Telekom Innovation Laboratories

Berlin, Germany

Telekom Innovation Laboratories

Berlin, Germany
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Li Y.,Tsinghua National Laboratory for Information Sciences and Technology | Qian M.,Tsinghua National Laboratory for Information Sciences and Technology | Jin D.,Tsinghua National Laboratory for Information Sciences and Technology | Hui P.,Hong Kong University of Science and Technology | And 5 more authors.
IEEE Transactions on Mobile Computing | Year: 2014

To cope with explosive traffic demands on current cellular networks of limited capacity, Disruption Tolerant Networking (DTN) is used to offload traffic from cellular networks to high capacity and free device-to-device networks. Current DTN-based mobile data offloading models are based on simple and unrealistic network assumptions which do not take into account the heterogeneity of mobile data and mobile users. We establish a mathematical framework to study the problem of multiple-type mobile data offloading under realistic assumptions, where (i) mobile data are heterogeneous in terms of size and lifetime; (ii) mobile users have different data subscribing interests; and (iii) the storages of offloading helpers are limited. We formulate the objective of achieving maximum mobile data offloading as a submodular function maximization problem with multiple linear constraints of limited storage, and propose three algorithms, suitable for the generic and more specific offloading scenarios, respectively, to solve this challenging optimization problem. We show that the designed algorithms effectively offload data to the DTN by using both the theoretical analysis and simulation investigations which employ both real human and vehicular mobility traces. © 2002-2012 IEEE.


News Article | December 14, 2016
Site: www.eurekalert.org

The University of California, Berkeley's worldwide network of smartphone earthquake detectors has recorded nearly 400 earthquakes since the MyShake app was made available for download in February, with one of the most active areas of the world the fracking fields of Oklahoma. The Android app harnesses a smartphone's motion detectors to measure earthquake ground motion, then sends that data back to the Berkeley Seismological Laboratory for analysis. The eventual goal is to send early-warning alerts to users a bit farther from ground zero, giving them seconds to a minute of warning that the ground will start shaking. That's enough time to take cover or switch off equipment that might be damaged in a quake. To date, nearly 220,000 people have downloaded the app, and at any one time, between 8,000 and 10,000 phones are active -- turned on, lying on a horizontal surface and connected to a wi-fi network - and thus primed to respond. An updated version of the MyShake app will be available for download Dec. 14 from the Google Play Store, providing an option for push notifications of recent quakes within a distance determined by the user, and the option of turning the app off until the phone is plugged in, which could extend the life of a single charge in older phones. "The notifications will not be fast initially - not fast enough for early warning - but it puts into place the technology to deliver the alerts and we can then work toward making them faster and faster as we improve our real-time detection system within MyShake," said project leader Richard Allen, a UC Berkeley professor of earth and planetary sciences and director of the seismology lab. In a presentation on Wednesday, Dec. 14, during this week's annual meeting of the American Geophysical Union in San Francisco, UC Berkeley developer and graduate student Qingkai Kong will summarize the app's performance. Ten months of operation clearly shows that the sensitivity of the smartphone accelerometers and the density of phones in many places are sufficient to provide data quickly enough for early warning. The phones readily detect the first seismic waves to arrive - the less destructive P waves - and send the information to Berkeley in time to issue an alert that the stronger S wave will soon arrive. "We already have the algorithm to detect the earthquakes running on our server, but we have to make sure it is accurate and stable before we can start issuing warnings, which we hope to do in the near future," Kong said. The app can detect quakes as small as magnitude 2.5, with the best sensitivity in areas with a greater density of phones. The largest number of phones to record a quake was 103, after the 5.2 magnitude quake that occurred on the San Jacinto fault near Borrego Springs in San Diego County on June 10. Phones 200 kilometers from the epicenter detected that temblor. The largest quake detected occurred on April 16 in Ecuador: a 7.8 magnitude quake that triggered two phones, 170 and 200 kilometers from the epicenter. Allen, Kong and their colleagues at Deutsche Telekom's Silicon Valley Innovation Center believe the app's performance shows it can complement traditional seismic networks, such as that operated nationally by the U.S. Geological Survey, but can also serve as a stand-alone system in places with few seismic stations, helping to reduce injuries and damage from earthquakes. While the app has detected quakes in seismically active areas such as Chile, Mexico, New Zealand, Taiwan, Japan and the West Coast of the U.S., one surprising hot spot has been the traditionally quiet state of Oklahoma. The practice of injecting oil well wastewater deep underground has activated faults in the area to the extent that the state is rattled hundreds of times a year. "Oklahoma is now clearly No. 1 in terms of the number of earthquakes in the lower 48 states," Kong said. Most of Oklahoma's earthquakes are small, but MyShake users in the state, which number only about 200, easily detected the Sept. 3 magnitude 5.8 quake, the strongest ever to hit the state. During that event, 14 phones in the state triggered, but even this relatively small number of phones allowed the seismology lab to peg the magnitude within 1 percent of estimates from ground seismic stations, and located the epicenter to within 4 kilometers (2.5 miles). "These initial studies suggest that the data will be useful for a variety of scientific studies of induced seismicity phenomena in Oklahoma, as well as having the potential to provide earthquake early warning in the future," Kong said. He will summarize the Oklahoma data during a poster session on Friday, Dec. 16. The MyShake app and the computer algorithm behind it were developed by Allen, Kong and a team of programmers at the Silicon Valley Innovation Center in Mountain View, California, which is part of the Telekom Innovation Laboratories (T-Labs) operated by Deutsche Telekom, owner of T-Mobile. Louis Schreier, the leader of that team, co-wrote a paper with Allen and Kong on the first six months of MyShake's observations, published Sept. 29 in the journal Geophysical Research Letters.


News Article | December 14, 2016
Site: www.rdmag.com

The University of California, Berkeley's worldwide network of smartphone earthquake detectors has recorded nearly 400 earthquakes since the MyShake app was made available for download in February, with one of the most active areas of the world the fracking fields of Oklahoma. The Android app harnesses a smartphone's motion detectors to measure earthquake ground motion, then sends that data back to the Berkeley Seismological Laboratory for analysis. The eventual goal is to send early-warning alerts to users a bit farther from ground zero, giving them seconds to a minute of warning that the ground will start shaking. That's enough time to take cover or switch off equipment that might be damaged in a quake. To date, nearly 220,000 people have downloaded the app, and at any one time, between 8,000 and 10,000 phones are active -- turned on, lying on a horizontal surface and connected to a wi-fi network - and thus primed to respond. An updated version of the MyShake app will be available for download Dec. 14 from the Google Play Store, providing an option for push notifications of recent quakes within a distance determined by the user, and the option of turning the app off until the phone is plugged in, which could extend the life of a single charge in older phones. "The notifications will not be fast initially - not fast enough for early warning - but it puts into place the technology to deliver the alerts and we can then work toward making them faster and faster as we improve our real-time detection system within MyShake," said project leader Richard Allen, a UC Berkeley professor of earth and planetary sciences and director of the seismology lab. In a presentation on Wednesday, Dec. 14, during this week's annual meeting of the American Geophysical Union in San Francisco, UC Berkeley developer and graduate student Qingkai Kong will summarize the app's performance. Ten months of operation clearly shows that the sensitivity of the smartphone accelerometers and the density of phones in many places are sufficient to provide data quickly enough for early warning. The phones readily detect the first seismic waves to arrive - the less destructive P waves - and send the information to Berkeley in time to issue an alert that the stronger S wave will soon arrive. "We already have the algorithm to detect the earthquakes running on our server, but we have to make sure it is accurate and stable before we can start issuing warnings, which we hope to do in the near future," Kong said. The app can detect quakes as small as magnitude 2.5, with the best sensitivity in areas with a greater density of phones. The largest number of phones to record a quake was 103, after the 5.2 magnitude quake that occurred on the San Jacinto fault near Borrego Springs in San Diego County on June 10. Phones 200 kilometers from the epicenter detected that temblor. The largest quake detected occurred on April 16 in Ecuador: a 7.8 magnitude quake that triggered two phones, 170 and 200 kilometers from the epicenter. Allen, Kong and their colleagues at Deutsche Telekom's Silicon Valley Innovation Center believe the app's performance shows it can complement traditional seismic networks, such as that operated nationally by the U.S. Geological Survey, but can also serve as a stand-alone system in places with few seismic stations, helping to reduce injuries and damage from earthquakes. While the app has detected quakes in seismically active areas such as Chile, Mexico, New Zealand, Taiwan, Japan and the West Coast of the U.S., one surprising hot spot has been the traditionally quiet state of Oklahoma. The practice of injecting oil well wastewater deep underground has activated faults in the area to the extent that the state is rattled hundreds of times a year. "Oklahoma is now clearly No. 1 in terms of the number of earthquakes in the lower 48 states," Kong said. Most of Oklahoma's earthquakes are small, but MyShake users in the state, which number only about 200, easily detected the Sept. 3 magnitude 5.8 quake, the strongest ever to hit the state. During that event, 14 phones in the state triggered, but even this relatively small number of phones allowed the seismology lab to peg the magnitude within 1 percent of estimates from ground seismic stations, and located the epicenter to within 4 kilometers (2.5 miles). "These initial studies suggest that the data will be useful for a variety of scientific studies of induced seismicity phenomena in Oklahoma, as well as having the potential to provide earthquake early warning in the future," Kong said. He will summarize the Oklahoma data during a poster session on Friday, Dec. 16. The MyShake app and the computer algorithm behind it were developed by Allen, Kong and a team of programmers at the Silicon Valley Innovation Center in Mountain View, California, which is part of the Telekom Innovation Laboratories (T-Labs) operated by Deutsche Telekom, owner of T-Mobile. Louis Schreier, the leader of that team, co-wrote a paper with Allen and Kong on the first six months of MyShake's observations, published Sept. 29 in the journal Geophysical Research Letters.


Shang S.,Princeton University | Cuff P.,Hong Kong University of Science and Technology | Hui P.,Telekom Innovation Laboratories | Kulkarni S.,Princeton University
Proceedings - IEEE INFOCOM | Year: 2013

We analyze a class of distributed quantized consensus algorithms for arbitrary networks. In the initial setting, each node in the network has an integer value. Nodes exchange their current estimate of the mean value in the network, and then update their estimate by communicating with their neighbors in a limited capacity channel in an asynchronous clock setting. Eventually, all nodes reach consensus with quantized precision. We start the analysis with a special case of a distributed binary voting algorithm, then proceed to the expected convergence time for the general quantized consensus algorithm proposed by Kashyap et al. We use the theory of electric networks, random walks, and couplings of Markov chains to derive an O(N3 log N) upper bound for the expected convergence time on an arbitrary graph of size N, improving on the state of art bound of O(N4 log N) for binary consensus and O(N 5) for quantized consensus algorithms. Our result is not dependent on the graph topology. Simulations are performed to validate the analysis. © 2013 IEEE.


News Article | March 14, 2016
Site: motherboard.vice.com

Bot networks still wreak havoc online. Millions of hacks, spam operations and online fraud campaigns perpetrated by botnets in recent years have done serious damage to law-abiding internet users: In the U.S. alone, botnets have caused over $9 billion in losses, the FBI estimates. Although you can protect yourself by setting up firewalls and antivirus software, combating botnets on a larger scale has traditionally been difficult for law enforcement, because there isn’t a proven methodology for connecting one “bot” to another or back to the hacker controlling the network. A group of Israeli researchers believe they are the first to have discovered a way to locate botnets and identify who is behind them, by planting honeypots that gather information about attacks carried out by the network, and analyzing that data with machine learning programs. A botnet is a group of computers infected with malware that’s used to do a cybercriminal’s bidding from afar. A hacker spreads malware to thousands or millions of unprotected computers around the world, typically through spear-phishing emails with malware-infected attachments. The hacker then controls the network remotely, harnessing the bots’ combined power to carry out denial-of-service attacks or spam campaigns that scam targets out of their money. The hacker conceals its identity and activities, and the hosts typically don’t even know they’ve become part of a virtual zombie army. Botmasters can also lease their zombie networks on the dark web to other cyber thieves, and this rent-a-bot scheme can be very lucrative when carried out on a large scale. The operators of the botnet Bamital, which had control of about 8 million computers worldwide in order to highjack search results, earned an estimated $1.1 million a year from their operation, according to the security firm Symantec. Now, researchers at Telekom Innovation Laboratories at Ben Gurion University of the Negev in Israel say they have discovered and traced six botnets by analyzing data collected through cyberattacks. I visited the lab at the new cybersecurity complex underway in Beersheba, Israel, in February. The researchers told me they had potentially found a way to teach computer programs how to identify relationships among the millions of malicious bots around the world in order to discover which network they belong to. The lab, a research and development arm of German telecom giant Deutsche Telekom, set up several hundred honeypots in Deutsche Telekom’s vast customer network, which comprises some 150 million people. Honeypots are designed to lure hackers by masquerading as a web server, pretending to contain the kind of personal data that hackers love, like credit card numbers, emails, and medical records. And in this case, it was successful. Some of the honeypots the team set up were real databases, and the idea was “basically to just expose them to the network” and wait for them to get attacked by zombie bots, researchers explained. Each of the team’s honeypots was attacked thousands of times a day over a roughly one-year period. Each time they were attacked, the honeypots recorded critical information about their attackers and the way they behaved, including the attackers’ geolocation and IP address. But the planted software recorded too much information for a mere mortal to ever hope to analyze, so they turned to artificial intelligence. To cope with the oceans of data the honeypot software had recorded from all these attacks, the researchers used machine learning algorithms tricked out to fit their specific needs. The team used 17 unique algorithms to classify and categorize attacks based on hundreds of characteristics designed to differentiate one bot from the next.If a spam campaign was sending out emails at a rapid pace for a certain number of hours, for example, that behavior was analyzed according to about 700 different micro-criteria. “Basically, we taught the technology to be able to identify a bot on its own” without the help of humans, said Dudu Mimran, a towering, teddy-bear like man with piercing blue eyes who is the Chief Technology Officer at the Labs. The artificial intelligence programs were eventually able to learn the behavior of thousands of bots in cyberspace and group them into networks based on that behavior. The researchers believe they’re the first to have done this. “We can see where the bots reside, what their IP addresses are, and to which bot network they belong,” said Lior Rokach, a professor of data science at Ben Gurion University of the Negev and one of the principal investigators of the the Labs project. At the labs office in Beersheba, Rokach showed me a map of tens of thousands of bots that his team’s project had identified around the world. Zooming in on North Dakota, Rokach revealed attack data on over 1,200 bots that had been curated autonomously by the group’s AI algorithms. The new bridge from the Beersheba cyber park to Ben Gurion University in the Negev. Image: Hunter Stuart So where are these botnets located, and who’s behind them? Although the data shows large numbers of bots in Russia and China, demographics are a better indicator than geography: There tend to be more bots in populations that are less computer-savvy—essentially anywhere where people aren’t educated about how to protect themselves from phishing emails or other online scams. Areas where piracy is prevalent also show higher rates of infection. If you use The Pirate Bay or another program to torrent movies for free, you’re at greater risk of unknowingly downloading malware. “As the saying goes, if it’s free, then you’re the product,” said said Oleg Brodt, Telekom Innovation Laboratories’ Senior R&D Developer. Though the popular perception of a hacker is a pale teenager working out of his parents’ basement, botnets are more often controlled by organized criminal entities, Mimran said. “You’re making a lot of money if you own a botnet of 500,000 bots,” he said. “So usually that’s organized crime.” For example, it was a Russian gang of cybercriminals who the FBI suspects was behind the “Gameover Zeus” botnet that infected 1 million computers with malicious software which the gang used to hack into online bank accounts, causing an estimated $100 million in financial losses before the FBI shut them down in 2014. The gang’s suspected ringleader, Evgeniy Bogachev, now has a $3 million bounty on his head. Deutsche Telekom says it doesn’t have any plans to provide information to law enforcement at this point because the data they have is outdated and won’t help find existing links between zombie bots and their commanders. This is the problem with battling botnets: the networks can quickly mutate and conceal themselves. “I say good on DT [Deutsche Telekom] for building this system, but make no mistake it's going to miss a lot of botnet activity,” security expert Brian Krebs, told Motherboard. “This stuff is in flux so quickly and up one minute for the first time ever and then down after a short time of spreading bad stuff.” Butthe researchers believe that if their algorithms can be applied to new attack data in the future, it would help tamp down the activity of these shadowy online networks. “The utopic idea is to identify the emergence of new botnets at the infection stage,” Mimran said. “If we can do that, it would help to eliminate--or at least reduce--the botnet phenomenon.” Theoretically, the bot networks identified by the research lab could be handed over to law enforcement to investigate to see what control center the bots are communicating with—in other words, who is at the top of the organized cybercrime hierarchy. “It’s like with drug trafficking,” Mimran said. “The idea is to find the kingpin.”


Ketabdar H.,Telekom Innovation Laboratories | Moghadam P.,CSIRO | Roshandel M.,Telekom Innovation Laboratories
Proceedings - 2012 6th International Conference on Complex, Intelligent, and Software Intensive Systems, CISIS 2012 | Year: 2012

Around Device Interaction (ADI) is recently introduced in the field of Human Computer Interaction (HCI) to provide touchless, more intuitive way of interaction using space beyond the physical boundary of the computing devices. In this paper, we introduce a new ADI input device called Pingu in the form factor of a fingering that allows users to interact with any nearby computing device with wireless connectivity in a ubiquitous environment. Fingering form-factor is chosen for our prototype design, as it is socially acceptable and is commonly worn in everyday social contexts, and based on the previous research, the information entropy of interaction by fingers is greater than the entropy for any other parts of the human body. The current Pingu prototype is consisted of an extensive set of sensors, visual and vibrotactile feedback mechanisms with wireless connectivity that make it a unique input device for human-computer or human-human interaction in the form of gestures, tactile and touch. Its usage can range from advanced, tiny and novel gestural interaction with a variety of devices to mobile and networked sensing, and social computing. We present a few potential applications of Pingu such as social interaction, context recognition, in-car interaction, and physical activity analysis. © 2012 Crown Copyright.


Uzun A.,TU Berlin | Salem M.,Telekom Innovation Laboratories | Kupper A.,TU Berlin
Proceedings - 2013 IEEE 7th International Conference on Semantic Computing, ICSC 2013 | Year: 2013

Conventional positioning approaches for Location-based Services (LBS) such as those provided by Google and Apple, are solely driven by geometric spatial data. Especially in proactive LBS scenarios, in which users are notified as soon as they reach a certain area, locations are mostly defined by geofences and do not incorporate any further information from the semantics of the location, such as the points of interest in the vicinity or more detailed information about the district the user is in. Leveraging LBS with the extensive pool of interconnected data in the Linking Open Data (LOD) Cloud will improve the LBS experience and will enable the development of sophisticated proactive services. In this paper, we present a Semantic Positioning Platform that enhances classic positioning methods by semantic features. This platform utilizes the OpenMobileNetwork, which is a Live Crowd sourcing Platform providing static as well as dynamic mobile network topology data based on the principles of Linked Data. It further uses the Positioning Enabler that enables persistent user background tracking and subscription to Semantic LBS Services. The Semantic Positioning approach allows LBS providers to locate users with respect to the semantics of their position instead of defining spatial geofences. As a proof-of-concept, a Restaurant Recommender Service is presented and its applicability is evaluated. © 2013 IEEE.


Alt F.,University of Stuttgart | Muller J.,Telekom Innovation Laboratories | Schmidt A.,University of Stuttgart
Computer | Year: 2012

For advertising-based public display networks to become truly pervasive, they must provide a tangible social benefit and be engaging without being obtrusive, blending advertisements with informative content. © 2012 IEEE.


Barbera M.V.,University of Rome La Sapienza | Kosta S.,University of Rome La Sapienza | Stefa J.,University of Rome La Sapienza | Hui P.,Telekom Innovation Laboratories | Mei A.,University of Rome La Sapienza
2012 IEEE 12th International Conference on Peer-to-Peer Computing, P2P 2012 | Year: 2012

The battery limits of today smartphones require a solution. In the scientific community it is believed that a promising way of prolonging battery life is to offload mobile computation to the cloud. State of the art offloading architectures consists of virtual copies of real smartphones (the clones) that run on the cloud, are synchronized with the corresponding devices, and help alleviate the computational burden on the real smartphones. Recently, it has been proposed to organize the clones in a peer-to-peer network in order to facilitate content sharing among the mobile smartphones. We believe that P2P network of clones, aside from content sharing, can be a useful tool to solve critical security problems on the mobile network of smartphones. In particular, we consider the problem of computing an efficient patching strategy to stop worm spreading between smartphones. The peer-to-peer network of clones is used to compute the best strategy to patch the smartphones in such a way that the number of devices to patch is low (to reduce the load on the cellular infrastructure) and that the worm is stopped quickly. We consider two well defined worms, one spreading between the devices and one attacking the cloud before moving to the real smartphones; we describe CloudShield, a suite of protocols running on the peer-to-peer network of clones; and we show by experiments that CloudShield outperforms state-of-the-art worm-containment mechanisms for mobile wireless networks. © 2012 IEEE.


Hassenzahl M.,Folkwang University of the Arts | Heidecker S.,Folkwang University of the Arts | Eckoldt K.,Folkwang University of the Arts | Diefenbach S.,Folkwang University of the Arts | Hillmann U.,Telekom Innovation Laboratories
ACM Transactions on Computer-Human Interaction | Year: 2012

A wealth of evidence suggests that love, closeness, and intimacy-in short relatedness-are important for people's psychological well-being. Nowadays, however, couples are often forced to live apart. Accordingly, there has been a growing and flourishing interest in designing technologies that mediate (and create) a feeling of relatedness when being separated, beyond the explicit verbal communication and simple emoticons available technologies offer. This article provides a review of 143 published artifacts (i.e., design concepts, technologies). Based on this, we present six strategies used by designers/researchers to create a relatedness experience: Awareness, expressivity, physicalness, gift giving, joint action, and memories. We understand those strategies as starting points for the experience-oriented design of technology. © 2012 ACM.

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