We bring our data and experience to you for a live talk.
Domos has a team of data science experts with years of experience working directly with data from the home and data models to properly develop machine learning algorithms. Our data science experts are working with data sets of unprecedented quality from 100,000 homes - logging data continuously on a second-by-second basis.
We collaborate and share our insights and data in multiple industry and data science networks. This makes Domos a great hub for sharing and developing data science expertise in an open forum. That is why we are bringing our knowledge and insight to companies and events that will benefit from our learnings over the years. This article contains the talks we currently provide and will be updated as we learn and understand even more.
For more information about how to collaborate with us or how to get us to your company/event, please reach out to us on info@domos.no
Service Classification & Quality of Experience
Service classification is the process of identifying services running on a network by looking at traffic patterns. Domos has developed a system for this which can detect specific services such as Fortnite and Skype. Combined with a new and better way to measure network quality, called QED, this technology allows ISPs to optimise for application outcomes. QED is a framework for measuring and reasoning about network performance which is now in the process of standardization through the broadband forum (BBF). The technique has successfully been used to improve network performance at CERN’s ATLAS experiment. It has also been used to measure the impact of network upgrades and to detect network problems by several major European ISPs with Vodafone as one of the main adopters.
In this talk, Bjørn Ivar Teigen will be presenting the two processes and why we think they are critical in order to achieve correct use of network resources based on the requirements each application needs to deliver excellent Quality of Experience to the users.
Mimicking Evolution For Better Wi-Fi In Dense Areas
Everyone hates unstable, unreliable Wi-Fi, and still, it’s everywhere. One of the most common reasons for slow Wi-Fi is other routers. There is a limited amount of data that can be transmitted through the radio spectrum at any time, and other routers on the same channel as yours will reduce your precious download and upload speeds.
Competing with other routers for the same capacity is known as Wi-Fi congestion. In large apartment complexes, there may be hundreds of routers that share the same frequency as there are only 3 non-overlapping channels to choose from on the widely used 2.4GHz band.
In densely populated areas, Wi-Fi often breaks down because it is highly congested. In this talk, Bjørn Ivar presents the challenges associated with congestion and a way to improve Wi-Fi in populated areas. At Domos we utilize our cloud solution along with genetic algorithms, to evolve the best channel assignments for each case. This reduces the number of competing neighbours and improves the utilization of the radio resources. The solution is tried and tested through actual deployment, and we showcase real-world results.
About the speaker
Bjørn Ivar Teigen is a researcher and data scientist at Domos with a Master’s Degree in Robotics and Intelligent Systems from The University of Oslo. He is currently doing a PhD co-sponsored by Domos and The Research Council of Norway titled “Hierarchical Reinforcement Learning Models with Applications in Radio Resource Management” in the field of Machine Learning and Computer Networks. He has previously worked on and developed several algorithms at Domos related to optimisation of networks in congested areas, classifying traffic based on packet sizes and other research initiatives we are involved in. To read more about what Domos is working on including articles written by Bjørn Ivar, please visit our Medium Publication here: https://medium.com/domos-creating-the-home-it-assistant.
What’s Wrong With Wi-Fi?
Domos have over the last year measured Wi-Fi Quality of Experience metrics in over 100 000 homes on a second by second basis. Through this data gathering and extensive research, we’ve learned to identify the cause and effects of low QoE.
The major pain is data transmitted slowly, or with low data rates as is industry jargon. We show that the primary cause of low data rates is not poor signal-strength as is popular belief, but end-user devices’ suboptimal selection of data rates. We show that the data rates set by devices are significantly lower than those set by the routers and that device types and device vendors show distinct behavioural patterns. And that it should be possible, though complex, to significantly increase speeds through configurations on the router.
Understanding Wi-Fi QoE is the core of Domos’ research, and we invite others to join us in attempts to optimize it on current and future hardware and protocols. Our preliminary results show that with machine learning and statistical techniques, optimization is possible by recognizing device types. We can learn to manipulate the behaviour of their rate adaptation algorithm, finding their strengths and weaknesses in different types of environments and for different types of usage.
This talk has been previously held for: Oslo Data Science Meetup, Telenor Research and TNOs Ultrafast Broadband seminar in Den Haag (now UFBB BASe).
Enabling ISP Data-Monetization in a Post-GDPR World
Our lives are increasingly digital. For the time we spend in our homes, every digital activity and experience can be tracked in the internet gateway (CPE). Better insight into this data can help the ISPs deliver higher quality products and get an exceptional understanding regarding users. Unfortunately, much of this data is sensitive and it is hard to argue that the ISP needs to store this data, an important part of GDPR. Additionally, collecting and deriving insight from the data in scale is challenging.
In this presentation, Magnus Olden explains how an ISP could achieve high levels of customer insight in a privacy-sensitive way. This can be done by using a Federated Learning approach and/or Secure Aggregation Protocols to create non-reversible, self-constraining methods for learning about users. This will facilitate happier customers and the ability to identify the groups of customers that are most likely to churn/cross and upsell/give high NPS scores, etc. All within the reach of GDPR.
He describes how deep insight can enable new business lines and revenue streams. The way Amazon knows the patterns of what you shop, Google what you do on the internet and Facebook about your social life — this presentation shows how the same approach can give the ISP the knowledge for where we spend most of our time: At home.
About the speaker
Magnus Olden is the Head of Data Science at Domos with a Master’s Degree in Informatics: Robotics and Intelligent Systems from the University of Oslo. He is specialized in Wi-Fi, CPE software stack and Machine Learning, currently involved in several projects and initiatives within these domains. He is an active member of the Connected Home Council in the Broadband Forum and co-writer of the USP (TR-369) white paper published by the Broadband Forum. To read more about what Domos is working on including articles written by Magnus Olden, please visit our Medium Publication here: https://medium.com/domos-creating-the-home-it-assistant
