The Impact Of Edge Computing In Disaster Response And Emergency Services – Modern technologies such as self-driving cars, smart buildings, and IIoT or industry 4.0 production generate a lot of data which causes traffic congestion to the servers. An elegant solution to this challenge is to move some functions from powerful but remote data centers to small processors at the edge or directly to IoT devices.
The concept is not new, but the arrival of high-speed 5G networks has made it more feasible. It is expected that soon 40% of data extracted from the edge will be stored and processed locally, without the need to move it to a central storage.
The Impact Of Edge Computing In Disaster Response And Emergency Services
Edge computing is a distributed IT infrastructure that processes raw data close to the source, primarily IoT sensors. This allows you to distribute the workload among many computers, instead of relying on one computer to manage the endless traffic of countless devices. Finally, only actionable results are sent to the main server. The main servers are located in remote areas, where electricity and rent are cheap.
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Moving parts of your work to the veil gives you increased bandwidth and lower latency than a framework built on remote, centralized servers.
In addition, edge computing reduces cloud storage footprint by storing only the data you really need and use.
The Cisco-coined terms “fog computing” and “edge computing” are used interchangeably because both require processing and analytics tools to be placed close to where data is being generated. This happens often. The important question is: how close is it?
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Remote computing, or fog, takes place over a local area network (LAN) between edge servers and mainframe servers. In edge computing, data is processed by devices that are physically connected to sensors.
Similar to edge computing and fog computing, cloud computing supports the concepts of data storage and distributed processing. It replaces or complements traditional data centers, enables the aggregation of data from multiple sources, and provides powerful analytical tools. However, cloud facilities can be hundreds or even thousands of miles away from where the data is extracted.
In fact, the three types of computing are different layers of systems that process IoT data. Mostly, the layers exchange information with MQTT (Message Queue Telemetry Transport), a simple IoT protocol for publishing or subscribing to communications.
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An IoT system architecture that outsources some peripheral processing functions can be represented as a pyramid with a computing layer at the bottom edges.
In the edge computing layer, processing is done on edge domains that directly communicate with tens, thousands, or even millions of sensors and controllers. These servers also have analytics capabilities and can run ML models to make real-time decisions on your site. For example, you can adjust the movement of a robot’s arm or predict equipment failure.
The edge layer also filters raw data according to predefined parameters to eliminate traffic congestion on the way to the cloud. A common example is video recognition. Instead of receiving the full stream in the cloud, the local device pre-processes everything the camera “sees”, cuts out the irrelevant parts, and sends only the relevant video data to the server.
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The fog computing layer bridges the edge and clouds. Here, fog nodes or IoT gateways perform further filtering and analysis. This layer can process more data than the edge. However, many systems do not require this type of mediator. In other words, distance calculation is not required for edge calculation, but fog calculation is not a substitute for corner calculation.
The cloud computing layer collects valuable data from all edge devices and fog nodes and stores it in a data warehouse. That’s where business intelligence lives and big processing power can be leveraged to do big data analysis. For more information about IoT cloud computing services, see the article Understanding IoT Platforms.
Broadly speaking, edge computing can be considered an important extension of cloud computing. Let’s see how it works in real life.
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Edge computing affects all major industries, including manufacturing, healthcare, agriculture, transportation, security, and more. In particular, we will promote the Internet of Medical Things (IoMT), autonomous vehicles and telematics technology, and predictive maintenance, a proactive way to protect industrial machinery.
The United States Postal Service (USPS) delivers 7.3 billion packages a year, or 231 packages per second. To handle this massive workload, the company has deployed AI algorithms on edge servers in 195 locations. Each server has built-in optical character recognition (OCR) functionality that analyzes the images of more than 1,000 mail sorting machines every day.
On-board deep learning models sort packages, check that the mail rate matches the package’s size, weight, and destination, and encode barcodes, including damaged barcodes. Edge Intelligence can also help you find lost luggage. In terms of AI, it used to be 8 to 10 people and days can be found in a few hours with a few people.
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Dutch technology company Kepler Vision has designed the Night Nurse Box to keep elderly patients safe at night. The device runs Kepler software to detect falls and physical distress and alert staff when needed.
Instead of sending video data to the cloud, the Edge Box uses internal computer vision to determine if a nurse should intervene. This ensures that your system is not affected by internet connection failures. Additionally, replacing the front sensors with Edge boxes is estimated to eliminate 99% of false alarms.
The Serichal Tunnel in Spain’s Galicia region utilizes 5G and edge computing to capture and analyze data from the tunnel’s sensors, cameras, and connected vehicles. Administrators can remotely monitor what is happening in their infrastructure using a dashboard.
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Drivers traveling through the tunnel receive notifications and alerts about the presence of pedestrians and emergency vehicles, traffic delays and accidents, weather conditions at the exit, and more. The project is supported by major telecommunications companies Telefonica and Nokia.
In response to the growing demand, many technology companies have launched computing devices. The key players here are:
Typically, the latter partner with the former to take advantage of storage and processing power. Below, we look at edge products from four popular vendors (two from each group).
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FreeRTOS and Greengrass extend the AWS IoT platform and enable third-party developers to program and manage edge devices that are eligible to run on the Amazon cloud. Click here for a list of Amazon Hardware Partners.
FreeRTOS, a free operating system for microcontroller units (MCUs), directly connects MCU-based sensors with cloud operators or more powerful edge devices running Greengrass.
The latter allows you to write code, train machine learning models using AWS services, and deploy to proven physical locations and IoT gateways. All communication is done via the MQTT protocol.
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FreeRTOS makes it easy to build a peer-to-peer platform called SOLshare. It will connect home solar energy systems across Bangladesh and enable them to harness their excess energy. Another application is the monitoring of hydraulic cranes in commercial vehicles operated by equipment manufacturer Shimadzu. This helps reduce equipment downtime and maintenance costs.
Eco Fit, previously adopted by Greengrass, uses edge computing to analyze exercise equipment data to improve maintenance.
Azure Stack Edge uses a service-as-a-service model to provide customers with edge processing tools that are compatible with other Azure products.
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Devices ordered through the Azure portal take advantage of Microsoft AI and IoT services, computing, and storage capabilities. You can use them to run containerized applications and machine learning models built and trained in the Azure cloud. The device has local storage to support fixed scenarios in a harsh environment.
Azure AI and edge tools are used by Japanese marine equipment manufacturer JRCS to implement computer vision and ensure safe navigation. Another example is how Olympus Medical Systems uses Azure tools and AI to analyze and interpret data from video cameras installed in operating rooms in real time.
The company, a global provider of graphics processing units (GPUs) and systems-on-chips (SoCs), launched its computing packages at EGX 2019. It is compatible with NVIDIA AI Enterprise software integrated with OpenShift, RedHat’s Kubernetes platform. . This enables companies to develop and train cloud models and manage and orchestrate AI deployments across all NVIDIA certified servers manufactured by Dell, Cisco, Lenovo, Hewlett-Packard and other industry leaders.
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EGX is also pre-connected to major IoT devices, allowing users to manage edge computing operations through AWS Greengrass or Azure IoT Edge.
Walmart, the world’s leading retailer, chose EGX to analyze 1.6 terabytes of data generated from its stores every second. Edge AI programs perform many functions. For example, send alerts when drawers need to be reclosed or new checkout lanes opened.
NVIDIA is also a popular choice for smart city solutions, including video camera data analysis to improve traffic flow, improve traffic flow, and increase pedestrian safety.
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Cisco, a US-based global networking leader, is one of the pioneers of edge computing. The company provides orchestration intelligence software that powers industrial gateways and service portals. Simplify data generation from IoT sensors with built-in industry-standard connectors. The software then performs processing of this information in real-time.
Cisco is already connected to Microsoft Azure, allowing you to send pre-arranged data to multiple cloud locations.
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