Cisco white paper estimated that by the end of 2021 50 billion IOT devices will be connected to internet by the end of 2021. Almost 850 Zettabytes of data will be generated each year, and the global data center traffic is almost 20.6 Zettabytes. This indicated that the data sources are undergoing a transformation from large scale data cloud data centers to wide range of edge devices. Existing cloud computing services are unable to manage this threshold of data. A large computational time requirement is also obligatory to do task. This leads to a challenge of managing high network capacity and computing power of cloud. Also, many new kinds of technologies have arrived; For example, Autonomous Car Driving App has a strict requirement that clouds would be unable to compute. Therefore, to fill this loophole a new technology replacing the traditional one has emerged, which is known as Edge Computing.
Brian Nobels in 1997 and his colleagues invented edge computing which is defined as the advanced level of computing in which IOT, and local edge servers are brought closer, and client data is processed at the periphery of the network.
Understanding Edge Computing:
Edge Computing Architecture:
Fundamentals of edge computing include Edge devices, Edge to edge server cluster and cloud cluster. Firstly, Edge devices are the devices that collect data from the operational environment consisting of sensors, actuators, microphones, data loggers, and web application monitors. This also includes Actuators and visualization tools. Secondly, the Edge server cluster are the connections that are developed. It can be peer to peer and long-distance connections are also possible. The edge servers can be device edge or network edge as well. The edge servers also include local edge servers, workloads, and gateway nodes. Lastly, cloud server is used to perform complex computation, resources allocation strategies, data handling techniques and data storage components.
How is it different from Cloud Computing:
Edge computing is different from cloud computing in terms of processing location, latency, data transmission, scalability, reliability, cost efficiency, security and privacy, power and resource efficiency and last integration. Edge computing and cloud computing are complementary technologies, each has its strength and limitations. Cloud computing is a better option for scalability, resource management and handling large scale data productions. Edge computing handles data that is closer to resource. Edge computing is limited by its decentralized nature to cloud solutions. A hybrid approach by combining both approaches is the foremost technique to achieve efficiency at its peak. This also highlights the importance of integration for innovation and better technology ecosystems.
Revolutionizing the Industry through Edge Computing:
The transformation of technologies has proved to be the major evolution for industries; such that it has enabled an unmatchable level of efficiency, innovation and scalability. Among these technologies cloud and edge computing proved to be the most impactful ones. Despite of their differences they complement each other to shape the digital world. Edge computing has emerged as a solution for growing demand to handle real time data processing. It is particularly efficient in applications that have delays resulting in significant consequences. For example, in smart manufacturing edge computing enables real time processing, exceptional control of machinery by reducing its downtime. The cloud integrates data from multiple facilities for predictive maintenance predictive maintenance and long-term optimization. Looking forward to the technology advancements in AI, Machine learning and deep learning has further revolutionized edge computing.
Evolutionary impact on Embedded Systems:
After 20th century the evolution in semiconductor technology, slenderizing, and miniaturization has significantly advanced chip-making processes, enabling the development of highly efficient, compact, and powerful integrated circuits. These advancements have revolutionized industries by powering modern technologies such as smartphones, IoT devices, autonomous systems, and edge computing. Miniaturized chips not only enhance device performance but also reduce energy consumption, paving the way for sustainable and scalable solutions. This progress in chip technology continues to drive innovation, shaping the future of electronics and computing.
Integration into Automated Systems:
Automative industries have started to invest dollars of money in autonomous cars using cutting edge new technologies. Complete dependency on the machine is not possible, safely operating is a better solution. To work accurately these vehicles, collect data from different orientations, surroundings and climatic conditions. It should be intelligent enough to decide on its own based on previous experiences. Efficiency is based on data collected and analyzed for providing alerts and communicating the information with nearby vehicles with no network. IOT sensors collect vast amounts of data from nearby devices and Edge computing processes this data to provide actionable results. This dichotomy of Edge computing and IOT is enabling new endeavors of productivity in their automated workflows.
Enhancing Blockchain Technology:
Blockchain establishes a trustworthy resource allocation environment for IOT devices. It helps in increasing reliability, security and privacy of the systems. The intervention of clouds makes the system behave like a client server system. Combining IOT and Edge computing provides universal internet access for blockchain networks. End to end encryption of transaction of data, sensor readings maintains security of system. Consequently, data produced at the end is reliable.
Transformation of 5G and Telecommunications:
IOT and Edge computing has revolutionized the telecommunication industry; as the recent transformation from 4G to 5G has proven that. Extremely high speed and less clock cycles: less delay in easy words has enabled the way for the conversing data consistently; even considering the large-scale networks. Edge computing paves the way for data to process in vicinity of the source resulting in reducing the use of complementary servers and enabling real time decision making. The evolution of technology has given rise to innovations like smart cities, monitoring infrastructure using IOT sensors, fault mitigation, creating information-based pools, PaaS and SaaS solutions. Together, IoT and edge computing, powered by 5G, are reshaping data transmission and procession, and utilized across the globe.
Advancements in Healthcare:
Edge computing and IOT captivate health sectors by creating new opportunities for encouraging patient care. Collecting, securing and processing patient data becomes real with the help of edge computing. Continuously changing real time decisions and actions are provided by healthcare providers even to rural areas. With the deployment of edge technology e-services take milliseconds of time to process. Furthermore, anytime access to information is more possible than depending on one centralized database. Nowadays many medical devices are provided enough to continuously collect and stores medical data of the patient which plays a major role in medical treatment and correct diagnosis. Apart from this, edge computing lays its footprints in sectors/applications like video monitoring, remote diagnosis, video conferencing, software-defined networking, connecting patient with online doctors.
Technical Challenges in Implementing Edge Computing and IOT:
With emerging advancements in technology, the deployment of edge computing and IoT systems comes with their own set of challenges that demand innovative solutions and robust strategies. From overcoming network delay to ensuring data security, implementing these transformative technologies involves addressing several technical hurdles. The major technical challenges in the way of ideal implementation of edge computing and IoT are security and privacy concerns, scalability issues, device compatibility, power and energy efficiency, data storage limitations, real-time data processing, network reliability, high deployment costs, and device maintenance updates.
The Transformative Potential of Edge Computing and IOT:
The evolution of edge computing and IoT lies in their ability to drive innovation and efficiency across various industries. A critical analysis reveals that these technologies are not only reshaping existing systems but are also paving the way for future directions and innovations in embedded and automation systems. With the integration of emerging technologies like AI: further expanding to general and super intelligence, 5G: 7G to 10G networks by 2030, and IoT, the landscape of smarter, more connected devices is rapidly evolving. The rise of trends in Edge-AI has enabled the development of smarter systems capable of real-time decision-making. Furthermore, expanding edge computing’s role in autonomous systems is unlocking new possibilities, making them more adaptive, efficient, and capable of functioning independently in complex environments. Together, these advancements signify a leap forward in technological evolution, bridging the gap between intelligent computing and practical applications.
