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Five requirements of a leading IoT edge platform

Enterprises and the public sector worldwide are looking for ways to increase security, improve productivity, provide higher levels of service and reduce maintenance costs. Many of them are using IoT technologies to improve their critical business processes or to drive innovation across their product lines. According to MachNation forecasts and the IoT Edge ScoreCard 2018, worldwide IoT application enablement revenue will be $1.8 billion in 2017 growing to $64.6 billion by 2026 at a compound annual growth rate of 49%.

According to our definitions, IoT edge computing is a technology architecture that brings certain computational and analytics capabilities near the point of data generation. IoT edge computing enables certain processes to occur in an optimal location to create more secure, reliable and scalable IoT deployments. An IoT deployment using edge computing takes advantage of connected IoT devices or gateways that offer functionality in areas such as device integration, data ingestion, data processing, analytics and device management.

Since the edge is critical to IoT success, leading IoT platform vendors must provide edge capabilities. In this post, I will discuss five required capabilities of edge platforms.

Five capabilities of IoT edge platforms

MachNation research shows that IoT edge platforms excel in five capabilities. Vendors that have a complete set of capabilities for addressing edge requirements offer extensive protocol support for data ingestion, robust capability for offline functionality, cloud-based orchestration capabilities to support device lifecycle management, hardware-agnostic scalable architecture and comprehensive analytics and visualization tools.

Extensive protocol support for data ingestion
Enterprise IoT systems need an edge platform that supports a wide ecosystem of devices and best-of-breed hardware vendors. Given the many verticals and use cases being transformed by IoT, we expect an extremely heterogeneous mix of devices that will be used to gather machine data and make it available to other IoT systems. In addition, there are at least several dozen well-accepted standards used in enterprise applications and a long list of proprietary ones that are being used in custom and off-the-shelf point products.

Leading IoT platforms must support an extensive mix of IoT devices that have myriad protocols for data ingestion. Platforms with a focus on edge provide a comprehensive set of protocols that can be used out-of-the-box. The list of protocols for industrial-minded edge platforms generally includes brownfield deployment staples such as OPC-UA, BACNET and MODBUS as well as more current ones such as ZeroMQ, Zigbee, BLE and Thread. Equally as important, the platform must be modular in its support for protocols, allowing customization of existing and development of new means of communicating with connected assets.

Finally, leading vendors provide encryption, authentication and data protection functionality to address elevated enterprise security requirements of connected mission-critical hardware. Retrofitting brownfield deployments to secure machine data at the source is a capability exclusive to leading IoT edge platforms.

Robust capability for offline functionality
Enterprise IoT systems need an edge platform with robust capabilities for offline functionality for resiliency, performance and reduction in operating costs. To save energy or minimize risks due to connectivity interruptions, IoT assets are not always connected to the cloud. It is becoming increasingly clear that most, if not all, enterprise IoT deployments will lean on edge processing technologies. The technologies make it possible to process a large amount of data generated by connected assets, adhere to low-latency requirements of industrial systems and meet established service-level agreements of mission-critical assets.

According to MachNation research, leading IoT edge platform vendors provide offline capabilities in three functional areas: data storage with normalization, event processing using rules and machine learning algorithms, and a set of edge-based integrations with local enterprise systems.

First, edge systems need to offer two types of data normalization and storage. They must offer these services to (a) successfully clean noisy sensor data and (b) support intermittent, unreliable or limited connectivity between the edge and the cloud. Providing both makes the overall system more reliable and cost-effective.

Second, a flexible event processing engine at the edge makes it possible to generate insight from machine data. By analyzing this data with machine learning tools, enterprises can identify behaviors that are valuable to solutions including predictive maintenance and cybersecurity. In addition, by applying a set of rules to this data, enterprises can automatically send fault alerts to identify troubles in real time.

Third, an IoT edge platform should integrate with local systems to optimize existing operational processes. Enterprise locations including manufacturing facilities, warehouses, oil refineries and remote field sites have many local systems including ERP, MES, inventory management and supply chain management. A leading IoT edge platform will provide edge-based integration with these types of existing operational systems to help ensure business continuity and access to real-time machine data.

Cloud-based orchestration capabilities to support device lifecycle management
Enterprise IoT systems need an edge platform with cloud-based orchestration capabilities to provide a centralized set of management and oversight functions supporting connected devices. An often overlooked yet critical aspect of distributed IoT platforms is their ability to manage and orchestrate newly deployed technologies and processes associated with connected devices. In order to harness the true value of IoT, an IoT platform has to provide a set of centralized, efficient and scalable tools for orchestrating the edge- and cloud-based requirements of connected assets.

The cloud-based orchestration provided by IoT platforms addresses provisioning, monitoring and updating requirements of connected assets. First, to simplify on-site deployment and add a level of security, a platform should provide factory provisioning capabilities for IoT devices. These API-based interactions allow a device to be preloaded with certificates, keys, edge applications and an initial configuration before it is shipped to the customer. This greatly reduces the amount of on-site work and troubleshooting that will be required to get the device online. Second, once the device is deployed and operational, the platform should monitor the device using a stream of machine and operational data that can be selectively synced with cloud instances. Third, using over-the-air update capabilities, the IoT platform should securely push updates to the edge. This includes updates for edge applications, the platform itself, the gateway OS, device drivers and also updates for devices that are connected to the gateway. This allows virtually all aspects of a device’s lifecycle to be managed centrally and gives the enterprise complete control over a locally, nationally or globally distributed IoT deployment.

Hardware-agnostic scalable architecture
Enterprise IoT systems need an edge platform with a hardware-agnostic scalable architecture to support a heterogeneous mix of deployed devices at scale. Today, most enterprise information technology environments are made up of heterogeneous assets from different makers, each with a unique set of capabilities. IoT deployments are no different. Actual IoT deployments use equipment from several vendors. And over time, systems tend to amass a mix of components with each subsequent launch.

IoT platforms that provide leading edge capabilities are capable running on a wide range of gateways and specialized devices. IoT hardware is powered by chips that use ARM-, x86-, and MIPS-based architectures. Using containerization technologies and native cross-compilation, the platforms offer a hardware-agnostic approach that makes it possible to deploy the same set of functionality across a varied set of IoT hardware without modifications. This improves performance and reduces the technology and labor costs of maintaining multiple versions of production software and hardware.

In addition, visionary platform vendors employ the same software stack at the edge and in the cloud, allowing a seamless allocation of resources and ensuring that edge-based operations are not limited by cloud-based tools. Platforms that are capable of shifting resources between the edge and cloud are better suited at meeting anticipated and unexpected application demands. This makes the overall system more scalable by improving resiliency and operational efficiency.

5. Comprehensive analytics and visualization tools
Enterprise IoT systems need an edge platform with comprehensive analytics and actionable visualization tools to deliver insight to a diverse group of stakeholders. The most valuable element of an IoT system is the insight that it generates for the enterprise, but distilling that insight from copious amounts of machine data is extremely difficult. Due to resource, latency and bandwidth constraints, a lot of the data generated at the edge must be processed and analyzed at the point of generation. IoT platforms that fully support the edge with analytics and visualization tools will enable an enterprise to analyze data, generate insights and provide actionable visualizations for end users.

IoT platforms with leading edge capabilities will offer an open and modular approach to edge analytics. Out-of-the-box edge platforms can aggregate data and run common statistical analyses. For capabilities that require specialized analytics, the platforms should make it easy to integrate leading analytics toolsets and use them to supplement or replace built-in functionality. Then, leading IoT platform vendors will enable edge data to be visualized and actioned on a set of mobile-ready customizable and interactive dashboards suitable for different end users. This makes it possible for a truck operator and a fleet manager to access interactive dashboards that deliver a combination of useful information and relevant controls for each of their respective roles. For development of other types of bespoke presentation layers, customers should be able to select their own best-of-breed visualization or application provider.

Conclusion

Enterprises and the public sector worldwide are looking for ways to increase security, improve productivity, provide higher levels of service and reduce maintenance costs. Yet, enterprises face many challenges when choosing to deploy an IoT technology. These challenges can impact overall IoT deployment costs and timing. So many enterprises are using IoT edge platforms to improve their critical business process while overcoming these deployment challenges.

Enterprises should select leading IoT edge platforms that have five key capabilities — extensive protocol support for data ingestion, robust capability for offline functionality, cloud-based orchestration capabilities to support device lifecycle management, hardware-agnostic scalable architecture and comprehensive analytics and visualization tools. Platforms that meet these requirements will simplify the short-term deployment experience while offering long-term flexibility as enterprises choose to innovate with new IoT services.

All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.

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