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IoT and Edge Computing: combining technologies for smart solutions 

Smart devices connected to the Internet of Things (IoT) send and receive large amounts of data to and from other devices, generating a lot of data that can be analyzed and processed. IoT data can be collected and processed at the edge of a network instead of being sent to a datacenter or cloud using edge computing, a strategy for computing on location where data is collected or used. Data analysis in real-time is made possible by IoT and edge computing together.

Edge computing and IoT: what are they?

Physical objects are connected to the internet via the Internet of Things (IoT). The Internet of Things refers to any system of physical devices receiving and transferring data over networks without the need for human intervention. Data is continuously sent, received, and analyzed in a feedback loop in an IoT system. Humans or artificial intelligence and machine learning (AI/ML) can conduct the analysis in near-real time or over the course of a long period of time.  It is generally assumed that anything referred to as smart is connected to the Internet of Things. Among these technologies are self-driving cars, smart homes, smartwatches, virtual and augmented reality, and industrial IoT.

Typically, edge computing takes place near either the user’s location or the data source’s location. It benefits users by providing faster, more reliable services with better user experiences by placing computing services closer to these locations, while companies benefit by being able to identify trends, offer better products, and support applications that are sensitive to latency. To handle increasing numbers of devices and data, companies can use edge computing to use a common pool of resources across a number of locations to scale centralized infrastructure.

What’s the difference between an IoT device and an edge device?

A network edge device is a piece of hardware located at the edge of the network with enough computing resources, memory, and processing power to collect data, process it, and execute on it in almost real-time without help from other parts of the network.

Data is gathered from an IoT device, which is a physical object connected to the internet. A device at the edge of the network collects and processes data. IoT edge devices can make low latency decisions and processdata within milliseconds, allowing them to be considered part of the Internet of Things. Devices connected to the Internet of Things are sometimes called edge devices.

IoT and edge: how do they relate?

The Internet of Things benefits from having compute power closer to the location of physical devices and data sources. The data generated by IoT devices must be analyzed at the edge instead of traveling back to a central location before being analyzed. This will enable IoT devices to react faster or mitigate issues.

IoT devices use edge computing to process and store data at the edge. Incorporating IoT with edge offers the following benefits: 

  • Improved IoT-central network communication latency.
  • Response times are faster and operations are more efficient.
  • Increased bandwidth on the network.
  • In the event of a lost network connection, systems continue to operate offline.
  • Using analytics algorithms and machine learning, local data is processed, aggregation is performed, and rapid decisions are made.

Using IoT gateways, data can be sent from the edge back to the cloud or centralized data center, or it can be sent locally to edge systems for processing.

Cloud computing and edge computing

Datacenters often house compute resources and services in cloud computing models. For IoT devices to connect to the internet, clouds often provide a portion of the network infrastructure. For a variety of reasons, edge devices require network connectivity to central locations: To manage remotely, receive automation instructions, forward telemetry traffic for analytics, and send data information that will be recently stored in databases, and analyzed to achieve business goals.

It is possible for a cloud service to provide data transfer from an edge device to a datacenter across a cloud—or the edge device could send back to a datacenter a log of the decisions it made to store, manage, process, or analyze big data.

Edge computing and IoT use cases

A factory machine, for example, can be connected to the Internet of Things through the Industrial IoT, or IIoT. If you think about the lifecycle of heavy machinery used in a factory, you’ll see what I mean. As equipment ages, it may be subjected to different stresses, and breakdowns are expected. It is possible to add IoT sensors to parts of the machinery that have a tendency to break or overuse. These sensors can collect data that can be analyzed and used for predictive maintenance, resulting in a reduction in overall downtime.

The example of autonomous vehicles illustrates why IoT solutions and edge computing should work together. Data from traffic, pedestrians, street signs, stop lights, and the vehicle’s systems should be collected and processed in real time by autonomous vehicles driving down the road.

In an emergency, sending data to the cloud for processing would take too long in case the vehicle had to stop or turn quickly. In order to avoid accidents, edge computing allows IoT sensors in the vehicle to process data locally in real-time based on cloud computing services. 


As a result of the combination of IoT and edge computing, smart solutions have received a whole new level of versatility. By collecting, processing, and analyzing data closer to the source, IoT devices and applications have significantly improved their performance, speed, and efficiency. From smart cities and homes to manufacturing and healthcare, this technology has enabled a variety of innovative use cases. Security, privacy, and interoperability are challenges as well, as with any emerging technology. There are many potential benefits associated with IoT and edge computing, and their integration could revolutionize the way we live, work, and interact with the world.

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