Networking

Edge Computing and IoT Go Hand in Hand

Moving compute and storage to the network edge can make a difference for businesses looking to tap the Internet of Things to improve operations and cut costs.

Juliet Van Wagenen

Juliet is the senior web editor for StateTech and HealthTech magazines. In her six years as a journalist she has covered everything from aerospace to indie music reviews — but she is unfailingly partial to covering technology.

The Internet of Things is coming to businesses everywhere and revolutionizing everything from manufacturing to retail.

But IoT devices have a huge impact on networks and will require new network infrastructures and devices to make them effective. Enter edge computing, which IDC describes as a “mesh network of micro data centers that process or store critical data locally and push all received data to a central data center or cloud storage repository, in a footprint of less than 100 square feet.”

This type of computing can breathe new life into IoT and analytics, offering all industries an opportunity to tap artificial intelligence and machine learning capabilities in real time to improve operations.

What Is Edge Computing?

So, what is edge computing exactly?

According to Microsoft, in edge computing, compute resources are “placed closer to information-generation sources to reduce network latency and bandwidth usage generally associated with cloud computing.” This helps to ensure continuity of services and operations even if cloud connections aren’t steady.

This moving of compute and storage to the “edge” of the network, away from the data center and closer to the user, cuts down the amount of time it takes to exchange messages compared with traditional centralized cloud computing. Moreover, according to research by IEEE, it can help to balance network traffic, extend the life of IoT devices and, ultimately, reduce “response times for real-time IoT applications.”

Edge Computing vs. Fog Computing

Edge computing is often mentioned in the same breath as “fog computing,” an architecture framework that determines how edge computing works. According to Cisco, which coined the term, fog computing allows businesses to bring cloud computing processes to the edge: “It facilitates the operation of compute, storage and networking services between end devices and cloud computing data centers.”

Essentially, fog computing helps to enable “repeatable structure in the edge computing concept,” which allows enterprises to scale performance accordingly by moving compute away from centralized locations or clouds.

The Potential for Edge Computing and IoT

IoT has already seen benefits from traditional cloud; namely, in that cloud can offer high computational capacity and vast amounts of storage. But edge computing takes these benefits one step further, by improving data transmission, storage and computation.

“IoT requires fast response rather than high computational capacity and large storage,” IEEE researchers note. “Edge computing offers a tolerable computational capacity, enough storage space, and fast response time to satisfy IoT application requirements.”

Edge computing, in turn, helps to:

Cut latency by bringing storage and compute closer to the user
Optimize bandwidth by controlling traffic flow
Preserve the energy capabilities of IoT devices by incorporating “a flexible task offloading scheme which considers the power resources of each device”
Reduce network overhead by aggregating and preprocessing “trivial packets”

There’s still much to be explored, however, and interest in improving how businesses can tap the technology is growing. Hewlett-Packard Enterprise recently announced the opening of a new innovation lab aimed at making the most of edge data for enterprises. And last June, Google released its Cloud IoT Edge platform, which “extends Google Cloud’s data processing and machine learning to edge devices,” according to Forbes.

Challenges in Embracing an Edge Computing Architecture

Organizations that embrace an edge computing architecture still have many challenges to overcome, however. Security is one, as IoT devices on a network likely come from various vendors, “making it difficult to deploy similar security schemes to ensure the same level of security.”

Businesses can take steps to safeguard devices against these security issues by practicing IoT security hygiene. Some IoT security best practices include:

Building security practices and tools into IoT deployments from the beginning, which includes budgeting for IoT security
Testing devices rigorously, both prior to deployment and with ongoing security tests
Protecting data during transit, when it’s most vulnerable
Managing security directly within the IT team by pushing out updates and patches remotely, as opposed to expecting end users to take charge

Moreover, managing the number of IoT-connected devices — from phones to cars — also proves a challenge.

“Considering the hundreds of the applications and millions of end users and devices, the management of edge computing for IoT will be exceptionally complicated,” IEEE researchers note.

A possible solution, according to the researchers, is to implement software-defined networking, which can help to effectively manage complexity while also cutting administration costs.

What Is Multiaccess Edge Computing?

Multiaccess edge computing (MEC), formerly known as mobile edge computing, is similar to edge computing in that it enables cloud computing capabilities at the network edge for a cellular network. The technology “promises to usher in an era of scalable and open programmability at the edge,” according to a recent white paper by Intel.

While MEC is mainly at play for telecommunications networks, it’s also important for businesses to understand how MEC will grow with the advent of 5G networking, which is ideal for IoT devices. The number of connected devices will inevitably grow as these next-generation networks arrive; this, in turn, will spur the need for MEC to help manage networking demands in an environment heavily populated by connected devices, according to a recent study.

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