See How Edge Analytics Complements Cloud Computing To Design Better Industrial Solutions

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See How Edge Analytics Complements Cloud Computing To Design Better Industrial Solutions

Cloud computing and edge computing are different approaches but complementing each other and purely depends on the application implemented.

· IoT Zone ·
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Connected applications and systems are moving to the cloud with the implementation of IoT. Parallelly, the number of end-devices and their data generated on the cloud is also increasing. Sensors, mobile devices, wearable, and many other connected devices in the IoT ecosystem generate a huge amount of decentralized data. Lack of reliable connectivity, delays, and difficulties in processing this huge data on the cloud, raised a challenge in analyzing and extracting important insights from this data.

To overcome this challenge, enterprises are leveraging edge analytics along with cloud computing. This will bring instability in the IoT network by bringing the computational power near to the data source and will reduce the delays in analytics, resulting in instantaneous vision and resolutions. Edge analytics brings algorithms to the data and provide important insights.

How Analytics Happen On Edge?

With the advancement in the semiconductor technology, MCUs and Processors are equipped with more processing power, specialized hardware components, and computation capabilities helping with faster analytics on edge by deploying advanced machine learning methods such as deep neural networks or convolutional neural network. 

A model developed on popular frameworks like TensorFlow, Keras, and Caffe can be deployed after optimization to run on inference devices like Andriod and micro-controllers. Inference engines designed considering capabilities of MCUs, like TensorFlow-Lite, TensorFlow-micro, CMSIS-NN, etc. are available which can execute the quantized model on the edge for faster analytics. 

Edge analytics benefits organizations where data insights are needed at the edge. According to a report “Global Markets for Automotive Sensor Technologies”, average number of sensors used in a car has been increased from 50-60 to 100+ and it is going to be 200+ shortly which will generate a huge amount of data. Edge analytics in automotive will help companies collect, analyze, and process data in real-time, allowing them to take necessary actions immediately. Also, intelligent applications like collision avoidance, traffic routing, eyes-off-the-road detection systems, etc. can be designed through artificial intelligence and machine learning on edge. 

This ensures optimized asset usage, low maintenance, and passenger safety. Similarly, IoT powered healthcare devices can collect patient’s data. Edge analytics can analyze the collected data without the need for continuous network connectivity. A clinician treating a patient with a mobile/tablet will be able to enter patient data into the analytics platform at the edge where it is processed and displayed in near real-time. This helps to treat patients faster reducing their visit frequency. Also, it adds a secure layer of computing power between the cloud and the device, thus safeguarding the patient data.

How Analytics Happen On Cloud?

Having seen the advantages of edge analytics, it is important to understand that it does not replace the cloud, but complements cloud computing with real-time analytics as it is close to a data source. Few processes will continue to execute in the cloud.

  • Training of the machine learning algorithm: The development of a machine learning algorithm depends on large volumes of data, from which the learning process draws many entities, relationships, and clusters before training the model. This can be carried out on the cloud along with training the model. 
  • Processing power & storage capacity: Unbounded scalability for storage and processing power, ease of deploying analytics make cloud analytics non-replaceable. Historic data is stored on the cloud that can be useful in the future as cloud-based analytics works on a larger variety of data. For instance, it can add historical data to streaming data or analyze all the output from all the devices using edge analytics.
  • Taking advantage of all edge devices of an application being connected to a single cloud, the cloud enables us to perform super analytics on edge analytics. Cloud has the means to manage and turn that data into meaningful predictions and analysis.

How Edge Analytics Complements Cloud?

Real-time decision-making in IoT systems is still challenging due to factors like latency, bandwidth, power consumption, cost, form factors, and various other considerations. This can be overcome by adding artificial intelligence to the edge.

  • Less utilization of data bandwidth/transfer: Shifting large amounts of data to the cloud for processing can consume high data bandwidth and produce a noticeable lag that may harm time-critical applications. To avoid this delay and eliminate dependence on data bandwidth, data processing on edge can be executed.
  • Eliminating the need for continuous connectivity to cloud: In industries like oil, gas, or mining, where company employees work on remote sites far from populated areas, the connectivity is non-existent. In such scenarios, sensors on edge devices such as robots can capture data, analyze it, and monitor operating parameters whether or not inside their normal range of values.
  • Real-time performance with faster processing: Edge computing dramatically reduces the amount of data that has to be sent over the network, thereby reducing network congestion and speeding up operation. Instead of running processes in the cloud, Edge Computing runs processes on local places like a computer, IoT device, or Edge Server. By bringing computation to a network edge, long-distance communication between a client and server is reduced and real-time insights are obtained.
  • Enhanced data security (as closer to data source & location-aware): To explain, instead of having a security camera stream the content of its video feed up to the cloud to be analyzed for certain situations (unknown people, objects, etc.), that analysis can be done within the camera itself. Data privacy and security concerns associated with biometric data make it extremely important to only use the data locally on the device and not send it out over a cloud connection.

Cloud computing and edge computing are different approaches and purely depends on the application implemented. While they don’t discredit but complement each other. There can’t be a one-fit solution for all the scenarios. There are few key factors like real-time performance, cost of bandwidth, size of data, the complexity of the application, etc. which decides whether to go for edge analytics or cloud analytics or both (best of both worlds). 

cloud, cloud computing, edge computing, iot

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