Overview

Edge computing integrates computational capabilities into everyday objects in such a seamless way that users often interact with them without realizing it. The term Internet of Things (IoT) was first coined in 1999 by Kevin Ashton during a presentation at Procter & Gamble (P&G). IoT is a transformative paradigm that connects physical objects to the Internet, enabling them to communicate, make decisions, and operate autonomously.

This model incorporates pervasive computing, RFID, sensor networks, communication technologies, and Internet protocols to foster intelligent interconnectivity. Applications of IoT now span industries such as manufacturing, transportation, healthcare, industrial automation, and emergency response.

The Internet of Things-Driven Big Data Era

The explosion of data generated by connected devices has put a strain on traditional cloud computing architectures. As the demand for real-time processing grows, edge computing has emerged as a promising solution. Various research efforts, including those aimed at improving power grid infrastructure, have demonstrated how edge-based architectures can enhance system security and performance.

Organizations like Bridge Group Solutions are actively exploring edge-powered infrastructures to support high-demand, real-time digital systems.

The Concept of Edge Computing

Edge computing addresses several key challenges: limited bandwidth, data privacy concerns, vulnerabilities in centralized systems, and additional costs related to data transmission, storage, and computation. Many IoT applications—particularly in the Internet of Vehicles (IoV)—require ultra-low latency and high-speed data processing.

Edge computing shifts data processing, analysis, and storage closer to the source—at the network’s edge near terminal devices. This reduces response time, lowers data transmission costs, and supports decentralized computing.

Autonomous vehicles, for example, rely heavily on real-time sensor data. Sending this data to the cloud for analysis introduces delay, which could prove critical in dynamic driving environments. Edge computing enables immediate processing at the device level, improving both safety and efficiency.

Conclusion

This study highlights the value of edge computing as a disruptive technology that addresses cloud computing limitations in real-time IoT environments. With the proliferation of 5G networks, edge computing brings several key advantages:

• Real-time data analytics at the edge
• Reduced operational and data transfer costs
• Improved performance via reduced latency
• Decentralized infrastructure
• Enhanced data security and control

As these technologies evolve, companies like Einfratech are advancing smart integration models that balance performance, scalability, and security across connected ecosystems.