Partnerships that could shape the internet of things for years are being forged just as enterprises fit IoT into their long-term plans. Representation of an IoT & IIoT Convergence
As a vast majority of organisations have included #IoT as part of their strategic plans for the next two to three years. No single vendor can meet the diverse #IoT needs of all customers, so they’re joining forces and also trying to foster broader ecosystems. General Electric and Bosch did both recently announced their intention to do the same.
The two companies, both big players in #IIoT, said they will establish a core IoT software stack based on open-source software. They plan to integrate parts of GE’s #Predix operating system with the #Bosch IoT Suite in ways that will make complementary software services from each available on the other.
The work will take place in several existing open-source projects under the #Eclipse Foundation. These projects are creating code for things like messaging, user authentication, access control and device descriptions. Through the Eclipse projects, other vendors also will be able to create software services that are compatible with Predix and Bosch IoT Suite, said Greg Petroff, executive director of platform evangelism at GE Software.
If enterprises can draw on a broader set of software components that work together, they may look into doing things with IoT that they would not have considered otherwise, he said. These could include linking IoT data to ERP or changing their business model from one-time sales to subscriptions.
GE and Bosch will keep the core parts of Predix and IoT Suite unique and closed, Petroff said. In the case of Predix, for example, that includes security components. The open-source IoT stack will handle fundamental functions like messaging and how to connect to IoT data.
Partnerships and open-source software both are playing important roles in how IoT takes shape amid expectations of rapid growth in demand that vendors want to be able to serve. Recently, IBM joined with Cisco Systems to make elements of its Watson analytics available on Cisco IoT edge computing devices. Many of the common tools and specifications designed to make different IoT devices work together are being developed in an open-source context.
DCNS's Scorpene Data Leak and Future of Indian Submarine Fleet
The startup today introduced an issue tracking feature for its fast-growing code hosting platform that promises to help development teams organize the features, enhancements and other items on their to-do lists more effectively.
The GitLab Issue Board is a sleek graphical panel that provides the ability to display tasks as digital note cards and sort them into neat columns each representing a different part of the application lifecycle. By default, new panels start only with a “Backlog” section for items still in the queue and a “Done” list that shows completed tasks, but users are able to easily add more tabs if necessary. GitLab says that the tool makes it possible to break up a view into as many as 10 different segments if need be, which should be enough for even the most complex software projects.
An enterprise development team working on an internal client-server service, for instance, could create separate sections to hold backend tasks, issues related to the workload’s desktop client and user experience bugs. Users with such crowded boards can also take advantage of GitLab’s built in tagging mechanism to label each item with color-coded tags denoting its purpose. The feature not only helps quickly make sense of the cards on a given board but also makes it easier to find specific items in the process. When an engineer wants to check if there are new bug fix requests concerning their part of a project, they can simply filter the board view based on the appropriate tags.
Google this week has published a new version of its TensorFlow machine learning software that adds support for iOS. Google initially teased that it was working on iOS support for TensorFlow last November, but said it was unable to give a timeline. An early version of TensorFlow version 0.9 was released yesterday on GitHub, however, and it brings iOS support.
For those unfamiliar, TensorFlow is Google’s incredibly powerful artificial intelligence software that powers many of Google’s services and initiatives, including AlphaGo. Google describes TensorFlow as “neural network” software that processes data in a way that’s similar how our brain cells process data (via CNET).
With Google adding iOS support to TensorFlow, apps will be able to integrate the smarter neural network capabilities into their apps, ultimately making them considerably smarter and capable.
At this point, it’s unclear when the final version of TensorFlow 0.9 will be released, but the early pre-release version is available now on GitHub. In the release notes, Google points out that because TensorFlow is now open source, 46 people from outside the company contributed to TensorFlow version 0.9.
In addition to adding support for iOS, TensorFlow 0.9 adds a handful of other new features and improvements, as well as plenty of smaller bug fixes and performance enhancements. You can read the full change log below and access TensorFlow on GitHub.
Major Features and Improvements
Python 3.5 support and binaries
Added iOS support
Added support for processing on GPUs on MacOS
Added makefile for better cross-platform build support (C API only)
fp16 support for many ops
Higher level functionality in contrib.{layers,losses,metrics,learn}
More features to Tensorboard
Improved support for string embedding and sparse features
TensorBoard now has an Audio Dashboard, with associated audio summaries.
Big Fixes and Other Changes
Turned on CuDNN Autotune.
Added support for using third-party Python optimization algorithms (contrib.opt).
Google Cloud Storage filesystem support.
HDF5 support
Add support for 3d convolutions and pooling.
Update gRPC release to 0.14.
Eigen version upgrade.
Switch to eigen thread pool
tf.nn.moments() now accepts a shift argument. Shifting by a good estimate of the mean improves numerical stability. Also changes the behavior of the shift argument to tf.nn.sufficient_statistics().
Performance improvements
Many bugfixes
Many documentation fixes
TensorBoard fixes: graphs with only one data point, Nan values, reload button and auto-reload, tooltips in scalar charts, run filtering, stable colors
Tensorboard graph visualizer now supports run metadata. Clicking on nodes while viewing a stats for a particular run will show runtime statistics, such as memory or compute usage. Unused nodes will be faded out.
India successfully launched the first technology demonstrator of indigenously made Reusable Launch Vehicle (RLV), capable of launching satellites into orbit around earth and then re-enter the atmosphere, from Sriharikota near Chennai.
A booster rocket with the RLV-TD lifted up at 7 a.m. from the Satish Dhawan Space Centre, and the launch vehicle separated from it at an altitude of 50 km.
The RLV-TD or winged space plane then climed to another 20 km and began its descent. It re-entered to earth’s atmosphere at an hypersonic speed of more than 5 Mach and touched down the Bay of Bengal between Chennai and the Andaman archipelago. Known as hypersonic flight experiment, it was about 10 minutes mission from liftoff to splashdown.
An ISRO spokesman said the mission was accomplished successfully. “Everything went according to the projections” he said adding that the winged space plane will not be recovered from the sea.This successful experiment of the ISRO is only a very preliminary step towards developing reusable launch vehicles. Several flights of RLV-TD will have to be undertaken before it really becomes a reusable launch system to put satellites into orbit.
This project has been in the design board in one form or other for the past 10 years. It was called AVATAR, SLE and some other names. This is in-fact the first attempt to boost it out of the drawing board and into the launch platform. It will need at-least a dozen successful launches, each validating a multitude of technologies before the system can be put in production.
There are a multiple mission profiles for the proposed system. And it will infact bring down a significant portion of the launch cost down. Already the cost of launching a satellite or probe into LEO is way cheaper when using ISRO’s launch vehicle than say Ariane-space. But the cost shifts heavily in favour of the latter for a GTO. Which India herself uses often. There are several other technological benifits to the program, like Hypersonic flight profile study, Effective Heat Shielding, Autonomous Navigation and a lot more so called “Dual-USe” technologies can be spin-over.
I believe a hearty Congratulations are in order for the Project Team.
A Business Intelligence Strategy for Real-Time Analytics – RTInsights
In modern business, data flows in from a wide array of physical sensors and online user interactions in large volumes and at high speed. As a direct result of such speeds and volumes, the analysis of this data, as well as the data-driven decisions and actions, must be almost fully automated, with no more than rare exceptions being brought to human attention. The automobile example demonstrates clearly the ability of combined sensor and AI systems to recognize real-world situations and act accordingly.
This Article had in-depth analysis of the Strategy for BI. Worth your time.
Introduction
This series of How To or Case Study I am attempting to write was the result of the work of our team for the past 2+ years.We were developing a Predictive Analytics Platform for a global truck OEM. This was to be integrated with their live OBU data, Warranty, Research & Design, Customer Support, CRM and DMS among other things.
In this journey, we have attempted to solve the problem in incremental steps. Currently we are working on the predictive analytics with learning workflows. So, I believe Its time to pen down the experience with building the other 3 incremental solutions.
First Baby Step– Fast Data capture and Conventional Analytics –
Kafka, Redis, PostreSQL
Next Logical Step – Big Data capture, Warehousing and Conventional Analytics
Kafka, Storm/Spark, Hadoop/Hive, Zookeeper
The Bulls Eye – Real Time Analytics on Big-Data
Same as Above with Solr and Zeppelin
The Holy Grail – Predictive Analytics
Same as Above with MLib on Spark
Now, in this post I will write about “The First Baby Step”. This involves fast acquisition of data, Real-time analytics and long term data archival.
The disparate data sets and sources posed a significant complexity, not to mention the myriad polling frequencies, sync models and EOD jobs. It goes without saying that the #OEM had a significant investment in SAP infrastructure. We had studied multiple architecture models, (Some are available in this Reference Architecture Model from Horton Works and SAP)
The following are the considerations from the data perspective,
FastData – Realtime Telematics data from the OBU.
BigData – Diagonastics data from each truck had 40+ parameters and initial pilot of 7500 trucks.
Structured Data – Data from Dealer Management System and Customer Relationship Management System.
Transactional Data – Data from Warranty management and Customer Support systems.
Fast Data: Our primary challenge for the 1st phase of design/development was the scaling of the data acquisition system to collect data from thousands of nodes, each of which sent 40 sensor readings polled once per second and transmitted every 6 seconds once. While maintaining the ability to query the data in real time for event detection. While each data record was only ~300kb, our expected maximum sensor load indicated a collection rate of about 27 million records, or 22.5GB, per hour. However, our primary issue was not data size, but data rate. A large number of inserts had to happen each second, and we were unable to buffer inserts into batches or transactions without incurring a delay in the real-time data stream.
When designing network applications, one must consider the two canonical I/O bottlenecks: Network I/O, and Filesystem I/O. For our use case, we had little influence over network I/O speeds. We had no control over the locations where our truck sensors would be at any given time, or in the bandwidth or network infrastructure of said location (Our OBDs communicated using GPRS on GSM Network). With network latency as a known variant, we focused on addressing the bottleneck we could control: Filesystem I/O. For the immediate collection problem, this means we evaluated databases to insert the data into as it was collected. While we initially attempted to collect the data in a relational database (PostgreSQL), we soon discovered that while PostgreSQL could potentially handle the number of inserts per second, it was unable to respond to read queries simultaneously. Simply put, we were unable to read data while we were collecting it, preventing us from doing any real-time analysis (or any analysis at all, for that matter, unless we stopped data collection).
The easiest way to avoid slowdowns due to disk operations is to avoid the disk altogether, we mitigated this by leveraging Redis, an open-source in-memory NoSQL datastore. Redis stores all data in RAM and in hybrid models in Flash storage (like an SSD) allowing lightning fast reads and writes. With Redis, we were easily able to insert all of our collected data as it was transmitted from the sensor nodes, and query the data simultaneously for event detection and analytics. In fact, were were also able to leverage Pub/Sub functionality on the same Redis server to publish notifications of detected events for transmission to event driven workers, without any performance issues.
In addition to speed, Redis features advanced data structures, including Lists, Sets, Hashes,Geospatials and Sorted Sets, rather than the somewhat limiting key/value pair consistent with many NoSQL stores. Available Data Structures in Redis Sorted Sets proved to be an excellent data structure to model timeseries data, by setting the score to the timestamp of a given datapoint. This automatically ordered our timeseries’, even when data was inserted out of order, and allowed querying by timestamp, timestamp range, or by “most recent #” of records (which is merely the last # values of the set).
Our use case requires us to archive our data for a period of time, enabling the business users to run a historical analytics along with data from the real-time source.
Enter Data Temperatures, Data Temperatures Hot Data – The data which is frequently accessed and is currently being polled/gathered. Warm Data – The data which is currently not being polled but still frequently used. Cold Data – The data that is in warehouse-mode, but still can be accessed for BI or analytics jobs with a bit of I/O Overhead.
Since Redis keeps all data in RAM that is the HOT Area, our Redis datastore was only able to hold as much data as the server had “Available RAM”. Our data, inserted at a rate of 27GB/hour, quickly outgrew this limitation. To scale this solution and archive our data for future analysis, we set up an automated migration script to push the oldest data in our Redis datastore to a PostgreSQL database with more storage scalability. As explained above, since Redis has native data types for Time Series data, it was a simple enough process for the Load operation.
The other consideration to be exercised is the “Available RAM”. As the amount of data that is queried, CPU cycles used and the RAM used for the Processing determines the amount of memory available for data stores. be reminded if the data-stores are fill to the brim your processing job is going to utulise the disk I/O. Which is very bad.
We wrote a REST API as an interface to our two datastores allowing client applications a unified query interface, without having to worry about which data-store a particular piece of data resided in. This web-service layer defined the standards for the time, range and parameters. Fast Data Architecture with Redis and Kafka
With the above represented architecture in place, generating automated event detection and real-time notifications was feasible, again through the use of Redis. Since Redis also offers Pub/Sub functionality, we were able to monitor incoming data in Redis using a small service, and push noteworthy events to a notification channel on the same Redis server, from which subscribed SMTP workers could send out notifications in real-time. This can even be channeled to an MQ/ESB or any Asynchronous mechanism to initiate actions or reactions.
Our experiences show Kafka and Redis to be a powerful tool for Big Data applications, specifically for high-throughput data collection. The benefits of Kafka as a collection mechanism, coupled with inmemory data storage using Redis and data migration to a deep analytics platform, such as relational databases or even Hadoop’s HDFS, yields a powerful and versatile architecture suitable for many Big Data applications.
After we have implemented HDFS and Spark in Phase 2-3 of this roadmap, we have of-course configured redis in the said role. Hope I have covered enough of the 1st step in our Big-Data journey. Will write an article per week regarding the other 3 phases we have implemented successfully.
Discovery that could make Quantum Computers Practically viable.
A major stumbling block that have kept quantum computers to the realms of Science Fiction is the fact that “quantum bits” also called as “Qubits” and the building blocks with which they’re made are prone to magnetic disturbances. These “noise” can interfere with the work qubits do, but on Wednesday, scientists announced a new discovery that could possibly help solve the problem.
They made this possible by tapping the same principle that allows atomic clocks to stay accurate. Researchers at Florida State University’s National High Magnetic Field Laboratory (MagLab) have found a way to give qubits the equivalent of a pair of noise-canceling headphones.
The approach relies on what are known as atomic clock transitions. Working with carefully designed tungsten oxide molecules that contained a single magnetic holmium ion, the MagLab team was able to keep a holmium qubit working coherently for 8.4 microseconds -– potentially long enough for it to perform useful computational tasks.
By offering exponential performance gains, quantum computers could have enormous implications for cryptography and computational chemistry, among many other fields.
MagLab’s new discovery could put all this potential within much closer reach, but don’t get too excited yet — a lot still has to happen. Next, researchers need to take the same or similar molecules and integrate them into devices that allow manipulation and read-out of an individual molecule.
MagLab’s new discovery could put all this potential within much closer reach, but don’t get too excited yet — a lot still has to happen. Next, researchers need to take the same or similar molecules and integrate them into devices that allow manipulation and read-out of an individual molecule, Stephen Hill, director of the MagLab’s Electron Magnetic Resonance Facility, said by email.
“The good news is that parallel work by other groups has demonstrated that one can do this, although with molecules that do not have clock transitions,” Hill said. “So it should be feasible to take the molecule we have studied and integrate it into a single-molecule device.”
After which, the next step will be coming up with schemes involving multiple qubits that make it possible to address them individually and to switch the coupling between them on and off so that quantum logic operations can be implemented, he said.
That’s still in the future, “but it is this same issue of scalability that researchers working on other potential qubit systems are currently facing,” he added.
Magnetic molecules hold particular promise there because the chemistry allows self-assembly into larger molecules or arrays on surfaces, Hill explained. Those, in turn, could form the basis for a working device.
Organisers of Brazil Protest use Analytics to Measure Attendance
Organizers of yesterday’s massive demonstration in São Paulo against the Brazilian government have employed an analytics tool to get accurate attendance data.
Opposition group Movimento Brasil Livre (MBL) was offered the technology by Israeli startup StoreSmarts for free through its Brazilian distributor SmartLok in exchange for the marketing exposure linked to the anti-government demo.
The technology used in the protest is all readily available and is in use for atleast 3 years now. Its is a combination of portable router and an application that is usually employed by retailers to monitor, analyze and provide insights on shopper behavior by detecting WiFi signals from mobile devices in a designated area.
In order to estimate the amount of people in any given area, the system only takes smartphones into account while ignoring other WiFi signals from devices such as laptops or routers. The calculations are carried out in real-time, so the system can also provide insight on its web dashboard into the peak hours of the protests.
By calculating the device’s receiver signal strength indication (RSSI), the system can also tell how long the smartphone – and therefore its owner – spent in the area that is being mapped. However, the system does not track or store data on individual users.
Typically, protest organizers in Brazil or their comrades across the world have to rely on data provided by the local authorities and large media organisations to get accurate insights on attendance. These media organisations themselves rely on local bodies. Those numbers are often believed to be inaccurate for political reasons – the StoreSmarts system suggests that 1.4 million people attended yesterday’s demonstration, a number that matches what has been provided by the local police.
When asked why it is interesting to provide the technology free of charge, the startup founder says that his Brazilian partner has been piloting StoreSmarts’ analytics tool with some retailers in São Paulo – so getting the extra attention is helpful.
“We believe in taking data driven decisions, whether it’s politics or retail. The exposure we get by supporting such requests is very important for us and our partner, as we see Brazil as a very important market,” Eliyahu says.
A joint European-Russian mission aiming to search for traces of life on Mars left Earth’s orbit Monday at the start of a seven-month unmanned journey to the Red Planet.
The Proton rocket carrying the Trace Gas Orbiter (TGO) to examine Mars’ atmosphere and a descent module that will conduct a test landing on its surface had been launched from the Russian-operated Baikonur cosmodrome in the Kazakh steppe at 0931 GMT on 14th March 2016.
The ExoMars 2016 mission, a collaboration between the ESA and its Russian equivalent Roscosmos, is the first part of a two-phase exploration aiming to answer questions about the existence of life on Earth’s neighbour.
The TGO will examine methane around Mars while the lander, Schiaparelli, will detach and descend to the surface of the fourth planet from the Sun.
The landing of the module on Mars is designed as a trial run ahead of the planned second stage of the mission in 2018 that will see the first European rover land on the surface to drill for signs of life, although problems with financing mean it could be delayed.
One key goal of the TGO is to analyse methane, a gas which on Earth is created in large part by living microbes, and traces of which were observed by previous Mars missions.
“TGO will be like a big nose in space,” said Jorge Vago, ExoMars project scientist.
Methane, ESA said, is normally destroyed by ultraviolet radiation within a few hundred years, which implied that in Mars’ case “it must still be produced today”.
TGO will analyse Mars’ methane in more detail than any previous mission, said ESA, in order to try to determine its likely origin.
One component of TGO, a neutron detector called FREND, can help provide improved mapping of potential water resources on Mars, amid growing evidence the planet once had as much if not more water than Earth.
A better insight into water on Mars could aid scientists’ understanding of how the Earth might cope in conditions of increased drought.
Schiaparelli, in turn, will spend several days measuring climatic conditions including seasonal dust storms on the Red Planet while serving as a test lander ahead of the rover’s anticipated arrival. The module takes its name from 19th century Italian astronomer Giovanni Schiaparelli whose discovery of “canals” on Mars caused people to believe, for a while, that there was intelligent life on our neighbouring planet.
The ExoMars spacecraft was built and designed by Franco-Italian contractor Thales Alenia Space.
For More Updates on ExoMars Updates head to: ESA ExoMars Update site
cost down. Already the cost of launching a satellite or probe into LEO is way cheaper when using ISRO’s launch vehicle than say Ariane-space. But the cost shifts heavily in favour of the latter for a GTO. Which India herself uses often. There are several other technological benifits to the program, like Hypersonic flight profile study, Effective Heat Shielding, Autonomous Navigation and a lot more so called “Dual-USe” technologies can be spin-over.
