Dec 22..

Connected devices like wearables highlight the business challenges faced in rolling out the Internet of Things (IoT). This is Part I of a three part story.

http://chipdesignmag.com/sld/blog/2014/01/21/business-models-and-divisions-challenge-connected-world/


Will Tu, ARM (earlier comments): …developers are also doing more contextual awareness and predictive analysis. By contextual, I mean that a smart phone turns on when it’s being held next to my head. Predictive refers to what I’ll do next, i.e., having the software anticipate my next actions. Algorithms enable those capabilities.

Tu: Right now there is an explosion of sensor companies, but there will be a consolidation down the road. The question one should ask is if integration key to the sensor and IoT space. I don’t know.  As a company, ARM would like to see a microcontroller (MCU) next to every sensor or sensor cluster – whether it is directly integrated to the sensor array or not. This is where scalability is important. Processing will need to be distributed; low power processing near the sensor with higher performance processing in the cloud.  It is very difficult to put a high-powered fan based system in a sensor. It just won’t happen. You have to be very low power near the sensor.

The FFE


Word association game.... more contextual awareness and predictive analysis.   QUIKs new algo team moving right along.

 predictive analysis- track along here. A tiered intelligence with another layer above the FFE.  You use what you have on your bench.  QUIK can integrate what they have into an SoC.  Revolutionary is what it will be.

Not only is the sensor node a very power constrained environment but it is also resource constrained, e.g., memory. That’s why embedded memory is critical – be it OTP or flash. In addition to low power, it is the cost of that memory is actually more influencing than the CPU.

Movea says...

1) What are the challenges of collecting and analyzing the data to find meaningful, actionable insights?

Soubeyrat: Data fusion is a critical enabling technology for pervasive context awareness on mobile devices. A wide variety of conditions could be detected:

• Device state: on the table; connected to a docking station; in hand; by an ear; in a backpack; in a pocket; in a purse; in a holster; in a shoulder bag
• User activity: standing, sitting, walking, or running; biking, riding, or skating; lying face up or down
• User environment: in a car, bus, train, or plane; in an elevator; going in/out the door

A good outline of QUIKs current algos for context?

The industry needs to come together to overcome several obstacles to enable pervasive applications and services for smarter devices. Among the challenges in developing a user-centric offering is an open framework supportive of data fusion, in which different players with very different skills can contribute to create smarter devices and apps. This open framework needs to multiplex many sources of data in vastly different formats across heterogeneous networks. The results can be tuned to new data types and new use cases, which is yet another challenge. Then one needs to accommodate different data rates, data synchronization, and data loss. Effective learning strategies need to be developed when no a priori knowledge exists about mappings from data to response. Finally, data and metadata representation standards need to be developed.