Source | IoT Home
Due to the technical advantages of edge computing, it has received significant attention in the industrial sector. Currently, the most innovative companies are deploying edge intelligent IoT.
Grand View Research (a U.S. consulting firm) predicts that by 2027, the market value of edge computing will reach $43.4 billion, with an annual growth rate of 37.4%. This growth is attributed to the potential of advanced edge intelligence in optimizing, accelerating, and simplifying industrial processes, providing a competitive advantage to organizations that adopt this technology early.How Edge Computing WorksEdge computing is a distributed computing system model that brings data storage and processing closer to the source or location of data acquisition. It enables real-time analysis of large amounts of distributed data in a network-secure and scalable manner, allowing integration of field devices from numerous vendors, technologies, and protocols.In an IoT environment, edge computing refers to systems that allow data to be collected, processed, and analyzed from the hardware and software layer (edge nodes), rather than sending data to more distant servers for processing and analysis.Currently, deploying edge computing in IoT environments is equivalent to optimizing processes, as it avoids the transmission of large amounts of data from IoT devices to the cloud.Edge Computing vs. Cloud ComputingTo some extent, edge computing can be understood as an alternative to cloud computing. Therefore, cloud computing is another model for data interaction in IoT. In this case, remote software can be accessed to store and process data. According to this system, users connect to this system via the cloud each time they need to retrieve or use this data, without needing to install any software locally.Although cloud computing offers the advantage of remote storage, the system may be inefficient because data must be transmitted to the server. This transmission can take a long time and slow down processes that require faster response times.To address this shortcoming, edge computing brings data processing closer to the source data, improving processing speed and immediacy. In this sense, edge networks enable businesses to analyze data in real-time, which is increasingly important in critical infrastructures such as energy, railways, oil and gas, and water industries.As IoT systems are forced to handle increasing amounts of data (by 2025, data generated by IoT devices is estimated to reach 79.4 Zettabytes), edge computing provides a more efficient alternative for managing industrial IoT systems.Advantages of Edge Computing
- Cost savings, as businesses can limit the amount of data processed in the cloud, they can save money on cloud services.
- Low latency, it can collect, send, analyze, and retrieve data with minimal latency.
- Connection reliability, as edge computing becomes increasingly common in mission-critical infrastructures, this reliability is becoming more important.
- Privacy and security, edge computing reduces the amount of data transmitted over the network and decreases the amount of data stored centrally by third parties, which is good news for businesses from a risk and compliance perspective.
Applications of Edge Computing in the Industrial SectorToday, many industrial companies are undergoing digital transformation. Connecting devices and collecting and utilizing data is crucial for driving their business success.For critical service sectors such as electricity, gas, or water, business continuity is essential. Monitoring assets to detect or even prevent failures is a fundamental function. However, their assets are often located in remote areas. In this case, edge computing enables real-time analysis, with data processing occurring closer to the assets, significantly reducing reliance on connectivity and shortening response times.Renewable energy. Edge computing can have a significant impact on the sustainable management of renewable energy resources such as solar and wind energy. Edge computing algorithms can assess and even predict energy supply and demand in real-time, leading to substantial improvements in energy balance. In this regard, companies seeking to reduce carbon emissions are increasingly looking at the combined use of edge computing and cloud.Distributed manufacturing. Distributed or decentralized manufacturing is defined as the production of its products in several smaller, geographically dispersed factories. Due to the need to frequently transmit large amounts of data between multiple factories, the costs of deploying and maintaining these networks are very high. While cloud computing has made significant progress in these environments, combining it with edge algorithms can optimize investments, enhance data security, and help comply with industry regulations that are less suited to cloud environments.
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