Cloud computing provides the way to process data using shared computers and provides the processed data to other devices on demand. The major advantage of cloud computing is found storing and processing data by enterprises for easy and better access to the data.
Edge computing performs data processing at the source of data, which is often referred to as the edge of the network. The major drawback of cloud computing is the bandwidth required for data upload and download. Edge computing drastically reduces the bandwidth by processing the data at the source of the data itself. To support such procedure, the network terminals (laptops, tablets, smartphones etc.) are to be huge in resources and physical specifications alongside being connected to the network all the time. Edge computing can be achieved either through wired communication or wireless communication.
Read more: 5G networks are more than smartphones.
In Edge computing, there are three phases to achieve processed data - southbound communication, processing and northbound communication.
Southbound communication - The southbound communication involves in collecting raw data from different sources like smartphones, laptops, tablets, sensors etc.
Processing - As the purpose of Edge computing is to process the data, data is processed locally at the gateway level itself.
Northbound communication - The northbound communication is the process of communicating the locally processed data to the cloud.
Benefits of Edge computing:
Lower costs: The cost for uploading the raw data and downloading the processed data can be reduced in terms of time for data transfer, resources for processing and the bandwidth required. If the data to be processed is a video, then the cost for processing increases further. With Edge computing, redundant data transfer can be minimised, by processing the data locally.
Lower latency: Edge computing helps to reduce the latency. Suppose that there is a situation where you need to turn off a remote switch, which operates a heat sensor. In this context, latency is the major parameter that affects the situation. Lower latencies can be achieved through Edge computing.
Enhanced privacy: As the data is processed at the gateway itself, the privacy of data is enhanced, where there are billions of data sources.
With respect to industry, IoT is emerging as Industrial IoT (IIoT), where enormous data is to be processed and analysed. From the survey by DataSphere, we will be generating 163 zettabytes of data by 2025! We need humongous abilities to process such huge data.
Qualcomm's part in Edge computing:
In regard to wireless communication, Qualcomm is working on the wireless communication technologies like wide-range technologies like WAN, long-range technologies like LANs, Wi-Fi, and short range technologies like Bluetooth, 15.4 and PANs. As a part of providing security through edge computing, Qualcomm is placing secuirty on the very hardware itself.
Qualcomm has been working in this direction with different OEMs to deploy IoT gateways. Including Qualcomm Snapdragon 410E chipset, Qualcomm is providing support to Amazon Web Services Greengrass. With the efficiencies of CPU, GPU and DSP along with AWS software helps enhance the quality of edge computing.
Image source: Microsoft.
Read more: Five ways data is changing technology.
Read more: 5G networks are more than smartphones.
In Edge computing, there are three phases to achieve processed data - southbound communication, processing and northbound communication.
Southbound communication - The southbound communication involves in collecting raw data from different sources like smartphones, laptops, tablets, sensors etc.
Processing - As the purpose of Edge computing is to process the data, data is processed locally at the gateway level itself.
Northbound communication - The northbound communication is the process of communicating the locally processed data to the cloud.
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| Decision making at the data source |
Lower costs: The cost for uploading the raw data and downloading the processed data can be reduced in terms of time for data transfer, resources for processing and the bandwidth required. If the data to be processed is a video, then the cost for processing increases further. With Edge computing, redundant data transfer can be minimised, by processing the data locally.
Lower latency: Edge computing helps to reduce the latency. Suppose that there is a situation where you need to turn off a remote switch, which operates a heat sensor. In this context, latency is the major parameter that affects the situation. Lower latencies can be achieved through Edge computing.
Enhanced privacy: As the data is processed at the gateway itself, the privacy of data is enhanced, where there are billions of data sources.
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| Objectives of Edge Computing |
Qualcomm's part in Edge computing:
In regard to wireless communication, Qualcomm is working on the wireless communication technologies like wide-range technologies like WAN, long-range technologies like LANs, Wi-Fi, and short range technologies like Bluetooth, 15.4 and PANs. As a part of providing security through edge computing, Qualcomm is placing secuirty on the very hardware itself.
Qualcomm has been working in this direction with different OEMs to deploy IoT gateways. Including Qualcomm Snapdragon 410E chipset, Qualcomm is providing support to Amazon Web Services Greengrass. With the efficiencies of CPU, GPU and DSP along with AWS software helps enhance the quality of edge computing.
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| Digital Enterprise Vision of Edge Computing illustrated by Microsoft |
Read more: Five ways data is changing technology.
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