REVIEW: 6 enterprise-scale IoT platforms

Here are the building blocks of successful enterprise Internet of Things deployment, plus details about AWS IoT and IoT 1-Click, Cisco Jasper, Azure IoT, IBM Watson IoT and Google Cloud IoT Core.

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There's little need to tell anyone in IT that the Internet of Things (IoT) is a big deal and that it's growing insanely fast; BI Intelligence estimates that there will be some 23.3 billion IoT devices by 2019. As IoT support becomes more of an enterprise concern, there are four key issues about enterprise IoT (EIoT) deployments to consider:

  1. The sheer number of enterprise IoT endpoint devices – There will be 1 billion by 2019.
  2. The frequency of data generated IoT devices – IDC estimates that by 2025, an average connected person anywhere in the world will interact with connected devices nearly 4,800 times per day or one interaction every 18 seconds.
  3. The incredible volume of IoT data – Of the 163 zettabytes (that's 1021bytes) of data that will be created in 2025, IDC estimates that 60% will be from IoT endpoints and half of that (roughly 49 zettabytes) will be stored in enterprise data centers.
  4. The challenges of maintaining security for your device constellation – IDC estimates that by 2025, 45% of the stored enterprise data will be sensitive enough to require being secured but will not be.

Now, if you're an early adopter, you may have already built your own IoT infrastructure and your endpoint-device deployment may well be underway. You'll have discovered that the do-it-yourself approach is non-trivial in the enterprise IoT world. On the other hand, if you're about to enter the IoT world and you'd rather not build a homegrown solution, then what are your platform choices? To make your research easier, we have complied a collection of six of the leading enterprise IoT platforms that can not only do the job but do so at enterprise scale.

gibbs iot image 11 Mark Gibbs

Generalized internet of things architecture

Building blocks of enterprise IoT

But before we get to the offerings, let's look at the building blocks of enterprise IoT. First, consider the type or types of endpoint devices you'll be using. There are now literally thousands of off-the-shelf products that can be used for prototyping, tracking vehicles and products, monitoring environmental conditions, controlling equipment, or reporting on the status of all sorts of things. You name it, there's probably a device waiting for you to use and, as the majority of platforms are device agnostic, there's not much to constrain your choice of EIoT platform. That said, a few platforms were designed to only support specific devices and in some cases that support is only for a single type of device (for example, see the Amazon AWS IoT Button).

Alternatively, you might have to "roll your own" and build IoT functionality into your existing processes and products. When it comes to endpoint hardware there are literally hundreds of single-board computers now available (such as Arduino, Raspberry Pi and SolidRun's HummingBoard-Gate) or you could choose any other device with more smarts than an electric toothbrush. For communications, you can use an industry standard protocol such as MQTT, HTTP/HTTPS, AMQP, XMPP, DDS, and CoAP (see the sidebar "Communications Protocols and Technologies"), and you'll have no trouble finding a compatible EIoT platform. Alternatively, if your endpoint devices use protocols or media that aren't supported, you can use IoT gateways to convert from whatever protocol and media the devices use to enable communication with an EIoT platform.

Fog computing and IoT

Next comes an optional layer of intermediate services, often called fog computing, that sits between the endpoint devices and the cloud (this is a major marketing battle zone so you'll find a lot of alternative names used for this layer). In the fog-computing layer, functions such as data aggregation, filtering and processing can be used to optimize communications bandwidth. For example, only sending critical data or summaries to improve communications performance to reduce data costs or reducing what would be a cloud-processing load such as machine learning or performing statistical analyses.

Examples of fog computing products include Cisco's Fog Data Services and Microsoft's Azure IoT Edge, although the latter focuses on moving the filtering and processing functions onto the device itself which really makes it an additional endpoint-device layer rather than what is generally considered to be fog computing. Whether you need fog computing will depend on the endpoint device choices you make and the communications limitations in the deployment environment.

It's worth pointing out that in IoT situations that require real-time or near-real-time interactions – for example, closing a valve when a liquid level is reached – you either have to have devices smart enough to respond and control the event with little or no delay or rely on fog computing to handle the sensor data and issue a control signal to an actuator.

Typically, the latency from endpoint devices to fog-computing services will be in the range of milliseconds to seconds. Compare that delay to a latency in the range of seconds to minutes connecting devices or fog computing services to an EIoT platform and you can see that fog computing has an important role in real-world environments.

IoT gateways

Another service you'll find in fog computing systems is IoT gateways. One of the most common uses of IoT gateways is to bridge from endpoint devices that, for example, connect over Wi-Fi to another medium such as an Ethernet network or cellular service. Gateways can also translate between protocols. For example, they might convert HTTP requests or a custom protocol from endpoint devices to MQTT messages.

Hosted IoT platforms

Finally, we reach the enterprise IoT platform core in the cloud. This will be a hosted Platform as a Service (PaaS) providing a publish-subscribe (pub-sub) or request-response (RR) message-brokering system. Which messaging pattern you'll use will depend on the available protocols given your devices and their constraints and performance. (For a serious discussion on the two communication patterns, see the paper, A Communication Model to Integrate the Request-Response and the Publish-Subscribe Paradigms into Ubiquitous Systems.)

IoT digital twins

A feature many EIoT platforms offer is virtual endpoint devices. A variety of names are used for these virtual devices; for example, AWS IoT Core calls them "device shadows" while Microsoft calls them "twins." Virtual endpoint devices are documents or database records that allow other devices to interact with the last known or predicted future state of the actual device even when it isn't connected. So, for example, if an endpoint device is setting the temperature of a water bath to 200 degrees but the device has to disconnect for some reason and might not reconnect until after the water bath has reached the target temperature, the virtual version can be set to show the target temperature and target time that it will be reached. Other devices that have to interact with the water bath can now read the device future target state without the measuring device being online.

Enterprise IoT security

There's one more critically important aspect of IoT architectures that applies to all layers: security. Given the problems and liability issues that unsecured IoT products have caused such as the massive 2016 Distributed Denial of Service (DDoS) attack on the DNS service management company, Dyn, Inc., robust security is absolutely required and all of the EIoT solutions we cover here have serious security baked in.

So, which enterprise IoT platform will meet your needs best? The bottom line is that successful enterprise IoT solutions require careful engineering starting with the endpoint devices, through the fog computing layer to the cloud and finally, to the EIoT platform. Then, when the data are captured by the EIoT platform, how they're handled, processed, and stored once they get there will determine the data's manageability and actionability and, therefore, the cost and value of your EIoT system.   

Amazon AWS IoT

gibbs iot image 03 AWS

AWS IoT Core dashboard

At the heart of Amazon's IoT offerings is the AWS IoT Core, built around a publish/subscribe message-broker service that supports HTTP 1.1, WebSockets and MQTT version 3.1.1 with X.509 mutual authentication, encryption and granular privileges. The broker is front-ended by the Device Gateway, which manages active device connections and handles protocol semantics. Amazon claims the gateway can scale automatically to support over one billion devices. Alongside these features is the Registry, which assigns a unique identity for each device and tracks device metadata such as attributes and qualities, and, optionally, virtual devices (device shadows).

AWS IoT is hardware agnostic so the AWS IoT SDKs provide tools to support pretty much any endpoint devices you might want to use. There are SDKs for Android, Arduino Yún, embedded C and C++, iOS, Java, JavaScript and Python, and they include open-source libraries, developer guides, sample code and porting guides.

The final feature that completes the AWS IoT Core is the Rules Engine that parses inbound messages, transforms them as required and, based on rules you define, routes them to a service or device. The services include all of the Amazon AWS service endpoints including AWS Lambda (cloud code hosting), Amazon Kinesis (real-time big-data handling and analysis), Amazon S3 (massive-scale object storage), Amazon Machine Learning, Amazon DynamoDB (a NoSQL database service), Amazon CloudWatch (AWS cloud monitoring), Amazon Elasticsearch Service with built-in Kibana visualization and Amazon Simple Notification Service. With this collection of tools and services you can build IoT systems at global scale.

AWS IoT 1-Click

Amazon Web Services also offers a unique IoT platfo

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