Living in the Edge with the Internet of Everything

If data is like a high school campus, then cloud computing is rather where like attracts in a bookish school marm way in a central location, and edge computing is where all the cool kids are at, at the fringes in the thick of the action.

Security cameras, phones, machine sensors, thermostats, cars and televisions are just a few of the items used daily that create data which can be mined and analyzed. On top of that, data created at retail stores, manufacturing plants, financial institutions, oil and gas drilling platforms, pipelines and processing plants, and it’s not hard to understand that the deluge of streaming and Industrial Internet of Things (IIoT) sensor data will very quickly overwhelm today’s traditional data analytics tools.

Enter Edge computing which consists of micro data centers or even small, purpose-built high-performance data analytics machines in remote offices and locations in order to gain real-time insights from the data collected, or to promote data thinning at the edge, by dramatically reducing the amount of data that needs to be transmitted to a central data center.

Without having to move unnecessary data to a central data center, analytics at the edge drastically speed up analysis while also cutting cost.

How is Edge Computing different from Cloud Computing and does Edge replace Cloud?

Cloud computing, requires that all things be connected to the central data storage, where huge volumes of information are processed to find optimization solutions or make business decisions.

Edge computing exists at the edges of data sources, e.g. devices (industrial machines like turbines, magnetic resonance systems, self-driving cars, smart homes, and other smart devices envisaging incorporating many sensors and operating with their data). In essence it is a special computing infrastructure  that pushes centralized nodes to the network extremes.

The difference is tabled below:

What this means for organisations

Centralized infrastructures work for analyses that rely on static or historical data, it is critical for many of today’s organizations to have fast and actionable insight by correlating newly obtained information with legacy information in order to gain and maintain a strong competitive advantage.

The proliferation of IoT sensors and other streaming data is driving organizations to use edge computing to provide the real-time analytics that impact the bottom line, or in some cases, stop a disaster from happening before it starts.

In addition, through caching and compressing of localized data at the edge of the mobile network, as near as possible to the end user location, Edge Computing accelerates and improves the performance of cloud computing for mobile users over Mobile Networks application.

Use Case Scenarios

In its race into the mainstream, the cloud revolutionized the way companies deal with data. The next wave of that revolution will happen at the edge, where the amount of data, the complexity of applications, and access demands are driving requirements for lower latency options.

The jawdropping number of connected devices and services coming online means that workloads are becoming highly interactive and transactional, while depending on and generating, a staggering amount of data.

The cloud alone simply isn’t suitable for many of these applications. Constantly ingesting IoT data to the public cloud and making it readily available to users is complex, expensive, and creates performance lags that certain apps just can’t afford. For these applications, the edge is a much better option. Keeping this data closer to its users – at the edge – eliminates many of the problems inherent with the public cloud model. The combination of edge computing and the public cloud creates an excellent opportunity for enterprises to bring more data to the cloud while still meeting performance and access goals.

Edge computing architecture is ideally suited for a number of situations. This includes poor connectivity of IoT devices, wherein IoT devices lack seamless connectivity to a central cloud. Further, factors such as high latency, low spectral efficiency, and non-adaptive machine type of communication are some serious challenges of cloud computing architecture that are leading to a shift to edge computing framework.

The economic benefits offered by Edge computing are many because scrutinizing and processing data close to the edge of the network helps organizations analyze crucial data in real-time. It is particularly useful for organizations across a number of industry verticals such as manufacturing, healthcare, finance, and telecommunications among others.

In Industrial Internet of Things (IIoT) applications such as power production, smart traffic lights or manufacturing, the edge devices capture streaming data that can be used to prevent a part from failing, reroute traffic, optimize production, and prevent product defects;  a retail store that is using a beacon to push in-store incentives to a mobile app.

IIoT,  autonomy & latency

IoT has been lauded with requiring little or no human interaction. The concept of connected devices interacting with each other, rather than humans, is only gaining momentum. Take the ubiquitous example of self-driving cars. As cars move through a city, for example, they will be required to interact with each other quickly and seamlessly.

With the number of sensors collecting data growing, data volume is set to continue growing exponentially. Moving data analytics to the edge with a platform that can analyze batch and streaming data simultaneously enables organizations to speed and simplify analytics to get the insights they need, right where they need them.

Ingesting data to a distant cloud and performing compute tasks there isn’t a realistic option. The data needs to reside closely to the devices generating the data. This ensures analysis can happen instantly and with high performance. With 5G’s promise of absolute interconnectivity, it’s no small wonder that infrastructure and technology of edge computing will continue to improve

Add to that, a lot of data, from multiple endpoints, that need to be analyzed and acted upon rapidly require a lot of bandwidth to transfer all that data.

Take for example, brick-and-mortar stores looking for any competitive advantage they can get over web-based retailers, and near-instant edge analytics — where sales data, images, coupons used, traffic patterns, and videos are created – provides unprecedented insights into consumer behavior. This intelligence can help retailers better target merchandise, sales, and promotions and help redesign store layouts and product placement to improve the customer experience. (One way this is accomplished is through use of edge devices such as beacons, which can collect information such as transaction history from a customer’s smartphone, then target promotions and sales items as customers walk through the store.)

One close to my heart - data analyses at financial institutions are to find and stop non-compliant transactions. When organizations have to take the time to move data back to the central data center or upload it to a cloud-computing architecture for processing and analysis, the lag time decreases the value of the data. For instance using micro data centers in financial institution branches enables analytics to happen in real-time, meaning that non-compliant transactions are caught and stopped much more quickly, which can have a real and positive impact on the bottom line.

Wearables at The Edge

At its heart, a Fitbit or heart monitor equivalent is Edge computing at its most basic form - the devices receive and analyse data without needing to connect to the cloud very much, more importantly, edge computing has the infrastructure to enable data processing as close as possible to the source, reducing latency which is where 5G networks come in with great processing power and greater speeds.

From the mining industry that will use wearables to monitor employee fatigue to Alcon measuring glucose levels in tears of diabetes patients and sending data to devices; wearables and Edge Computing have just begun their fruitful partnership. IDC estimates that spending on IoT [devices, software, services] will surpass the $1 trillion mark in 2020. Toshiba’s dynaEdge DE-100 device combines with AR100 Viewer smart glasses which allows information to be delivered to the glasses while keeping the hands free for other work.

Using the internet to ingest this data to the cloud just isn’t an option for these applications. For applications that need to move a lot of data quickly, this makes keeping data at the edge a much better fit, as uploads and downloads happen equally quickly.

Keeping data analytics at the edge does require a shift in corporate thinking, but the benefits far outweigh the transition. The cost savings by scaling back central data analytics infrastructures to handle non-time sensitive analysis while installing cost-efficient platforms purpose-built for edge analytics can have a real impact on an organization’s budget. Additionally, avoiding latency and getting near-instant insights is the brass ring of data analytics, and eliminating the time needed to transport data to and from the edge is a major step toward achieving that. Frankly I’m very excited to see the next generation of wearables and how this will change our lives.

With growing and increasingly dispersed sources of information and the pace of organizational change accelerating rapidly, the ability to simultaneously and in real-time analyze historical data with social, IoT sensor and other streaming data is invaluable.