Fog Computing: Bringing cloud vapors to the IOT fields

Sensor data creates needs for local analytics that fog computing serves

By Kishore Jethanandani

Fog computing has arrived as a distinct class of customized solutions catering to local analytical needs in physical ecologies that constitute the Internet of Things. Sensors stream vast volumes of data, from field sites like wind farms, expeditiously processed only in the vicinity and their actionable intelligence is intuitive to local decision-makers. Cloud analytics, by contrast, delays data flows and their processing far too long and loses its value.

Bringing Analytics close to sensor data

The configuration and customization of fog computing solutions address a heterogeneous mix of speed, size, and intelligence needs. An illustrative case is of Siemens’ gas turbines that have five thousand embedded sensors, in each of them, which pour data for storage in databases. Data aggregated locally helps to compare performance across gas turbines, and this is done at the moment as sensors stream live data that can be analyzed to act instantaneously.

An entirely different situation is intelligent traffic lights that sense the beams of the light of incoming ambulances and clear the way for them while alerting other vehicles ahead to reroute or slow down before choking the traffic. In this case, the data analysis spans a region.

Time is of the tactical essence with the users of information generated by sensors and connected devices. A typical case of an application is the extent of use of high-value assets such as jet engines; a breakdown could have a spiral effect on the scheduling of flights. Sensors generate data every second or milliseconds that need to be captured and analyzed to predict equipment failures at the moment.  The volumes of data are inevitably massively large and delays in processing intolerable. Fog analytics slashes the time delays that are inescapable with cloud computing, by parsing the data locally.

Users have expressed keen interest in gathering and analyzing data generated by sensors but have reservations about the technology and its ability to serve their needs. A study completed by Dimensional Research in March 2015 found that eighty-six percent reported that faster and more flexible analytics would increase their return on investment. The lack of conviction about analytics technologies is palpable by the fact that eighty-three percent of the respondents are collecting data but only eight percent capture and analyze data in time to make critical decisions.

The value of data

We spoke to Syed Hoda, Chief Marketing Officer, of ParStream, a company that offers an analytics database and a platform for real-time analytics, on data volumes as large as Petabytes of IoT data, to understand how new breakthroughs in technology help to extract value from it.

ParStream’s technology helps companies gain efficiencies from IoT data which is event specific. The productivity of wind turbines, as measured by electricity generated, is higher when their velocity is proportionate to the speed of flow of winds which is possible when their blades do not buck the wind direction. “By analyzing data, at once, companies can get better at generating actionable insights, and receive faster answers to what-if questions to take advantage of more opportunities to increase productivity,” Syed Hoda told us.

ParStream slashes the time of data processing by edge processing, at the gateway level, rather than aggregate it centrally. It stores and analyzes data on wind turbines, for example, at the wind farm. Numerical calculation routines, embedded in local databases, process arrays of live streams of data, instead of individual tables, to flexibly adjust to computation needs.

Unstructured data and quality of service

We spoke to three experts, who preferred to remain anonymous, employed by a leading company in fog computing about the state of the technical and commercial viability of IOT data-based analytics. They do not believe that impromptu learning from streaming data flowing from devices in the Internet of Things. In their view, the IOT affords only preconfigured inquiries such as comparing the current data to historical experience for purposes such as distinguishing an employee from intruders.

In their view, analytics, in local regions, encompass applications that need unstructured data such as image data for face recognition which are usable with the consistent quality of service. In a shared environment of the Internet of Things, a diversity of demands on a network are potentially detrimental to service quality. They believe that new processors afford the opportunity to dedicate the processing of individual streams of data to specific cores to achieve the desired quality of service.

Fog computing applications have become user-friendly, as devices with intuitive controls for functions like admitting visitors or to oversee an elevator are more widely available. The three experts confirmed that solutions for several verticals have been tested and found to be financially and operationally workable and ready for deployment.

Edge Intelligence

Another approach to edge intelligence is using Java virtual machines and applets, for intelligence gathering, and for executing controls. We spoke to Kenneth Lowe, Device Integration Manager, at Gemalto’s SensorLogic Platform about using edge intelligence for critical applications like regulating the temperature in a data center. “Edge intelligence sends out an alert when the temperature rises above a threshold that is potentially damaging to the machines while allowing you to take action locally and initiate cooling, or in the worst case, shut the system down without waiting for a response from the cloud,” Kenneth Lowe told us. “The SensorLogic Agent, a device-agnostic software element, is compiled into the Java applet that resides on the M2M module itself.  As sensors detect an event, the Agent decides on how to respond, process the data locally, or send it to the cloud for an aggregated view,” Kenneth Lowe explained.

Java virtual machines help to bring analytics from the cloud to the edge, not only to gateways but all the way to devices. We spoke to Steve Stover, Senior Director of Product Management at Predixion Software, which deploys analytic models on devices, gateways and in the Cloud. The distribution of analytics intelligence to devices and gateways helps to function in small or large footprints and disconnected or connected communication environments.

“We can optimize wind turbine performance in real time by performing predictive analytics on data from multiple sensors embedded on the individual turbine in a wind farm,” Steve Stover told us. “Orderly shutdowns prompted by predictive analytics running on the gateway at the edge of the wind farm helps to avoid costly failures that could have a cascading effect,” Steve Stover told us.

Similarly, analytics on the cloud can compare the performance of wind farms across regions for purposes of deciding investment levels in regional clusters of wind farms.

Fog computing expands the spectrum of analytics market opportunities by addressing the needs of varied sizes of footprints. The geographical context, the use cases, and the dimensions of applications are more differentiated with fog computing.

Previously published by All Analytics of UBM TecWeb

MEMS transform Mobile Devices

Mobile device sales have tapered in developed markets. To regenerate demand, vendors are turning to tiny, low-powered MEMS (Micro-electro-mechanical systems) devices that enable novel services on smaller energy efficient devices.

Outdoor navigation was one of the first successful mobile applications. MEMS will make indoor navigation possible.  GPS enabled navigation systems are not capable of sensing altitudes whether indoors or outdoors. Inside multi-storied buildings, people could be on any of the floors and they can’t be tracked unless their phones sense their altitude. Samsung has added in its Galaxy 3 and 4 models pressure sensors just for this purpose according to a report by IEEE.

For indoor navigation, pressure sensors work in conjunction with other sensors like the gyroscope and accelerometer that have been available in mobile phones but have barely been used. Individually, these sensors stream only one type of data feed—not enough to spawn very many applications. The accelerometer, for example, senses motion but cannot read direction or orientation.

French company, Movea, has developed, MotionCore, an API for Android ad Windows 8 devices that allows phones to connect with multiple sensors of any make according to another report of IEEE. Once the GPS signal is lost inside the building, users can easily lose their way inside a cavernous commercial building. With sensors, the phone will ask the user to enter data of his or her height. The accelerometer senses motion and calculates from the steps taken the distance covered. The gyroscope reads changes in direction and the pressure sensor the height. The combined data can be represented on the building map in order to direct the user to a meeting room or any other destination within the building.

Samsung’s Galaxy 4 is chockfull of sensors suggesting plans for a varied mixed of services not yet disclosed to the public. According to a report by Analog EE Times , the Samsung Galaxy 4 includes an accelerometer, RGB light, geomagnetic, proximity, gyroscope, barometer, gesture and even temperature and humidity varieties.

Projection of visual content is another area where MEMS has transformed mobile devices. Despite the richness of the visual content on mobile device, their users are stuck with a tiny screen as the only viewing option. Pico projectors, offered by companies like Mezmeriz, are tiny versions of room projectors. They can be embedded into mobile phones like Samsung’s Galaxy Beam and display visual content on any surface or even as a holographic image.  The MEMS device is a collection of microscopic mirrors, all part of a chipset that projects, a pixel at a time, the image.  The MEMS devices, by reducing the power consumption and the heat generation, are able to offer image quality of room projectors with a tiny fraction of their size.

MEMS devices overcome the limits of size, battery life and excessive heat of mobile devices and raise their utility with new services that will sustain and increase interest in mobile devices well into the future. They make mobile devices far more aware of their environment and translate the data into services that come in handy in the daily rhythm of life.


Note: This post was earlier contributed to the now defunct “The Mobility Hub” published by UBM Techweb.