Rugged NVR computers are used to gather, process, and analyze video footage, only sending footage that sets off certain triggers to the cloud for remote monitoring and analysis. This reduces the amount of required internet bandwidth, since not all video footage has to be sent to the cloud, only specific clips where triggers have been set off are sent for additional analysis and inspection. (https://fiberclean.com/) This is different from the traditional model where all video footage was sent to the cloud for remote monitoring and analysis. Deploying rugged NVR computers to manage smart surveillance systems is especially beneficial for those on metered data plans where they pay for the data that they use. Edge computing, as the name implies, is designed to power applications, data use and computing services at the edge of a network – regardless of where that edge is located.

You can employ capabilities purpose-built for specific edge use cases, and choose from more than 200 integrated device services to deploy edge applications to billions of devices quickly and easily. Sending large quantities of data from its origin to centralized data centers is expensive because it requires more bandwidth. The edge computing model allows you to decrease the amount of data being sent from sites to data centers because end users only send critical data.

What is edge computing and why does it matter?

Telecoms have been and will likely continue to be one of the most prominent beneficiaries and providers of edge computing. Because telecommunications organizations help companies set up networks, they rely on edge computing topology to enable a wide range of devices to connect to the organization’s network and function near its edge. Everything from virtual reality headsets to gaming devices to IoT devices on manufacturing floors interact with edge computing topologies set up by telecoms. Some of the most simple forms of edge computing involve basic events and straightforward processes. For example, a device that can monitor someone’s pulse and blood pressure can be positioned on their body and then send information to an edge-based server. Only certain information is then sent to the cloud, while most of it is handled within the edge network.

Before edge computing, a smartphone scanning a person’s face for facial recognition would need to run the facial recognition algorithm through a cloud-based service, which would take a lot of time to process. With an edge computing model, the algorithm could run locally on an edge server or gateway, or even on the smartphone itself. Edge computing comes with significant security concerns, most of which stem from the novel attack surfaces edge topologies create. With a cloud-based topology, even though you have to put up with slower response times, the attack surface beyond the end-user’s local network is limited to the data centers that form your cloud. However, with edge computing, every edge device connected to the system is another attack surface.

Autonomous Vehicles

Despite an ever-increasing number of computing devices and key early-stage gains by edge companies, Satyanarayanan said edge computing is still in a holding pattern. The use of edge computing also eases growth costs as each new device does not add further bandwidth demands on the whole network. Additionally, as most processes occur locally, hackers cannot intercept data in transit.

Banks may need edge to analyze ATM video feeds in real-time in order to increase consumer safety. Mining companies can use their data to optimize their operations, improve worker safety, reduce energy consumption and increase productivity. Retailers can personalize the shopping experiences for their customers and rapidly communicate specialized offers. Companies that leverage kiosk services can automate the remote distribution and management of their kiosk-based applications, helping to ensure they continue to operate even when they aren’t connected or have poor network connectivity. Rugged edge computers are being used in industrial settings to run machine vision applications.

Edge-Enabled Fighter Jets and Drones

And edge computing also addresses security needs by processing sensitive defense data locally to avoid interception or breaches. However, in the same transaction conducted with a distant location, such as a transcontinental cloud server, the round-trip latency could increase to 80 milliseconds. Consider the time it takes to power up a mobile device, wait for a network connection, wait for a secure handshake, transmit data packets, waiting for data acknowledgments after each over thousands of miles between. Although 80 milliseconds may appear insignificant in human time scales, accumulated delays profoundly impact a mobile device’s overall performance and battery life. Since AI algorithms are capable of understanding language, sights, sounds, smells, temperature, faces and other analog forms of unstructured information, they’re particularly useful in places occupied by end users with real-world problems. These AI applications would be impractical or even impossible to deploy in a centralized cloud or enterprise data center due to issues related to latency, bandwidth and privacy.

In practice, cloud computing is an alternative — or sometimes a complement — to traditional data centers. The cloud can get centralized computing much closer to a data source, but not at the network edge. Sending all that device-generated data to a centralized data center or to the cloud causes bandwidth and latency issues. Edge computing offers a more efficient alternative; data is processed edge computing definition and analyzed closer to the point where it’s created. Because data does not traverse over a network to a cloud or data center to be processed, latency is significantly reduced. Edge computing — and mobile edge computing on 5G networks — enables faster and more comprehensive data analysis, creating the opportunity for deeper insights, faster response times and improved customer experiences.

What Is Edge AI and How Does It Work?

Finally, it entails operational technologies (OT) — those responsible for managing and monitoring hardware and software at the client endpoints. What’s challenging here is to encourage collaboration and cooperation between these parties. Breaking down silos is crucial in this case, as one party cannot understand the requirements or perform the duties of the other.

• Edge computing architecture is a modernized version of data center and cloud architectures with the enhanced efficiency of having applications and data closer to sources, according to Andrew Froehlich, president of West Gate Networks.
• Edge computing works by bringing computation and storage closer to the producers and consumers of data.
• This could increase vehicle reaction times and reduce accidents, as well as keep  vehicle operations up when offline or in a rural area, making for safer travel.
• Most data processing takes place outside the central server and the security team’s direct line of sight.
• The explosive growth and increasing computing power of IoT devices has resulted in unprecedented volumes of data.
• Developers of edge-dependent applications may also be more incentivized to ramp things up if they were confident that infrastructure builders were investing big in the edge, Satyanarayanan explained.

It’s called “edge AI” because the AI computation is done near the user at the edge of the network, close to where the data is located, rather than centrally in a cloud computing facility or private data center. With huge volumes of data being stored and transmitted today, the need for efficient ways to process and store that data becomes more critical. This is where edge computing comes in — we can improve performance and reduce latency by deploying processing power and storage closer to the data generation sources. Edge computing can help us manage our ever-growing data needs while reducing costs. This blog discusses the importance of edge computing, its advantages, and its disadvantages. Here is where the viability of the edge computing model has yet to be thoroughly tested.

New strategies for modern service assurance

A single edge deployment simply isn’t enough to handle such a load, so fog computing can operate a series of fog node deployments within the scope of the environment to collect, process and analyze data. In traditional enterprise computing, data is produced at a client endpoint, such as a user’s computer. That data is moved across a WAN such as the internet, through the corporate LAN, where the data is stored and worked upon by an enterprise application.

So, IT and security professionals need to acquire the latest best practices to safeguard their edge computing infrastructure. In any telecommunications network, the edge is the furthest reach of its facilities and services towards its customers. In the context of edge computing, the edge is the location on the planet where servers may deliver functionality to customers most expediently. The hardware required for different types of deployment will differ substantially.

What Are the Advantages of Edge Computing?

Perhaps the most noteworthy trend is edge availability, and edge services are expected to become available worldwide by 2028. Where edge computing is often situation-specific today, the technology is expected to become more ubiquitous and shift the way that the internet is used, bringing more abstraction and potential use cases for edge technology. Fog computing environments can produce bewildering amounts of sensor or IoT data generated across expansive physical areas that are just too large to define an edge. Consider a smart city where data can be used to track, analyze and optimize the public transit system, municipal utilities, city services and guide long-term urban planning.