IoT Applications Are Headed for Edge

Edge computing is a counter-trend that decentralizes cloud services and allocates them to various sites that are situated closer to end-users or data sources. It enables applications to provide a better quality experience, thereby also allowing new use cases and acquiring operational efficiencies.

The concept of edge computing has been mainly utilized to outline everything from actions performed by IoT devices to datacenter-like infrastructure.

By processing data near to the data source, Edge Computing can assist to minimize network bandwidth needed to move device data to back-end systems. This article focuses on edge computing from the angle of IoT application developers. After all, it will be the applications, leveraging evolving technologies such as artificial intelligence, IoT and machine learning (AI/ML) that will deliver insights to reveal opportunities to provide new services or optimize costs.

As per the study from the International Data Corporation (IDC), 45 % of all data generated by IoT devices will be restored, processed, monitored and acted upon close to or at the edge of a network by 2020.

Arising use cases such as an IoT, AR/VR, robotics and telco network functions are frequently specified as key drivers to move computing to the edge. Service providers can utilize a completely new class of services at the network edge to take benefit from their proximity to the customers. Even cloud service providers have realized the requirement for processing data closer to the source and are providing edge solutions.

Edge Applications

Although basic infrastructure plays an important role, the advantages of edge computing will be recognized on the backs of applications. Various edge applications can empower new experiences across several industries:

Industry 4.0

It allows real-time analytics with AI/ML abilities at the factory floor to empower predictive maintenance enhancing equipment utilization.

IoT in Healthcare

Advance patient care by combining live real-time data from a patient’s fitness tracker, medical equipment and environmental conditions.

Grid Edge Control and Analytics

Edge grid computing technologies are allowing utilities with advanced real-time analyzing and analytics abilities, generating useful and valuable insights on distributed energy-generating resources such as renewable.

Smart Infrastructure

Empower cities to support real-time data from roadside sensors as well as cameras to increase traffic flow, improve safety such as a dynamic speed limit, loading/unloading of cargo ships.

Autonomous Vehicles

Real-time decisions to safely traverse the vehicle across a various range of driving conditions. Edge and distributed computing techniques improve safety, space-based awareness and compatibility with current-generation hardware.

Far Edge Services

Service providers are utilizing proximity to customers to provide low latency, high bandwidth location-based services for various use cases like AR/VR, or VDI (virtual desktop).

Edge Computing: Best Practices

Edge computing provides companies the flexibility and coherence of cloud computing for a distributed pool of resources across various locations. To develop embedded applications, the IoT Application developer required a thorough interpretation of hardware and interfaces. Deeply customized operating system with a vigorous dependency on fundamental hardware required functional specialization. Edge computing contains some of the following best practices:

Compatible tooling

Developers required being capable to utilize the same tools, regardless of the location the application is placed at. One of the examples of such tooling, Red Hat CodeReady Workspaces, generated on Eclipse Che, gives Kubernetes-native development solution with in-browser IDE for quick application development that can be simply deployed at edge or cloud.

Open APIs

Definite and open APIs enable real-time data to be acquired in a programmatic manner allowing businesses to provide new classes of services. Developers require APIs to generate standards-based solutions that can gain data without worrying about the fundamental hardware interfaces.

Accelerated Application Development

Although edge architectures are still developing, design decisions made today will have a long-lasting influence on future abilities. Rather than acquiring offerings purpose-built for the edge that minimizes developer quickness, a better perspective includes solutions that can work anywhere – cloud, on-premise and edge.

Containerization

Most new applications are being designed as containers as they’re better to deploy and handle at scale. Edge application requirements contain modularity, isolation and invariability, making use of containers an especially good fit. Applications will require to be deployed on various edge tiers, each with their unique resource characteristics. Integrated with microservices, containers representing functions can be scaled up or down based on underlying resources or conditions.

It’s essential to consider that it’s not going to be an either/or choice between edge computing and centralized computing. As edge computing gets larger adoption in the marketplace, the overall solution would often enclose an integration of the two. In such a hybrid computing model, centralized computing would be utilized for compute-intensive workloads, data accumulation and storage, AI/machine learning, coordinating operations across geographies and traditional back-end processing. Open source is a clear choice that gives the flexibility of choice and future-proofing investments in edge computing.

The physical benefit from the proximity of edge a device enhances real-time data analytics and reduces barriers-of-entry for on-premise hardware utilized in real-time applications such as machine learning, IoT and AR/VR. Using a small hardware footprint, edge computing reacts as a high-performance bridge to the cloud, which more organizations are depending on.