Consider a conference that is simultaneously transmitted to audiences in three cities. When a presenter asks the audience a survey question, they can respond by simply raising their hands. The total vote is tallied across all three cities and displayed to the presenter and the audience in real time.
Actually, that futuristic set-up became reality recently at Freescale. We did that, in part, to illustrate the potential of the Internet of Things (IoT). Here’s how it worked. Each audience member is fitted with a wristband embedded with motion sensors. Sensor data from the wristband captures the movement of the audience member’s wrist. Contextually-aware algorithms running on the wristband interpret sensor data and look for data patterns that suggest vertical displacements congruent to a user raising his hand. When such a signature movement is present, the wristband transmits its data to a wireless access point situated at that conference location. The wireless access points time-stamp the data they receive and then forward the information expeditiously to a cloud-based application which uses the results from the wristbands in all three conference locations to deduce when the presenter is taking a vote and the results.
Click to enlarge
This example illustrates many of the architecture challenges for the IoT. It has been estimated that IoT-connected devices, like the wristband given in our example, will dwarf all connectivity by 2020, including human-to-human, human-to-machine and machine-to-machine connections. At the heart of an IoT-connected device are one or more sensors. Because sensors provide data continuously and autonomously, sensor data can quickly exceed human-generated data in volume. To alleviate data congestion and associated transmission costs, smart sensors can make a real-time determination on the salience or relevance of the data and transmit them only when they are deemed potentially useful by upstream applications. A more sophisticated contextual algorithm may be able to differentiate between the wearer raising his hand and other actions such as standing up. Placing intelligence at the data source can reduce the communications bandwidth consumed by sensor data and prolong the battery lives of battery-powered wireless sensor nodes. Intelligence at the data source is also critical where security is a concern. This is particularly sensitive in body-worn sensors that can record signals which may seem meaningless to the individual. However, when these signals are combined with other information in a data-mining algorithm, they can unintentionally breach consumer privacy.
Today, Freescale serves over 150 unique sensor applications per year. We see sensors integrating more intelligent functions and we see the need to more closely integrate our sensors with MCU and digital networking offerings as systems solutions. Our observations reflect the need for more layered intelligence across our product families to address power conservation, security and connectivity concerns. With the coming waves of IoT applications, we believe sensor systems will become more complex, more context and environmentally aware, and, fortunately for all of us who are working with them, more interesting.
Come see the newest developments in Freescale sensors at the Sensor Expo and Conference at the Donald E Stephens Convention Center, Rosemont, IL from June 24-26, 2014.
Ian Chen manages marketing, systems architecture, software and algorithm development for Freescale’s Sensor Solution Division. He held senior business, marketing and engineering leadership positions at Sensor Platforms, Mobius Microsystems, Analogix Semiconductor, Cypress Semiconductor, IC Works, National Semiconductor and Texas Instruments. Ian received bachelor and master’s degrees in electrical engineering and an MBA, all from the University of Illinois at Urbana-Champaign. He holds more than ten patents.