A version of this article originally appeared as IoT cloud services market spurs products from cloud giants on SearchCloudApplications at Techtarget.
IoT Developers Can Learn From Mobile Apps and Exploit Cloud Services for IoT Backends
The past year has seen IoT evolve from IT buzzword to strategic business imperative as a steady drumbeat of big business projects and vendor product announcements have legitimized the concept of connected devices. IoT was one of 2015’s top trend predictions that technology analysts got right, although it was a phenomenon with significant momentum. There are about 10 billion connected devices now in use and various forecasts project that number to double or even quintuple by 2020. This translates into at least a billion dollars in annual revenue for companies active in the IoT industry with a total economic impact rivaling that of the German economy by 2025. Even should these estimates prove wildly optimistic, companies and IT developers can’t ignore business applications that promise new sources of revenue, higher customer satisfaction and greater efficiency by incorporating intelligent, connected devices into products, services and business processes.
Consumer products like wearables, connected appliances and smart home controllers have generated most of the IoT buzz, but its more important, profit and revenue enhancing applications come from adding sensors, intelligence and connectivity to equipment. The combination of smart sensors, cheap, battery-powered processors and storage and ubiquitous wireless networks yields a bonanza of new information that can be transformed into business insight.
Indeed, ‘things’ are only half of the IoT the story since device ‘intelligence’ is a relative term: they only collect and distribute data about local conditions with the ability to process the data. Thus IoT is equally a big data problem since the whole point of connecting intelligent devices is to gather and share data, information that once aggregated and analyzed can spot trends, detect problems, flag anomalies and modify actions. Yet IoT isn’t your typical big data system since it involves thousands, if not millions of data sources scattered across myriad remote networks that combined can generate enormous amounts of data.
Cisco estimates that connected devices will create 507.5 zetabyes (1 billion terabytes) of data per year by 2019. Although most of this raw data, like machine telemetry or device logs, will never make it to a data center, it still implies gigabytes, if not terabytes per year per device flowing into some sort of IoT analysis system. The question is where? What can handle IoT data volumes, from millions of connections, where the data flow can be highly variable and episodic, and process the data into useful information? Hyperscale cloud services are a natural fit.
Cloud and IoT: Central Intelligence for Distributed Data
We agree with IDC’s forecast that within five years, “more than 90% of all IoT data will be hosted on service provider platforms as cloud computing reduces the complexity of supporting IoT ‘Data Blending’.” IDC also projects that “the growing importance of analytics in IoT services will ensure that hyperscale data centers are a major component of most IoT service offerings;” that is, IoT will fuel cloud growth.
We already have an example from the smartphone world. Mobile app developers needing backend processing, data aggregation and state management for millions, if not billions (in the case of Facebook) of connected clients, recognize the value of cloud backends and have fueled the rise of MBaaS (mobile backend-as-a-service) products. IoT is following a similar path, although this time cloud providers are ahead of the developers. The last few months have seen a spate of IoT service announcements from the cloud giants as each seeks to build an ecosystem that can win developers and capture the fast-growing market for enterprise IoT projects. GE was prominently featured at October’s reInvent conference discussing its use of AWS to replace traditional data center workloads and as an IoT data processing engine.
AWS and Other Cloud Majors Fighting for Business
That cloud services have gotten IoT religion is evidenced by recent product introductions. AWS launched an ambitious IoT service to manage intelligent things (connected devices and physical objects) that includes an object abstraction layer (Shadows), object registry, message brokers, message rules engine that can trigger other AWS services. Not to be outdone, and in a preemptive strike the week before, Microsoft released an IoT Suite that like Amazon’s is designed to capture, integrate, analyze and report the information from myriad devices with the cloud acting as the focal point for data aggregation and processing. Google also has an IoT message for its cloud services, however it’s not a cohesive product and requires a DIY approach stitching together existing services like Big Query, Cloud Pub/Sub (message bus), Firebase (MBaaS) to a streaming data backend.
Besides handling IoT data analysis, the other key requirement cloud IoT services must address is security. Here cloud services are ideal due to their proven ability to scale and if there’s one thing IoT requires, with millions of devices and terabytes of data, it’s scale. It’s a multifaceted problem that includes: device and user authentication, security credentials management, incident detection, alerting and auditing and even threat prevention and mitigation. A promising strategy uses the cloud backend as a security hub/controller to control connections and enforce policies for IoT device communication. Microsoft implements what it calls service assisted communication through the Azure IoT hub, however the AWS IoT security model takes a similar approach.
Intelligent devices generating reams of data are coming whether enteprises want them or not. Industrial and IT products will increasingly provide much richer telemetry about their state of operation, usage and anomalies, however without an IoT data collection and analysis strategy, organizations will end up wasting it. We offer the following suggestions.
- Investigate the IoT features of existing data center, manufacturing and facilities equipment and select a few areas in which better understanding of operating conditions might eliminate service calls, prevent or mitigate equipment problems or provide deeper understanding of user behavior.
organizations developing hardware products should make IoT data collection and communication a part of the design. Look at reference architectures from component manufacturers like Intel, Marvell, MediaTek and others.
- Exploit cloud services for the IoT data aggregation and processing backend. Although AWS and Azure are leading the way and have beta services available today, others are sure to follow.
- Build the IoT software architecture on three pillars:
- data streaming, collection and management
- big data analysis
- security, looking at the full spectrum of authentication, credential and monitoring features
IoT is still a new and dynamic field meaning projects must start small, adapt and iterate quickly and include user and business unit feedback early and often since the goal is improved operations, greater efficiency and new sources of revenue. Look for problems that could be easily fixed with better information, but that don’t require a major new hardware design (unless of course, you’re in the hardware business and starting a new design cycle). Using cloud serivces eliminates a major roadblock, namely backend infrastructure deployment and management, from the project and will reduce the time between idea and results.