In India, the integration of digital technologies is progressing rapidly. The uptake of 5G networks, government-backed 6G Alliance and edge-native platforms by domestic start-ups and telcos is laying the groundwork for a distributed digital infrastructure. Simultaneously, technologies like internet of things (IoT), containerised data centres, digital twins, software defined network and blockchain are being adapted for edge deployments, creating new products and business models. Further, the fusion of edge computing with these emerging technologies is reshaping India’s digital landscape, all while paving the way for next-generation infrastructure.
Integrating edge with 5G and 6G networks
The blend of 5G and edge computing promises numerous opportunities. While 5G can increase speeds by up to 10 times over 4G, mobile edge computing can lower latency. Therefore, when 5G and edge are combined, storing and processing time-sensitive data on high-bandwidth devices becomes easier. A 5G-enabled computing platform that provides integrated cloud and edge capabilities (including enterprise edge, network edge and satellite edge) can help deliver high performance and ultra-low latency solutions that accelerate business transformation. With this, organisations can focus on developing distributed applications utilising cloud technology and various edge computing resources to deliver business transformation use cases. Instead of “moving everything to the cloud”, enterprises can opt for a mix of cloud and edge environments, integrated on a 5G network.
According to industry projections, India’s 5G edge computing segment is expected to grow rapidly, with a projected compound annual growth rate (CAGR) of nearly 64 per cent between 2025 and 2030. Reacting to this trend, telcos are making significant infrastructure investments. Bharti Airtel has already established more than 120 edge data centres, collectively offering over 120 petabytes of storage capacity. Meanwhile, Reliance Jio has deployed cloud-native 5G core networks across more than 50 locations, laying the foundation for ultra-low latency applications and next-generation digital services.
Similarly, as discussions around 6G intensify, edge computing will be foundational to the architecture of next-generation connectivity. Unlike 5G, where mobile edge computing is an optional add-on, 6G will have edge computing natively embedded across all network layers. This integration is essential to meet the demands of ultra-reliable low-latency communication, which requires extremely low delays and high throughput. 6G networks are expected to generate significantly more data than their predecessors, driving a need for more efficient processing through enhanced coordination between edge and core infrastructure. Edge computing will serve as a critical intermediary, enabling decentralised, low-latency processing close to the source of data while complementing cloud computing for more complex and high-latency tasks.
IoT and edge
IoT technology is growing rapidly, with billions of connected devices generating massive volumes of data. Managing these complex ecosystems is becoming increasingly challenging. Traditional centralised approaches to IoT system management are struggling to keep pace with real-time responsiveness and security. However, the rise of edge computing is facilitating a new phase of intelligent IoT system management.
Edge computing is enhancing IoT system management by relocating computation and data processing from centralised cloud servers to the network edge, closer to where data is generated. This shift enables faster, more efficient and secure operation across connected systems. By processing data locally, edge computing significantly reduces latency, which is essential for real-time applications like autonomous vehicles and industrial automation. It also optimises bandwidth by transmitting only relevant or summarised data to the cloud, easing network congestion and lowering costs. Moreover, processing sensitive data at the edge enhances privacy and security by minimising exposure during transmission.
Containerised data centres
In an era marked by artificial intelligence (AI) boom, proliferation of IoT and increased cloud adoption, there has been a growing interest in modular and containerised data centres in India’s smaller towns and semi-urban areas. According to Grandview Research, the containerised data centre market in India is expected to reach a revenue of $3.16 billion by 2030, depicting a CAGR of 27.7 per expected between 2024 and 2030.
Containerised data centres are compact, portable units equipped with power backup, cooling and other essentials, housed within a cabin-like structure. Designed for quick deployment, they avoid the high costs and long timelines of traditional data centres, which can take up to two years to build. With modular and scalable architecture, they can be installed within weeks, making them ideal for fast-changing IT needs across cloud services, telecom, edge computing and content delivery.
Recently, NES Data Private Limited announced the deployment of edge and containerised data centres in Tier II cities and underserved regions across India, with operations expected to commence in August 2025. These compact facilities are engineered for low-latency, high-efficiency performance and are tailored for rapid deployment. Equipped with compute and storage infrastructure, cooling systems, battery energy storage, automated operations and hybrid cloud integration, these centres aim to address localised digital infrastructure gaps. In parallel, CtrlS Datacenters Limited is launching its first such facility in Bhubaneswar as a pilot project before scaling to other regions. One of the key drivers for these decentralised deployments is cybersecurity. Traditionally, security systems and applications are centralised at corporate headquarters, causing delays in response and limited effectiveness at branch levels. With edge locations, CtrlS is bringing cybersecurity functions closer to the point of threat detection, allowing for faster responses, real-time monitoring and the implementation of zero-trust frameworks that are more deeply embedded within regional networks.
Digital twins
Digital twins have become an important tool across multiple sectors in India, with the telecom industry being a key beneficiary of this global shift. As per industry estimates, the domestic digital twin market is expected to grow from $2.30 billion in 2025 to $45.51 billion by 2034, recording a CAGR of 39.30 per cent from 2025 to 2034.
One of the key benefits of edge computing in digital twin applications is improved latency and responsiveness. By bringing computing power closer to where data is generated, it reduces transmission and processing delays, which is crucial for real-time analysis and decision-making. Another major advantage is enhanced security and privacy. Processing data locally at the edge minimises the risk of sensitive information being exposed during transmission, particularly important in sectors like healthcare and finance. Edge computing also brings greater flexibility and scalability, allowing organisations to expand or adjust resources without heavy investment in centralised systems.
Generative adversarial networks (GANs)
GANs have gained significant attention due to their ability to generate highly realistic images, videos and other forms of data. The generator produces synthetic data, while the discriminator evaluates the authenticity of this data. This iterative process allows GANs to progressively improve, resulting in high quality synthetic data. GANs have found applications across a wide range of industries, including entertainment, healthcare, autonomous systems and augmented reality. However, despite their impressive capabilities, GANs face challenges that hinder their widespread deployment, particularly in real-time applications.
Integrating edge computing with cloud platforms is a powerful approach for enhancing the performance of GAN-based image synthesis, especially in real-time environments. This hybrid architecture combines the low-latency benefits of edge computing with the cloud’s computing strength, offering an effective way to manage the heavy computational demands typically associated with GANs. Intensive tasks such as model training or complex image generation are handled in the cloud, while edge devices manage pre-processing and post-processing locally. This setup ensures faster response times, optimised resource use and better scalability across different deployment scenarios. It also enables the intelligent distribution of workloads across the network, improving overall system efficiency. By bridging the gap between edge and cloud, this model makes it possible to deploy high-quality, real-time GAN applications even in environments with limited on-site computing power.
Blockchain
Blockchain technology is essentially a distributed ledger system that records transactions across a network of computers, ensuring that the data remains secure and tamper-proof. Each transaction is stored in a “block”, which is then cryptographically linked to the previous one, forming a continuous chain of blocks, hence the term “blockchain”.
The combination of blockchain and edge computing creates a strong framework for secure and decentralised data processing. Both rely on distributed networks and infrastructure. While blockchain ensures data integrity through its tamper-proof ledger, edge computing processes data closer to its source, reducing latency and dependence on central servers. The use of graphics processing units in edge devices further boosts this setup by speeding up blockchain transaction validation. Together, they enable faster, real-time data verification, improve system efficiency and support secure and high-speed operations across various environments.
In sum
As India accelerates its transition to a distributed and data-driven digital infrastructure, the convergence of edge computing with these next-generation technologies presents immense potential. However, realising this vision will require addressing several challenges. These include high upfront infrastructure costs, lack of standardisation across edge deployments, inconsistent rules and a significant skills gap in edge-native system design and AI-driven optimisation. Moreover, managing data security, working together across systems and ensuring energy efficiency at scale remains a pressing concern. As demand for real-time and localised processing continues to rise across industries, the role of edge computing will only become more central. Continued innovations, future investments and collaborations among industry stakeholders will be key to unlocking its full value.
