The Internet of Things is lurching forward into the coming year, like never before. Its growth is manifesting rapidly, exponentially, with an increasingly broadening array of use cases and applications influencing verticals well removed from its conventional patronage in the industrial internet.
With advances throughout the public and private sectors, its sway is extending beyond retail and supply chain management to encompass facets of delivery route optimization, financial services, healthcare and the marked expansion of the telecommunication industry in the form of connected cities and connected cars.
An onset of technological approaches, some novel, some refined, will emerge in the coming year to facilitate the analytics and security functionality necessary to solidify the IoTâs impact across the data sphere with a unique blend of big data, cloud, cognitive computing and processing advancements for customized applications of this expressivity of IT.
The result will be a personalization of business opportunities and consumer services veering ever closer to laymen users.
Speed of Thought Analytics
The interminable sensor generation and streaming of data foundational to the IoT warrants a heightened analytic productivity facilitated in a variety of ways. Surmounting the typical schema constraints germane to the relational world can involve semantic technologies with naturally evolving models to accommodate time-sensitive data. Other techniques involve file formats capable of deriving schema on the fly. âSelf-describing formats is the umbrella,â MapR Senior Vice President of Data and Applications Jack Norris reflected. âThere are different types of files that kind of fall into that, such as JSON and Avro.â Still other approaches involve General Processing Units (GPUs), which have emerged as a preferable alternative to conventional Central Processing Units (CPUs) to enable what Kinetica VP of Global Solution Engineering Eric Mizell referred to as answering questions at âthe speed of thoughtââin which organizations are not limited by schema and indexing designs for the number, speed, and type of questions provisioned by analytics in real-time.
According to Mizell, GPUs are âpurpose-built for repetitive tasks at parallel with thousands of cores for aggregation, mathematics, and those type of thingsâ whereas CPUS are better for discreet, sequential operations. Analytics platformsâparticularly those designed for the IoTâleveraging GPUs are not bound by schema and rigid indexing to allow for multiple questions equitable to the speed at which data is generated, especially when augmented with visualization mechanisms illustrating fluctuating data states. âYou can ask questions of the data without having to have predetermined questions and find answers in human readable time,â Mizell explained. âWeâre getting tremendous response from customers able to load hundreds of millions and billions of rows [and] include them in interactive time. It transforms what business can do.â These capabilities are integral to the expansion of the IoT in the telecommunications industry, as âConnected cities and connected cars are huge with a lot of the telcos,â according to Mizell.
The best means of deriving value from the IoT actually transcends analytics and necessitates creating action between connected machines. Effecting such action in real-time will increasingly come to rely on the various forms of artificial intelligence pervading throughout modern enterprises, which is most readily accessible with options for machine learning and deep learning. Furthermore, Forbes contends AI is steadily moving to the cloud, which is instrumental in making these capabilities available to all organizationsânot just those armed with a slew of data scientists. Regarding the options for libraries of deep learning and machine learning algorithms available to organizations today, Mizell remarked, âWeâre exposing those libraries for consumers to use on analytics and streaming. On the data streaming end weâll be able to execute those libraries on demand to make decisions in real-time.â The most cogent use case for machine-to-machine interaction involving the IoT pertains to connected cars, autonomous vehicles, and some of the more cutting edge applications for race car drivers. These vehicles are able to account for the requisite action necessary in such time-sensitive applications by leveraging GPU-facilitated AI in real time. âFor autonomous cars, the Tesla has a bank of GPUs in the trunk,â Mizell commented. âThatâs how itâs able to read the road in real-time.â
Back from the Edge
Another substantial trend to impact the IoT in the coming year is the evolving nature of the cloud as it relates to remote streaming and sensor data applications. Cloud developments in previous years were focused on the need for edge computing. The coming year will likely see a greater emphasis on hybrid models combining the decentralized paradigm with the traditional centralized one. In circumstances in which organizations have real-time, remote data sources on a national scale, âYou canât respond to it fast enough if youâre piping it all the way down to your data center,â Mizell said. âYouâll have a mix of hybrid but the aggregation will come local. The rest will become global.â One of the best use cases for such hybrid cloud models for the IoT comes from the U.S. Postal Service, which Mizell mentioned is utilizing the IoT to track mail carriers, optimize their routes, and increase overall efficiency. This use case is similar to deployments in retail in which route optimization is ascertained for supply chain management and the procurement of resources. Still, the most prominent development affecting the IoTâs cloud developments could be that âall of the cloud vendors are now providing GPUs,â Mizell said. âThatâs very new this year. Youâve got all the big three with a bank of GPUs at the ready.â This development is aligned with the broadening AI capabilities found in the cloud.
Software Defined Security
Implementing IoT action and analytics in a secure environment could very well represent the central issue of the viability of this technology to the enterprise. Numerous developments in security are taking place to reduce the number and magnitude of attacks on the IoT. One of the means of protecting both endpoint devices and the centralized networks upon which they are based is to utilize software defined networking, which is enjoying a resurgence of sorts due to IoT security concerns. The core of the software defined networking approach is the intelligent provisioning of resources on demand for the various concerns of a particular network. In some instances this capability includes dedicating resources for bandwidth and trafficking, in others it directly applies to security. In the latter instance the network can create routes for various devicesâon-the-flyâto either connect or disconnect devices to centralized frameworks according to security protocols. âDevices are popping up left and right,â Mizell acknowledged. âIf itâs an unknown device shut it down. Even if it has a username and a password, donât give it access.â Some of the applications of the IoT certainly warrant such security measures, including financial industry forays into the realm of digital banking in which mobile devices function as ATM machines allowing users to withdraw funds from their phones and have cash delivered to them. âThatâs what they say is in the works,â Mizell observed.
Security measures for the IoT are exacerbated by the involvement of endpoint devices, which typically have much less robust security than centralized frameworks do. Moreover, such devices can actually perpetuate attacks in the IoT to wreak havoc on centralized mainframes. Strengthening device security can now take the form of endpoint device registration and authorization. According to Mizell: âThereâs a notion of device registration, whether itâs on the network or not. If [you] can bring your phone or whatever device to work, it detects the device by its signature, and then says it only has access to the internet. So you start locking devices into a certain channel.â Blockchain technologies can also prove influential in securing the IoT. These technologies have natural encryption techniques that are useful for this purpose. Moreover, they also utilize a decentralized framework in which the validity of an action or addendum to the blockchain (which could pertain to IoT devices in this case) is determined by effecting a consensus among those involved in it. This decentralized, consensus-based authorization could prove valuable for protecting the IoT from attacks.
As the use cases for the IoT become more and more surprising, it is perhaps reassuring to realize that the technologies enabling them are becoming more dependable. Accessing the cognitive computing capabilities to implement machine-based action and swift analytics via the cloud is within the grasp of most organizations. The plethora of security options can strengthen IoT networks, helping to justify their investments. Hybrid cloud models use the best of both worlds for instantaneous action as well as data aggregation. Thus, the advantages of the continuous connectivity and data generation of this technology are showing significant signs of democratization.