edge intelligence.
From Connection to Intelligence
The first wave of smart city investment focused on instrumentation, deploying sensors across traffic systems, utilities, public safety networks, and environmental monitors. This was a necessary foundation, but it created a new challenge: an overwhelming volume of data flowing to centralised systems.
In many cases, latency, bandwidth constraints, and dependence on cloud processing can limit how quickly cities respond. A traffic controller that cannot adjust signal cycles until a cloud round trip is completed, or a flood sensor that records rising water levels without triggering a local alert, highlights the gap between being connected and being intelligent.
Why Edge Intelligence Matters
This is where edge intelligence becomes critical. Edge intelligence enables devices such as smart meters, streetlights, and healthcare monitors to process data locally and make faster, autonomous decisions.
Instead of sending every data point to the cloud, edge-enabled systems can filter, analyse, and act locally, reducing latency and improving reliability. A flood sensor with edge inference, for example, can compare readings against historical baselines, assess severity, and trigger alerts before data reaches the command centre. Similarly, an AI-enabled traffic controller can measure queue depth in real time and autonomously adjust signal cycles.
India’s scale makes this shift even more important. With millions of connected endpoints across urban and semi-urban environments, the traditional cloud-centric model must evolve. Connectivity remains foundational, but the real value lies in combining resilient wireless networks with on-device intelligence that continues functioning even under constrained or intermittent connectivity.
This is particularly important for applications such as smart grids, water management, and distributed healthcare systems, where reliability is critical.
The Importance of Interoperability
Edge intelligence cannot operate in isolation. India’s smart city ecosystem spans multiple vendors, technologies, and use cases, making interoperability essential. Without common standards and seamless communication between devices, fragmentation can limit scalability.
Open, multiprotocol connectivity frameworks built on unified software-defined platforms allow cities to integrate diverse device ecosystems, add capabilities without replacing existing hardware, and avoid vendor lock-in that limits long-term flexibility.
Building Smarter Infrastructure
As India continues investing in urban transformation, the focus must extend beyond infrastructure to intelligence itself. The foundation of edge intelligence lies in the chips, modules, and system-on-chip designs embedded in IoT devices that determine whether data can be processed locally or must be sent to the network for processing.
India’s edge computing market, valued at $656 million in 2025, is projected to grow at a CAGR of approximately 15.5% to reach $2.4 billion by 2034. This growth reflects rising investment in distributed computing infrastructure, but its long-term impact will depend on whether edge capabilities are built into devices at the design stage.
What Will Define the Winning Cities
The cities that succeed will not necessarily be those with the most sensors or the largest command centers. They will be the ones with infrastructure that can act, not just observe, where traffic systems respond in real time, flood sensors trigger alerts before operators check dashboards, and energy grids rebalance locally before faults cascade.
That level of responsiveness requires intelligence at the edge working alongside cloud-based analytics, with edge systems handling time-critical decisions while the cloud provides broader operational insight.
The next phase is making that instrumentation intelligent. And the intersection of robust IoT hardware and edge intelligence is precisely where that next phase begins.
