How IoT and Edge Computing Can Function Jointly: There is a rising demand for edge computing as many tech companies seek less complicated ways to enhance their products and cut operating expenses. Computing on edge facilitates data processing without relying primarily on centralized data centers, and Iota devices appear to be at the vanguard of this computing paradigm’s use.
Recent statistics from the Global Edge Computing Industry Report 2022 indicate that the edge computing market is estimated to reach $155.90 billion by 2030, expanding at a CAGR of around 38.9% over the forecast period.
There is also a comparable surge in the global adoption of IoT devices, which Statista predicts will reach approximately 29 billion by 2030.
Given these good advances, it is necessary to investigate how IoT and edge computing may collaborate.
Edge computing and the Internet of Things
Edge computing is a computing paradigm that calculates data resources at the network’s edge or near the data-generating devices. This computing approach eliminates the need to transfer data to distant data centers for processing and analysis.
IoT is a collection of networked smart devices that utilize sensors, communication gear, and embedded systems to gather, transfer, and process data in cloud-hosted data storage centers or at the network’s edge. IoT refers to the technology that provides physical items with internet connectivity, intelligence, and analytics.
Internet-enabled sensors and analytic capabilities are now powering a rising number of devices that are transforming how machines function across various industries. With this surge in use cases, IoT devices will process more data. Nonetheless, additional data to process can lead to delays, privacy concerns, and slower data processing required to tackle urgent problems.
There is a need to improve IoT devices and decrease these bottlenecks, which are largely created by the transfer of massive data chunks from IoT devices to data centers for processing and analysis. In order to support real-time computing, IoT data must be collected and processed at the edge.
How the IoT and edge computing can operate together
The IoT sector and edge computing can collaborate to improve performance in multiple ways. Today, edge computing is being implemented on IoT devices. Listed below are a few prominent use cases where edge computing can fuel IoT in the present and future.
Condition-based monitoring in the Internet of Things devices
Condition-based monitoring is one example of how edge computing may power the Internet of Things. IoT condition monitoring is an integral component of company maintenance and monitoring strategies. Condition-based monitoring is a term used to describe the monitoring of device conditions to identify changes and how those changes may lead to malfunctions.
Condition-based monitoring on the Internet of Things focuses on data input. And output to check for changes and potential courses of action to prevent device failure or downtime. For this to function, data must migrate between sensors, networks, and IoT-connected devices. Where it is analyzed and interpreted for real-time, high-quality predictive maintenance reports. IoT condition-based monitoring devices require edge computing to improve performance due to the large amount of data they process.
With edge computing, IoT devices for condition-based monitoring can analyze data more quickly. Eliminate latency, and offer engineers information that will assist them in making better maintenance decisions. Incorporating edge computing is essential in this situation. It will enable enterprises to be more proactive in assuring system effectiveness and substantially cutting maintenance costs.
Improved AI application in IoT
With the global development of smart IoT devices, one cannot disregard the role of artificial intelligence in IoT. There are currently self-driving cars, robots that assist in numerous manufacturing production processes, computer vision, and other applications of AI in IoT. All of these technological improvements are enabled by the application of AI to the Internet of Things.
However, the increased application of AI in IoT devices can be made more efficient via edge computing. Unquestionably, a vast quantity of data is required for AI to generate correct predictions in IoT devices. Edge computing can be used to reduce the data processing time by keeping data computation to the network’s edge.
Industry 4.0 and Industrial IoT
In recent years, Industrial IoT and Industry 4.0 have acquired significant traction. The two concepts revolve around utilizing the internet and massive amounts of data to power and govern industrial smart devices.
Prior to the present, industrialization was mostly driven by humans and mechanical equipment, also referred to as dumb tools. Nevertheless, given the pervasiveness of technology in many facets of modern corporate life. Massive data and real-time analytics are being applied to industrial machinery in order to increase efficiency and output.
In order to communicate with other devices in real time, IIoT also relies on collection. An analysis of vast amounts of data, as well as the internet. Due to the amount of data computation necessary to power IIoT. And Industry 4.0, edge computing becomes essential for enabling computing in IIoT.
Why IoT and edge computing will remain complementary
Current trends indicate that the confluence of edge computing and IoT has not yet reached its conclusion. Despite this, there are numerous reasons why IoT and edge computing will continue to collaborate.
Computing in the network’s periphery ensures the IoT’s super-efficient connectivity. The Internet of Things (IoT) does not need to be in constant contact with the data held in the central cloud since edge computing enables data computation at the edge. The application of IoT in organizations that provide financial services, healthcare services, and autonomous vehicles cannot tolerate delay if they are to please customers. Consequently, IoT in these domains may continue to rely on edge computing in order to achieve business objectives and customer pleasure.
