By Johnny Fang, Product Manager and Patrick Bor, Product Manager, Moxa
Industrial automation suppliers need embedded computing platforms that are optimized for machine-to-machine (M2M) communications. The fundamental challenge in designing IoT clouds for industries involves negotiating the divide between IT and IA technologies.
Industrial automation protocols are fundamental to the proper functioning of the mass of devices connected to various fieldbuses, input/output configurations, serial interfaces and gateways. But for most IT engineers, the entire field appears esoteric and mysterious.
On the other hand, securing networks with firewalls and VPNs, protecting against dropped packets or node failures, and the multifarious problems introduced by wireless communications are typical challenges for IT, but ones which industrial automation (IA) engineers would rather avoid.
The biggest challenge today when building an industrial M2M network is its massively distributed nature: there is very little value gained by a gradual transition that takes years. IoT deployments must be rapid enough to sustain investment value, but the massive number and scope of the devices used makes rapidly completing the full installation pretty much impossible.
Specialized technicians are needed for every individual station. They must be competent electricians, as well as network designers, to juggle the details of multiple protocols, interfaces and communications media. Unfortunately, few IT professionals have ever worked with the protocols and interfaces that are common to industrial automation networks, and IT people have limited experience setting up input/output stations or configuring sensors.
Thus, effective software automation at the connectivity layer is critical: First, to help deploy devices that may ultimately include hundreds of thousands of nodes, and second, to flatten the learning curve for the men and women installing these devices. Software tools that transparently automate the rollout of industrial automation devices and simplify network deployments are important for anyone managing the deployment of an IoT network.
Industrial grade stresses, enterprise class challenges
Yet another way in which IoT networks differ from consumer networks is their strict availability and reliability requirements. Industrial M2M systems for intelligent transportation systems, or the smart grid, operate 24/7, 365 days a year. At any moment, the network must be able to call upon remote stations at the network’s edge and command them to make adjustments, return data or perform maintenance checks.
Obviously, devices that are not capable of reliably maintaining network connectivity for years on end will not be valuable to network administrators. Similarly, all devices along the network must be able to deliver key information for preventive maintenance, and to respond to a variety of common network challenges, such as failed nodes, network congestion and wireless re-association. For the most reliable performance, network redundancy, automated connectivity checks, preventive maintenance routines, and effective maintenance, monitoring and control protocols must be integrated as deeply into the hardware level as possible.
Finally, there are the related problems of data integrity and network security. Authorization, access and accounting controls are imperative for an M2M network; illicit access to a massively distributed industrial network by a hostile party has clear, potentially lethal consequences. IoT networks must support the strongest possible encryption and access controls. Similarly, accounting controls over the entire network are important not only for the monitoring and management of the network itself, but also to aid in preventive maintenance, as well as to perform forensic analysis on suspected intrusions or other security breaches.
Taken together, these imperatives amount to a lot of work that, until recently, had simply not yet reached a stage where M2M communication networks could be considered viable. Now, however, that has changed, and the IT/IA convergence enables these two technological realms in a secure, reliable, cost-effective manner that has become relatively easy to achieve.
Converging solutions for converging technologies
The overall technical challenge that must be addressed when building an industrial IoT network may be broken down into three key aspects:
• network topology (and how that relates to device deployment and engineering)
• deployment, setup and maintenance of network nodes and edge stations
• overall monitoring and control
At first glance, it might be tempting to simply divide the network into two layers of two dimensions: network/process and edge/core. That, however, would neglect some important opportunities for automation and optimization.
To begin with, the physical devices of the network are best broken up into three concentric layers: the core, the connectivity layer and the terminal edge stations, where nearly all of the remote process data and events will be generated. Edge devices will include sensors, automatic metering infrastructure, embedded computers for control and monitoring, and gateways to bind all of these devices together, to allow effective communications between the various parts. The question then becomes: How can automation and device engineering speed up and simplify M2M deployments, monitoring and management?
When viewed from this perspective, it is clear that when considering how best to optimize deployment efficiency, the main areas where industrial automation and controls can give the most value is at the connectivity and edge layers.
The setup and installation of devices that communicate over serial protocols and interfaces at the edge should be automated so that configuration tasks like addressing, tagging, and logic programming can be reduced to processes that set up hundreds of devices in seconds, without any need for a technician experienced in low-level programming languages.
Fieldbus protocols will need to be supported, centralized and administered from a central control station. In the ideal situation, a technician would only need to connect a device, test the interfaces to verify the communication links have been established, and the device automatically detected, configured and initialized by the remote control center.
Transparency for connectivity and communications
With effective engineering and design, computing platforms and network nodes in the connectivity layer may be reduced to a nearly transparent layer of automated deployment, monitoring and management. Embedded computing platforms can be engineered with a variety of communications interfaces, with software features that monitor and automatically respond to changes in the communications and connectivity layers.
Switches and routers should be remotely configurable and support a robust implementation of SNMP that can trigger automated alarms, regular service logs and detailed diagnostic reports. Cellular stations should be capable of automatic re-association whenever a link goes down, and all other wireless links—ZigBee, Bluetooth, 802.11, or sub-gigahertz—should feature analogous reliability and availability. With effective redundant ring backbones—whether over NPL, BPL or wired Ethernet—coupled with highly redundant, rapid-response wireless mesh topologies, a M2M network can virtually guarantee constant, highly available links with any associated edge station.
In M2M networks, the central control, monitoring and management solutions will generally be custom packages built and maintained either by the end user, or by a contracted system integrator.
If designed with suitably intuitive controls that have integrated the connectivity- and edge-layer optimizations, deployments of networking and edge devices could be reduced to a simple process: the remote technician plugs it in, the central control sees it light up, and then with a few quick clicks of a mouse, the control center can set up tagging, addressing, logic configuration, and other automated maintenance and management processes.
Flexibility that simplifies development and adaptation
To guarantee that the network remains customizable and flexible, open platforms should be used wherever prudent. Linux/GNU and other open source approaches provide an excellent platform for IoT integration, and may reliably power both RISC and x86 platforms. These software systems offer strong security (for both data integrity and AAA protocols) while providing a wide-open system that allows customization, optimization and feature development on any subsystem process, no matter how low- or high-level it may be.
Linux/GNU systems also offer two additional advantages: strong security in the form of packet filtering, firewalls, VPNs and the strongest RSA encryption available, along with enabling end users to escape proprietary lock-in. Thus, system integrators and end users alike benefit when using open source/free software solutions like Debian.
Software optimizations are not, however, the only consideration. The physical devices that make up the IoT network must also be specifically engineered for customizability, security, reliability and deployment flexibility. For embedded computers, features like a modular design allow end-users to adapt devices to specific roles within the network, or even to repurpose a device being used in an obsolete role. A variety of communications modules must be available, as well: ZigBee, Ethernet/IP, 802.11, cellular and fiber.
5 principles to guide the engineering of an IoT platform
Taking all of these observations into account, a clear vision emerges of what kinds of embedded computing platforms should be sought out when building an IoT system.
1. IoT networking devices should conform to the strictest standards of flexibility, reliability and security, starting with the physical hardware and then moving on up through every networking layer, right into user-space.
2. Software optimizations that automate configuration, setup, and the overall deployment of embedded computers and other edge devices are critical components of an effective IoT architecture.
3. All elements of an M2M networking platform should be easily integrated into high-level, custom IoT implementations to aid administration, maintenance and management of the network. At the highest central administrative layer, smart grid IoT solutions will share little in common with intelligent traffic systems, while solar farm solutions will be distinct from both. IoT networking platforms must not intrude on the work of building the final solution envisaged by the customer, but should assist in achieving that goal in every possible way.
4. An IoT networking platform must make the connectivity layer as transparent as possible, effectively turning the intermediate portion of the network between the edge and the core into a black box, with which system integrators and application engineers never need concern themselves.
5. Communications between the edge and the core must reliably process all data, regardless of the health of the network as it is accumulated. This means asynchronous, encrypted transmissions between the edge and core, with strong fail safes to guarantee the physical integrity of the data.
Any computing platform or networking device intended for use in IoT deployments should be engineered from a system-wide perspective, where each device is viewed as part of a mutually supportive interlocking whole, rather than as a single, one-off networking tool.
By way of example…
As IA engineers know, when forced to work at the lowest programming layer the configuration of remote I/O gateways (for automated alarms and event-triggers) is laborious. Similarly, setup of fieldbus devices or wireless addressing and failovers can be equally toilsome. This is where pushing automated features out to the very edge of the network is useful and can deliver a cost-efficient system. In this way, masses of edge devices may be efficiently and rapidly configured with a dramatically reduced need for user input.
Moxa software is an example of how platforms with integrated, automated setup and administration utilities put the work of configuration and maintenance into the background of system design and deployment:
• MXconfig is a mass configuration tool that speeds up the configuration of 100 switches by a factor of ten.
• MXview uses SNMP to automatically discover, query and configure edge devices (including the setup of OPC 2.0 tags), and then automatically assembles the results into a visualization of the wired ring.
• Synmap, the virtualized process monitoring and control interface, also uses SNMP to serve as a universal control protocol that may be used to script any device that supports it, without any need for further compiling or low-level adjustments.
• Smart Recovery is an automated, BIOS-level system re-write utility that allows administrators to trigger remote rewrites of the entire software system or to configure remote devices for fully automated recoveries at either scheduled times or critical events. Because Smart Recovery operates at the BIOS level, it can restore a system that has become so corrupt it can no longer boot up.
• Moxa’s DA-Center automates the setup and administration of databases, easing the conversion and display of field data and simplifying connectivity setup with edge I/O, while Active OPC Server delivers asynchronous, event-driven push communications—from the edge to the core—for remote I/O devices, while enabling DHCP addressing on remote ioPAC units.
• For cellular wireless connections, OnCell Central Manager and Guaranlink ensure that cellular nodes with hidden addresses may be set up for direct communications with the core, while also providing network association fallbacks in the case of station failures.
When combined with modular computing platforms, like Moxa’s DA series of rackmount servers or the UC series of universal computers for embedded and edge solutions, these software enhancements allow secure, reliable, highly automated deployments of massively distributed networks like those now envisaged by smart grid solutions providers, traffic systems engineers or residential solar power providers.
These are early, strong steps towards creating a virtualized connectivity layer specifically engineered for industrial cloud solutions. The automation involved simplifies the deployment, setup and management of industrial networking and edge devices, while consolidating and simplifying their management at the central core. By calling upon tailored software solutions carefully integrated with key hardware optimizations in networking, I/O, and embedded computing platforms, industrial cloud engineers will be able to set aside the work of connectivity integration and low-level coding to concentrate on the work of developing the most effective system for their needs.
Filed Under: Design World articles, Networks • connectivity • fieldbuses