Edge Computing Elevating Telecom’s Realm Beyond IoT

Edge computing heralds a new era in telecommunications, propelling the industry toward unprecedented levels of efficiency, reliability, and innovation.

Aanchal Ghatak
New Update

Edge computing

Edge computing heralds a new era in telecommunications, propelling the industry toward unprecedented levels of efficiency, reliability, and innovation. And, the rapid integration of Internet of Things (IoT) devices in telecom industry is reshaping industries. Yet, the surge in IoT adoption brings forth a pressing challenge: the imperative for low-latency processing. Enter edge computing, a pivotal solution offering the infrastructure necessary to power real-time applications and elevate the performance of telecom networks.


At its core, edge computing entails processing data closer to its origin — at the network’s edge — rather than relying on centralized cloud systems. This strategic shift significantly reduces data travel distances, slashing latency and bolstering application responsiveness.

Implementing edge computing in telecom networks offers a multitude of advantages over traditional centralized architectures.

Addressing Latency Challenges


Latency, the delay before a transfer of data begins following an instruction for its transfer, is a critical factor in the performance of IoT applications. Traditional cloud computing models, which process data in centralized servers often located far from the data source, can introduce significant delays. Edge computing addresses these challenges by enabling:

1.           Proximity to Data Sources: By processing data near the IoT devices that generate it, edge computing reduces the distance data must travel, cutting down on transmission delays.

2.           Reduced Network Congestion: Offloading data processing to the edge reduces the burden on the central network, decreasing traffic and improving overall network performance.


3.           Faster Data Processing: Localized data processing allows for immediate analysis and response, which is crucial for applications requiring real-time decision-making.

Piyush Somani, the Founder, CMD & CEO of ESDS Software Solution Limited and President of CCICI, emphasizes the transformative potential of edge computing: “Edge computing reduces data travel distances and substantially mitigates latency by relocating data processing closer to the source. This directly addresses the latency challenges encountered by IoT devices in telecommunications networks.”

Somani underscores the explosive growth of the global edge computing market, projected to reach $274 billion USD by 2025. This meteoric rise mirrors the escalating utilization of edge computing for network optimization, particularly in latency reduction efforts. Telecommunication giants stand poised to deliver unparalleled low-latency experiences by leveraging local data processing at the edge. Notably, applications like online gaming, real-time video analysis, and autonomous vehicles stand to benefit immensely. The resultant swift response times not only elevate user satisfaction but also unlock novel use cases once deemed impractical.



Telecom providers must ensure edge deployments comply with applicable data privacy laws. Strong data encryption is advised for every device in the network, from the edge to the core. - Piyush Somani, Chairman & MD,  ESDS Software Solutions Ltd

Key Benefits for Telecom Networks


Implementing edge computing in telecom networks offers a multitude of advantages over traditional centralized architectures. Somani emphasizes several significant benefits. “Edge computing, first and foremost, substantially reduces latency,” he asserts. “Critical applications can respond immediately when real-time processing takes place locally.”

He highlights that “processing data locally increases bandwidth efficiency and frees up core network resources for other uses.” He underscores the importance of reliability, noting that “edge architectures are more reliable since edge devices can continue to operate independently for a while during network disruptions, ensuring uninterrupted service.”

Somani also emphasizes the scalability of edge computing, stating, “We can quickly adjust the processing capacity at various points in the network to suit particular requirements.” Additionally, he points out that “processing sensitive data locally and according to data regulations improves privacy and data security.”


He further highlights the agility edge computing offers to operators. “Due to edge computing’s scalability and flexibility, telecom operators can swiftly roll out new services and effectively handle fluctuating loads,” he adds.

While acknowledging initial setup costs, Somani notes, “there are long-term savings in central processing and data transfer, which makes it cost-effective.” He stresses that “it facilitates the development of novel, latency-sensitive services that improve user engagement and innovation.”

Real-Time Data Processing for IoT Applications


Edge computing is particularly beneficial for IoT applications that demand real-time data processing. In the telecom industry, these applications include:

Smart Cities: Traffic management systems and public safety applications rely on real-time data to function effectively.

Healthcare: Remote monitoring and telemedicine services require immediate data analysis to provide timely medical interventions.

Industrial Automation: Manufacturing processes and supply chain management benefit from instantaneous data processing for efficient operation.

Piyush Somani notes, “The full potential of IoT data in telecoms can be tapped via real-time processing. The global deployment of 15 billion edge devices highlights the demand for effective data processing solutions, such as edge computing. For instance, network equipment sensor data can be used to anticipate and prevent failures before they occur or dynamically modify network resources in response to actual traffic patterns to maximize efficiency.”

Edge computing opens up new possibilities for user experiences, such as latency-sensitive services like connected automobile applications or augmented reality. It provides a local source for data processing and storage requirements for the Internet of Things. Machine learning and analytics algorithms facilitate timely decision-making, local data processing, and data aggregation.

The edge computing industry is projected to expand at a compound annual growth rate (CAGR) of 34.1%, from USD 3.6 billion in 2020 to USD 15.7 billion by 2025. The demand for real-time data processing in various IoT applications fuels this increase.

Beyond IoT: Broader Applications of

Edge Computing in Telecom

Edge computing extends its benefits beyond IoT applications, offering valuable solutions for various other use cases in telecommunications:

Content Delivery Networks (CDNs): By caching content closer to users, edge computing reduces latency and improves user experience. Piyush Somani explains, “Think about CDNs or content delivery networks. Edge caching transmits content closer to users. It speeds up upload times and enhances streaming quality.”

Augmented and Virtual Reality (AR/VR): Low-latency processing is crucial for delivering seamless and immersive AR/VR experiences.

5G Networks: Edge computing supports the low-latency requirements of 5G applications, enhancing the capabilities of next-generation mobile networks.

Enhancing Network Efficiency and Reliability

Edge computing significantly enhances the efficiency and reliability of telecom networks. By processing data locally, edge computing conserves bandwidth and reduces operational costs. Somani highlights, “Edge computing decreases the pressure on core networks by processing data locally, which has several benefits. This increases network efficiency and reduces traffic congestion, enabling faster data flow and reaction times.” Furthermore, the distributed nature of edge computing infrastructure makes networks more resilient, capable of maintaining high performance even if individual nodes experience failures.

Security Implications: Data Privacy

and Compliance

Deploying edge computing in telecom networks also offers substantial security benefits:

Data Privacy: Processing data at the edge reduces the need for extensive data transmission, minimizing the risk of exposure to cyber threats. “By minimizing the need to send sensitive data over the network, edge data processing improves data privacy and lowers vulnerability to potential cyber risks,” Somani notes.

Compliance: Localized data processing helps telecom operators adhere to data sovereignty laws and regulations, ensuring that sensitive information is managed according to local standards. “Telecom providers must ensure edge deployments comply with applicable data privacy laws. Strong data encryption is advised for every device in the network, from the edge to the core,” adds Somani.

Leveraging Edge Computing for New Services

Telecom operators are increasingly leveraging edge computing to optimize network resources and deliver innovative services. By deploying edge nodes, operators can offer:

Local Content Caching: Enhances user experience by reducing access times for frequently requested content.

Real-Time Analytics: Provides valuable insights for better decision-making and operational efficiency. He mentions, “Edge processing facilitates almost fast data analysis that provides insightful information for network optimization and service enhancement.”

Advanced Security Measures: Improves threat detection and response times, enhancing overall network security. “Because edge computing allows for real-time threat detection and mitigation at the network edge, it guarantees the reinforcement of security postures,” explains Somani.

Telecom operators are poised to innovate their services with edge computing, projected to reach $445 billion by 2030. “Operators who use this technology can set the standard for new developments in real-time data processing, network efficiency, and focused customer innovation,” Somani emphasizes. At ESDS, we believe edge computing will greatly impact how communications evolve in the future.