Technology Trends Around IoT
Technology Trends Around IoT
Data and analytics are getting bigger and bigger.
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To gather insights for DZone's 2016 Internet of Things (IoT) Research Guide, scheduled for release in June, 2016, we spoke to 18 executives, from 16 companies, who develop IoT devices or help clients do so.
Here's who we talked to:
Craig McNeil, Managing Director of IoT, Accenture | Prathap Dendi, General Manager, AppDynamics | Aaron Lint, Vice President of Research, Arxan | Suraj Kumar, General Manager, Digital as a Service, Axway | Rod McLane and Justin Ruiz, Marketing, Ayla Networks | Paul Hanson, CEO, bbotx, Inc. | Mikko Jarva, CTO, Comptel | Brad Bush, COO, and Jeanette Cajide, VP of Corporate Development, Dialexa | Scott Hilton, Executive Vice President Products, Dyn | Anders Wallgren, CTO, Electric Cloud | Mathieu Baissac, Vice President Product Manager, Flexera | Darren Guccione, CEO, Keeper Security | Tony Paine, CEO, Kepware | Johan den Haan, CTO, Mendix | Joan Wrabetz, CTO, Quali | Tom Hunt, CEO, Windspring
Here’s what they told us when we asked, “What technologies are you using or considering around IoT?”
- More are being used in industrial than consumer where M2M was already in place.
- APIs and standards. Information from smart or dumb assets aggregating data over standard APIs. How to take dumb devices, add sensor technology, make smart and collect data to a central location wirelessly via IoT gateways (e.g. ZigBee, Bluetooth, et al).
- More devices and more data, that’s why we compress, encode and convert data.
- There’s a ton of fragmentation and a lot of coalescing. OSC is becoming popular to look at from the standards perspective. Platforms are curling up from 181 18 months ago around GE Predix, AWS, and Microsoft Azure. Google will be a player as might PTC. In network connectivity I see SigFox as the leading proprietary network in the cellular range. Simply buy a license to use their platform. They’re connected with sensor manufacturers that connect with their network so inexpensive devices like thermostats and smoke alarms can connect with batteries that have lives of 10 years. It brings brownfield situations to life. They’re building a global network. 3GPP who governs standards are trying to catch up. Sensors are not coalescing yet.
- Wearables, video surveillance camera networks. The sensors aren’t all providing text data. You have structured and unstructured data with the potential for data to be even more diverse.
- Vertical specific: automotive (buses, cars), small medical devices, home devices. You never know what’s under the hood until you take a look. Optimize for power and speed based on the intent of the device.
- Smart home.
- From the chip perspective, the ARM infrastructure is low energy, small footprint. It started with mobile. Quite a bit around Microsoft Azure and AWS with regards to IoT adoption.
- There is a need for high frequency, high speed data stores. These data stores will handle big data to allow for seamless extraction to run analytics – storage and persistent. Writing better algorithms. New computing resource code. Know how to scale at the lowest cost.
- Intelligent compression of IoT data.
- APIs will be everywhere. Everything will be connected.
- Two sides: 1) the device itself with client code, firmware, encryption and APIs; 2) server-side which provides greater exposure for security issues. The browser model with the ability to control users is more discreet and direct. Interaction between client and server. There are a lot of ways to detect breeches and intrusions. Risk assessment platforms are not the best model. We’ll see updates coming from data scientists in the next year as part of the modeling process.
- For manufacturers the value is in the data. Now there’s more focus on data analytics from collection, to analysis, to predicting the future. Analytics technology will expand.
- It depends on the individual verticals. Smart infrastructure is using MQTT, homes are using C. How to keep data storage costs low and secure when in transit and while sitting still (persistent). Everyone wants the data. The elegance of the data is critical. Take the exceptions and not all of the data to keep the size manageable. It’s where the magic is happening.
What technologies are you seeing used most often in conjunction with IoT?
Opinions expressed by DZone contributors are their own.