The DeviceHive team has released a new version of their gateway for Android N. DeviceHive Android Gateway for Bluetooth Low Energy devices makes it possible to connect multiple Bluetooth Low Energy devices to DeviceHive IoT clouds through a single Android device. Now it’s possible to connect sensors, buttons, indicators, wearables, and any other BLE devices to prototype solutions even faster.
All that’s needed is to start the gateway, connect it to the device, and send a command (or subscribe to sensor data notifications).
Here is a presentation showing common use cases for the DeviceHive Gateway on Android.
This quick tutorial will help to start prototyping the solutions faster.
Today IoT is expanding it’s borders and creeping into our daily lives. Devices are placed everywhere, various size from tiny things to monstrous automatic machinery. The internal device architecture and design form the constraints on the device behavior. Device protocol, operational cycle, and time are often limited by batteries, the underlying hardware, and existing libraries. The variety of protocols and message types means a typical solution to unite them involves implementing bridge-adapters that are capable of transforming data into a common format.
Bridges are often seen as a pipe accepting messages and passing them to the next collector or adapter-bridge. The central business logic resides in the middle. Central logic responsible for accepting and handling all the messages. The replies are sent back, routed through the channels back to the device and client. A typical solution will require several type of adapters to be deployed. With an increasing number of bridges, fault tolerance requirements should be kept. At this point, cluster maintenance and monitoring becomes important task.
Traffic accidents in the UK, 1979-2004.
Whether you are a journalist, a researcher or a data geek, in order to start working with large data sets, you have to complete laborious tasks of setting-up an infrastructure, configuring an environment, learning new unfamiliar tools and coding complicated apps – with DC/OS you can start crunching those numbers within minutes.
Let’s start with a problem of analyzing a set of data and take a road safety data from Great Britain, 1979-2004. While the data set might seem small, some of the analysis might require distributed processing and we should have an environment that allows our processing jobs to scale horizontally. To achieve this, we’ll be running a DC/OS cluster on top of a cluster of virtual machines. We’ll be using AWS EC2 in this scenario, but the same solution can be ported to other public and private clouds.
Why would one create a package?
Once you get familiar with DC/OS, the open source project that was created by Mesosphere you get access to packages certified by Mesosphere. There are several ways to deploy your service into DC/OS: (1) use dcos marathon command in CLI; (2) use Marathon REST API directly; (3) deploy your service as a package. Using package approach makes your solution consistent with the environment and gives other benefits.
The Third Annual Wearable Technology Show was held at London’s ExCel conference center on 15th-16th of March. There were over 140 exhibitors, nine tracks and more than 40 product launches to keep technology executives on their toes.
Healthcare & Life Science, Travel & Hospitality and Betting & iGambling Practices of DataArt exhibited at the event and demoed many of our concept products, examples of how technology brings business value. Here are some of them: KidPRO uses gamification to motivate and engage young patients while managing their own healthcare or participating in a clinical trial. MedAR is an augmented reality app that recognizes medications and provides all relevant information about it. Pills Adjutant simplifies medication adherence. Tiredness Checker helps users check their tiredness level. SmartBet and WatchSlots apps bring quick bets and slots to your wrist.
Information technology has always been full of surprisingly contradicting beliefs and every market, product or community has its own FAQ list or Top 10 Myths whitepaper. This week brought another “myth case” to my desk. Though it has been around for several years already, it is still hot. While my fellow database developers are busy completing another data warehousing project (“traditional” relational solution, by the way) for a travel firm, our marketing department approached me with the discussion of how we can define our new data warehousing offering. The question and concern was: “Hasn’t big data killed data warehousing already?”
On 27 February – 2 March, DataArt exhibited with Canonical at Mobile World Congress in Barcelona. More than 2,000 exhibitors and 100,000 attendees gathered in an arena bursting with networking opportunities to present and absorb the latest technological developments and next generation services of the mobile industry.
The big trends this year were the Internet of Things, Big Data, Cloud, VR and 5G. Almost every booth had an IoT demo stand. Cars, refrigerators, smart home solutions, low energy sensors, and the list goes on. Overcrowding of IoT booths made it evident that this year IoT is finally reaching the end users. Notably, the cloud technology becomes the new IoT standard. Almost every IoT solution enables your device data to be sent straight to the cloud. Why? Because from that point you can do whatever you want with your solution: scale it, use big data, do machine learning, etc. The sky’s the limit.
On 27 February – 2 March, DataArt exhibited with Canonical at Mobile World Congress in Barcelona. The sheer scope of the world’s biggest mobile industry event was mind boggling – 100,000 attendees and 2200 exhibitors spanned nine halls and one dozen outdoor spaces at Fira Gran Via and Fira Montjuïc.
DataArt demoed enterprise predictive maintenance IoT solution, enabling preventative, condition-based monitoring of a piece of manufacturing equipment. We used accelerometer-based sensors and an IoT gateway running Snappy Ubuntu Core to capture the vibration profile of a fan and analyzed it in AWS, to determine whether it’s in range of a normally operating equipment, and if not – to trigger a maintenance alert.
Data warehousing is not a new thing today. The concept was first introduced in the 1970s and its key terms “dimension” and “fact” appeared even earlier – in the 1960s. Since then, many businesses have successfully implemented and adopted various data warehouse solutions. Though they were using a great variety of technologies, processes, and ways of thinking, their goals were alike – consolidating data from scattered operational systems, making data clean and trustworthy, extracting the information, and unlocking hidden knowledge. All this was necessary to improve business decisions, to make them knowledgeable, rather than based on blind-guesses.
Many organizations from various industries – from finance to hospitality, from healthcare to gambling – leverage the benefits provided by this several decades old concept. But technologies evolve and brings new methods of data processing, new algorithms and implementations, new features and new possibilities. The amount of data available for analysis grows dramatically. The speed of communication increases. Thus businesses face new challenges – they need to cope with a highly competitive environment which is much faster than before, they need to evaluate the situation in a much more accurate manner, they cannot wait.
Machine learning, cloud, visualization, Hadoop, spark, data science, scalability, analytics, terabytes, petabytes, faster, bigger, more secure, simply better. The kind of a merry-go-round that keeps spinning in your head after you spend three days on the exhibit floor at Strata+Hadoop conference. And lots of elephants, of course.