The Role Of Edge Computing In Accelerating Iot Innovation

Over the past few years, edge computing is expanding in both industrial as well as academic circles substantially. It has gained attention because of its integrating capacity in advancing various state of the art technologies such as Internet of Things, 5G, artificial intelligence and reality.

In this blog, we will discuss various computing paradigms for IoT which offers an overview of the current cutting edge computing approaches which can be used with IoT.

Internet of Things is a new paradigm for attaching various physical objects to the Internet so that they can interact and make informed decisions. New technologies that fall under this paradigm include pervasive computing, communication technologies, sensor networks and internet protocols. Physical things have the ability to intelligently collaborate and establish connection with the internet along with operating autonomously and introducing innovative applications in IoT. A variety of industries are there for such applications such as manufacturing, transportation, healthcare, industrial automation and emergency response.

Edge Computing

A massive amount of data produced by IoT devices is used which is managed by the architecture of the cloud. It also encounters various challenges such as increased bandwidth requirement, lengthy transmission time and latency between IoT devices and cloud. In order to overcome these difficulties, the concept of edge computing has emerged. The approach of edge computing boosts latency, scalability and privacy factors by processing data at the source while enabling real time predictions.

In an extension of cloud computing, edge computing places computer services at the edge of the network where they are more accessible to the end users. Edge computing shifts from cloud servers and push it towards the edge of a network. Services such as shift services, computational data and applications are done by it. The content providers and application developers then can provide all the services to their users that are nearby located. It is unique in that it may be used for a variety of applications because of its high bandwidth, very low latency and fast access to the network data.

Advantages of Edge Computing based IoT

The close proximity and low latency of edge computing provide a solution to the response delay faced by user equipments while accessing typical cloud services. Edge computing can drastically reduce response time which includes communication, processing and propagation delays.
IoT devices often have limited energy supply due to their size and intended user scenarios, yet they are expected to conduct complicated activities that are frequently power intensive. Edge computing enables IoT devices to offload power consuming computation task to edge servers proposing solution.
Edge computing has the benefit of allowing local deployment of customised security solutions. Majority of processing can be done at the network edge using this approach, implying necessity of less data transport.
Edge servers can handle as well as acquire data generated by user equipments based on their geographical locations with location awareness. As a result, personalised and location specific services can be offered to user equipments allowing edge servers to collect data directly from nearby sources without sending it to the cloud.
Edge computing results in less data transmission, lower bandwidth consumption and lower latency, which has the advantage of minimising data uploading volume.
Its servers are better equipped to adapt an accommodate to the various user equipments and network conditions which leads to an optimum use of network resources. It can effectively handle a large amount of traffic, improving network performance.

Role of Edge Computing in accelerating IoT innovation

Edge computing based Internet of Things can be integrated with several enabling technologies. The need for intelligent edge devices has grown, the industry has responded with innovation and adoption of intelligent edge architectures. Innovations architecture support real time, mission critical applications that work with a wide variety of devices. Edge computing applications are greatly profited from the powerful processing capabilities of deep learning, enabling them to handle intricate scenarios like video analytics and transportation control. It also offers specialised hardware foundations and platforms to effectively support deep learning operations at the edge.

Conclusion:

Edge computing is increasingly regarded as a viable and promising solution to address the complexities to manage numerous sensors and devices along with the demands for resources they require. It involves placing data processing and storage to the edge of network dragging them closer to the end users. Therefore, by dispersing compute notes across the network, it is possible to reduce message exchange latency and reduce the computational load on the centralised data centre.

The Role of Edge Computing in Accelerating IoT Innovation