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Big Data at Internet of Things

Big Data is used to describe datasets so large and complex that traditional data applications are not adequate for handling them, while Internet of Things (IoT) is the network of physical objects embedded with electronics, software, sensors which enables them to collect and exchange data.

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This Wednesday, Data Science Society went back to its roots and held a meetup in the Technical University. This was, however, the only similarity to our previous meetups. The venue was the Experian modelling laboratory at the TU, there was much more interaction between the audience and the speaker, and the topic was an introduction to an area not covered before in our events.

Our speaker was Stanimir Kabaivanov, the missing link between academia and business. While studying Macroeconomics at the Plovdiv University, Stanimir started working at EUROS Bulgaria as a software engineer developing a real-time operating system. Afterwards, he followed with a MSc in Financial Management and a PhD in Finance from his alma mater, all this while simultaneously working as a developer at EUROS, where he has been the Head of software development for 7 years. Another remarkable fact about Stanimir is that he is successfully specializing in several very different fields, like a Renaissance scholar – he is an Assistant Professor in Finance at his home Plovdiv University, an expert in financial econometrics and a software developer! However, for his talk he had another topic up his sleeve – Internet of Things.

Stanimir started his presentation by introducing the terms in the headline – Big Data is used to describe datasets so large and complex that traditional data applications are not adequate for handling them, while Internet of Things (IoT) is the network of physical objects embedded with electronics, software, sensors which enables them to collect and exchange data. Afterwards, Stanimir focused on the major problems in gathering data from these devices. The first of them that he covered is the interconnectivity – every device has its own communication protocol – for example LIN, FlexRay and DeviceNET in cars. Unifying these standards is not easy due to the difference in prices of microcontrollers that support each protocol. Stanimir gave a striking example with a chip for coffee machines – a single unit might cost less than a dollar, but the monthly production of these amounts to 35 million units! And a modern car might need 150 microcontrollers.

Another problem such devices face is the increasing complexity of their software – it grows from 10% to 50% every year. Programming code for this hardware cannot be easily optimized and often contains dead code that is never used, simply because the manufacturers demand describing in code all the possible situations it might face, even the impossible ones! Surprisingly, complexity is not hardware demanding – Stan gave an example with a controller for the water level in rivers and dams that has a 48 MHz CPU and is 20 to 50 times less powerful than a modern smartphone.

Next, Stanimir focused on the security issues for IoT devices. He summed up the main challenge in this regard as “appropriate data should reach the appropriate people.” For example, encrypting data is the best security solution, but data transfer takes a lot more time  – the problem breaks down to “frequent data updates vs. computational power limitation”.

Another issue that Stanimir discussed is the power consumption of IoT devices. They consume less power when in static regime, and more when they transmit data in dynamic regime.

Finally, Stanimir presented a possible solution to the heterogeneity – the OPC UA protocol. He described the idea behind it as standardizing – every device can be represented in a general scheme. Stan illustrated the approach with an analogy from NLP – representing sentences as a semantic network.

As a desert, our speaker made a live demonstration how to test if the software similar to the one used in modern cars works. It served as an illustration of yet another unsolved problem – what to do with all the data collected. If the data is transferred in real time and stored remotely, this puts a load on the transmitting network. If it is stored locally on a SSD or flash drive, this makes the device more expensive.

As a testament to the involvement of the audience of the talk, the presentation lasted for 30 minutes, while the Q&A session and the discussions after – an hour. Everybody was so engulfed in the topic that if it wasn’t for the suggestion of Prof. Marchev Jr. to continue over a beer at the Milenkata restaurant, it could well drag on until midnight.

Take a look at the video from the presentation if you want to learn more. Don’t miss our great upcoming events and projects – stay tuned by visiting our website, following our Facebook pageLinkedIn page or following our twitter account.

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