Plug-And-Play Sensors… Are Easier To Use And Are More Secure


Q. Any exciting imaging products coming up in the IoT space?
A. Cameras! These have the entire analytics engine built into them, with occupancy detection and open application program interfaces to enable developers to build on top of them.
Sensor integration is a trend, but what is rising up now are plug-and-play sensors that are easier to use and are more secure. These also support over-the-air/networks upgrades. This makes development pipeline easier and also lets a piece of hardware in the field to be upgraded over time. Overall, it signifies a shift of firms from being based on capital expenditure towards being based on operating expenditure since these can continue to add value after a system has been deployed.

Q. Please give an application example along with a chip that can be used to implement it.
A. One example use case would be a security alarm that can detect whether the object is a human or an animal. We have a family of processors known as the Blackfin line, which are signal processors with an architecture designed for low-power digital signal processing. Such features are enabled by contextual analysis of images.

Q. What are some interesting engineering trends in this space?
A. A big trend is that the nature of our customer base is changing; software engineers are making decisions on what hardware to chose. Hardware systems are integrated to single chips and they are outsourcing hardware development to ADI. This shows that the current IoT decision-makers are more software-centred due to the data science elements involved.

Q. What challenges do you face while building tools for software engineers?
A. Challenges with data science are due to the fact that it tends to be a very specialised field. So when we build platforms for the IoT, the questions being asked are on how to make data science easier, or how to enable sophisticated data science tools for software developers.

Q. What is getting engineers to bring more devices to the IoT paradigm?
A. The ease of doing data science at the cloud is an enabler. We are seeing that many firms are moving analytics to the edge. We also see a need to enable customers to more quickly build and deploy their systems solutions.



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