@article{Mayrhofer_2021_ACMCS,
author={Rene Mayrhofer and Stephan Sigg},
journal={ACM Computing Surveys},
title={Adversary Models for Mobile Device Authentication},
year={2021},
abstract={Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. 
In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. 
However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. 
In this article, we introduce a new classification of adversaries to better analyze and compare mobile device authentication methods. We apply this classification to a systematic literature survey. The survey shows that security is still an afterthought and that most proposed protocols lack a comprehensive security analysis. 
The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.
},
issue_date = {to appear},
publisher = {ACM},
volume = { },
number = { },
pages = {1-33},
group = {ambience},
doi = {10.1145/3477601}
}

Mobile device authentication has been a highly active research topic for over 10 years, with a vast range of methods proposed and analyzed. In related areas, such as secure channel protocols, remote authentication, or desktop user authentication, strong, systematic, and increasingly formal threat models have been established and are used to qualitatively compare different methods. However, the analysis of mobile device authentication is often based on weak adversary models, suggesting overly optimistic results on their respective security. In this article, we introduce a new classification of adversaries to better analyze and compare mobile device authentication methods. We apply this classification to a systematic literature survey. The survey shows that security is still an afterthought and that most proposed protocols lack a comprehensive security analysis. The proposed classification of adversaries provides a strong and practical adversary model that offers a comparable and transparent classification of security properties in mobile device authentication.

@inproceedings{Sigg2020Camouflage,
  title={Camouflage Learning},
  author={Stephan Sigg and Le Ngu Nguyen and Jing Ma},
  booktitle={The 19th International Conference on Pervasive Computing and Communications (PerCom 2021), adjunct},
  year={2021},
  abstract={Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. Instead of gathering datasets to a central server for model training in traditional machine learning, in federated learning, model updates are computed locally at distributed devices and merged at a coordinator. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a distributed machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.
},
  group = {ambience},
  project = {radiosense, abacus}
}

Federated learning has been proposed as a concept for distributed machine learning which enforces privacy by avoiding sharing private data with a coordinator or distributed nodes. Instead of gathering datasets to a central server for model training in traditional machine learning, in federated learning, model updates are computed locally at distributed devices and merged at a coordinator. However, information on local data might be leaked through the model updates. We propose Camouflage learning, a distributed machine learning scheme that distributes both the data and the model. Neither the distributed devices nor the coordinator is at any point in time in possession of the complete model. Furthermore, data and model are obfuscated during distributed model inference and distributed model training. Camouflage learning can be implemented with various Machine learning schemes.

@InProceedings{Nico_2020_HeartFailure, 
author = {Nico Jähne-Raden and Udo Bavendiek and Henrike Gutschleg and Ulf Kulau and Stephan Sigg and Marie Cathrine Wolf and Tanja Zeppernick and Michael Marschollek}, 
booktitle = {Studies in health technology and Informatics}, 
title = {A structured measurement of highly synchronous real-time ballistocardiography signal data of heart failure patients},
year = {2020},
project ={ballisto},
group={ambience}
}

@InProceedings{Sameera_2020_Beamsteering,
  author    = {Sameera Palipana and Nicolas Malm and Stephan Sigg},
  booktitle = {18th Annual IEEE International Conference on Pervasive Computing and Communications (PerCom) },
  title     = {Beamsteering for training-free Recognition of Multiple Humans Performing Distinct Activities},
  year      = {2020},
  doi = {10.1109/PerCom45495.2020.9127374},
  abstract  = {Recognition of the context of humans plays an important role in pervasive applications such as intrusion detection, human density estimation for heating, ventilation and air-conditioning in smart buildings, as well as safety guarantee for workers during human-robot interaction. Radio vision is able to provide these sensing capabilities with low privacy intrusion. A common challenge though, for current radio sensing solutions is to distinguish simultaneous movement from multiple subjects. We present an approach that exploits multi-antenna installations, for instance, found in upcoming 5G instrumentations, to detect and extract activities from spatially scattered human targets in an ad-hoc manner in arbitrary environments and without prior training of the multi-subject detection. We perform receiver-side beamforming and beam-steering over different azimuth angles to detect human presence in those regions separately. We characterize the resultant fluctuations in the spatial streams due to human influence using a case study and make the traces publicly available. We demonstrate the potential of this approach through two applications: 1) By feeding the similarities of the resulting spatial streams into a clustering algorithm, we count the humans in a given area without prior training. (up to 6 people in a 22.4m2 area with an accuracy that significantly exceeds the related work). 2) We further demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation.},
  %url_Paper = {http://ambientintelligence.aalto.fi/paper/findling_closed_eye_eog.pdf},
  project = {radiosense},
  group = {ambience}
  }

Recognition of the context of humans plays an important role in pervasive applications such as intrusion detection, human density estimation for heating, ventilation and air-conditioning in smart buildings, as well as safety guarantee for workers during human-robot interaction. Radio vision is able to provide these sensing capabilities with low privacy intrusion. A common challenge though, for current radio sensing solutions is to distinguish simultaneous movement from multiple subjects. We present an approach that exploits multi-antenna installations, for instance, found in upcoming 5G instrumentations, to detect and extract activities from spatially scattered human targets in an ad-hoc manner in arbitrary environments and without prior training of the multi-subject detection. We perform receiver-side beamforming and beam-steering over different azimuth angles to detect human presence in those regions separately. We characterize the resultant fluctuations in the spatial streams due to human influence using a case study and make the traces publicly available. We demonstrate the potential of this approach through two applications: 1) By feeding the similarities of the resulting spatial streams into a clustering algorithm, we count the humans in a given area without prior training. (up to 6 people in a 22.4m2 area with an accuracy that significantly exceeds the related work). 2) We further demonstrate that simultaneously conducted activities and gestures can be extracted from the spatial streams through blind source separation.

@InProceedings{Hintze_19_CORMORANTImplementingRisk,
  author    = {Daniel Hintze and Matthias F\"uller and Sebastian Scholz and Rainhard Dieter Findling and Muhammad Muaaz and Philipp Kapfer and Wilhelm N\"ussler and Ren\'e Mayrhofer},
  booktitle = {17th International Conference on Advances in Mobile Computing and Multimedia},
  title     = {CORMORANT: On Implementing Risk-Aware Multi-Modal Biometric Cross-Device Authentication For Android},
  year      = {2019},
  abstract  = {This paper presents the design and open source implementation of CORMORANT , an Android authentication framework able to increase usability and security of mobile authentication. It uses transparent behavioral and physiological biometrics like gait, face, voice, and keystrokes dynamics to continuously evaluate the user’s identity without explicit interaction. Using signals like location, time of day, and nearby devices to assess the risk of unauthorized access, the required level of confidence in the user’s identity is dynamically adjusted. Authentication results are shared securely, end-to-end encrypted using the Signal messaging protocol, with trusted devices to facilitate cross-device authentication for co-located devices, detected using Bluetooth low energy beacons. CORMORANT is able to reduce the authentication overhead by up to 97\% compared to conventional knowledge-based authentication whilst increasing security at the same time. We share our perspective on some of the successes and shortcomings we encountered implementing and evaluating CORMORANT to hope to inform others working on similar projects.},
  url_Paper = {http://ambientintelligence.aalto.fi/paper/Hintze_19_CORMORANTImplementingRisk_cameraReady.pdf},
group = {ambience}}

This paper presents the design and open source implementation of CORMORANT , an Android authentication framework able to increase usability and security of mobile authentication. It uses transparent behavioral and physiological biometrics like gait, face, voice, and keystrokes dynamics to continuously evaluate the user’s identity without explicit interaction. Using signals like location, time of day, and nearby devices to assess the risk of unauthorized access, the required level of confidence in the user’s identity is dynamically adjusted. Authentication results are shared securely, end-to-end encrypted using the Signal messaging protocol, with trusted devices to facilitate cross-device authentication for co-located devices, detected using Bluetooth low energy beacons. CORMORANT is able to reduce the authentication overhead by up to 97% compared to conventional knowledge-based authentication whilst increasing security at the same time. We share our perspective on some of the successes and shortcomings we encountered implementing and evaluating CORMORANT to hope to inform others working on similar projects.

@article{Hintze_2019_Ubicomp,
  author = {Daniel Hintze and Matthias F\"uller and Sebastian Scholz and Rainhard Findling and Muhammad Muaaz and Philipp Kapfer and Eckhard Kochand Ren\'{e} Mayrhofer},
  journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)},
  title = {CORMORANT: Ubiquitous Risk-Aware Multi-Modal Biometric Authentication Across Mobile Devices},
  year = {2019},
  month = sep,
  abstract = {People own and carry an increasing number of ubiquitous mobile devices, such as smartphones, tablets, and notebooks. Being
small and mobile, those devices have a high propensity to become lost or stolen. Since mobile devices provide access to their
owners’ digital lives, strong authentication is vital to protect sensitive information and services against unauthorized access.
However, at least one in three devices is unprotected, with inconvenience of traditional authentication being the paramount
reason. We present the concept of CORMORANT , an approach to significantly reduce the manual burden of mobile user
verification through risk-aware, multi-modal biometric, cross-device authentication. Transparent behavioral and physiological
biometrics like gait, voice, face, and keystroke dynamics are used to continuously evaluate the user’s identity without explicit
interaction. The required level of confidence in the user’s identity is dynamically adjusted based on the risk of unauthorized
access derived from signals like location, time of day and nearby devices. Authentication results are shared securely with
trusted devices to facilitate cross-device authentication for co-located devices. Conducting a large-scale agent-based simulation
of 4 000 users based on more than 720 000 days of real-world device usage traces and 6.7 million simulated robberies and thefts
sourced from police reports, we found the proposed approach is able to reduce the frequency of password entries required on
smartphones by 97.82% whilst simultaneously reducing the risk of unauthorized access in the event of a crime by 97.72%,
compared to conventional knowledge-based authentication.
},
doi={10.1145/2800835.2800906},
group = {ambience}}

People own and carry an increasing number of ubiquitous mobile devices, such as smartphones, tablets, and notebooks. Being small and mobile, those devices have a high propensity to become lost or stolen. Since mobile devices provide access to their owners’ digital lives, strong authentication is vital to protect sensitive information and services against unauthorized access. However, at least one in three devices is unprotected, with inconvenience of traditional authentication being the paramount reason. We present the concept of CORMORANT , an approach to significantly reduce the manual burden of mobile user verification through risk-aware, multi-modal biometric, cross-device authentication. Transparent behavioral and physiological biometrics like gait, voice, face, and keystroke dynamics are used to continuously evaluate the user’s identity without explicit interaction. The required level of confidence in the user’s identity is dynamically adjusted based on the risk of unauthorized access derived from signals like location, time of day and nearby devices. Authentication results are shared securely with trusted devices to facilitate cross-device authentication for co-located devices. Conducting a large-scale agent-based simulation of 4 000 users based on more than 720 000 days of real-world device usage traces and 6.7 million simulated robberies and thefts sourced from police reports, we found the proposed approach is able to reduce the frequency of password entries required on smartphones by 97.82% whilst simultaneously reducing the risk of unauthorized access in the event of a crime by 97.72%, compared to conventional knowledge-based authentication.

@article{Findling_2018_TMC,
  author = {Rainhard Findling and Michael H\"olzl and Ren\'e Mayrhofer},
  title = {Mobile Match-on-Card Authentication Using Offline-Simplified Models with Gait and Face Biometrics},
  journal = {IEEE Transactions on Mobile Computing (TMC)},
  year = {2018},
  volume = {14},
  number = {11},
  pages = {2578-2590},
  url_Paper = {http://ambientintelligence.aalto.fi/findling/pdfs/publications/Findling_18_MobileMatchon.pdf},
  doi = {https://doi.org/10.1109/TMC.2018.2812883},
  abstract = {Biometrics have become important for mobile authentication, e.g. to unlock devices before using them. One way to protect biometric information stored on mobile devices from disclosure is using embedded smart cards (SCs) with biometric match-on-card (MOC) approaches. However, computational restrictions of SCs also limit biometric matching procedures. We present a mobile MOC approach that uses offline training to obtain authentication models with a simplistic internal representation in the final trained state, wherefore we adapt features and model representation to enable their usage on SCs. The pre-trained model can be shipped with SCs on mobile devices without requiring retraining to enroll users. We apply our approach to acceleration based mobile gait authentication as well as face authentication and compare authentication accuracy and computation time of 16 and 32 bit Java Card SCs. Using 16 instead of 32 bit SCs has little impact on authentication performance and is faster due to less data transfer and computations on the SC. Results indicate 11.4% and 2.4-5.4% EER for gait respectively face authentication, with transmission and computation durations on SCs in the range of 2s respectively 1s. To the best of our knowledge this work represents the first practical approach towards acceleration based gait MOC authentication.},
group = {ambience}}

Biometrics have become important for mobile authentication, e.g. to unlock devices before using them. One way to protect biometric information stored on mobile devices from disclosure is using embedded smart cards (SCs) with biometric match-on-card (MOC) approaches. However, computational restrictions of SCs also limit biometric matching procedures. We present a mobile MOC approach that uses offline training to obtain authentication models with a simplistic internal representation in the final trained state, wherefore we adapt features and model representation to enable their usage on SCs. The pre-trained model can be shipped with SCs on mobile devices without requiring retraining to enroll users. We apply our approach to acceleration based mobile gait authentication as well as face authentication and compare authentication accuracy and computation time of 16 and 32 bit Java Card SCs. Using 16 instead of 32 bit SCs has little impact on authentication performance and is faster due to less data transfer and computations on the SC. Results indicate 11.4% and 2.4-5.4% EER for gait respectively face authentication, with transmission and computation durations on SCs in the range of 2s respectively 1s. To the best of our knowledge this work represents the first practical approach towards acceleration based gait MOC authentication.

@INPROCEEDINGS{Kazuya_2018_PerCom,
author={Kazuya Ohara and Takuya Maekawa and Stephan Sigg and Moustafa Youssef},
booktitle={2018 IEEE International Conference on Pervasive Computing and Communication (WiP)},
title={Preliminary Investigation of Position Independent Gesture Recognition Using Wi-Fi CSI},
year={2018},
month={March},
group = {ambience}}

@article{hintze2017large,
  title={A Large-Scale, Long-Term Analysis of Mobile Device Usage Characteristics},
  author={Hintze, Daniel and Hintze, Philipp and Findling, Rainhard D and Mayrhofer, Ren{\'e}},
  journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies},
  volume={1},
  number={2},
  pages={13},
  year={2017},
  publisher={ACM},
  abstract = {Today, mobile devices like smartphones and tablets have become an indispensable part of people’s lives, posing many new questions e.g., in terms of interaction methods, but also security. In this paper, we conduct a large scale, long term analysis of mobile device usage characteristics like session length, interaction frequency, and daily usage in locked and unlocked state with respect to location context and diurnal pattern. Based on detailed logs from 29,279 mobile phones and tablets representing a total of 5,811 years of usage time, we identify and analyze 52.2 million usage sessions with some participants providing data for more than four years. Our results show that context has a highly significant effect on both frequency and extent of mobile device usage, with mobile phones being used twice as much at home compared to in the office. Interestingly, devices are unlocked for only 46% of the interactions. We found that with an average of 60 interactions per day, smartphones are used almost thrice as often as tablet devices (23), while usage sessions on tablets are three times longer, hence are used almost for an equal amount of time throughout the day. We conclude that usage session characteristics differ considerably between tablets and smartphones. These results inform future approaches to mobile interaction as well as security.},
  doi = {10.1145/3090078},
  url_Paper = {http://ambientintelligence.aalto.fi/findling/pdfs/publications/Hintze_17_LargeScaleLong.pdf},
group = {ambience}}

Today, mobile devices like smartphones and tablets have become an indispensable part of people’s lives, posing many new questions e.g., in terms of interaction methods, but also security. In this paper, we conduct a large scale, long term analysis of mobile device usage characteristics like session length, interaction frequency, and daily usage in locked and unlocked state with respect to location context and diurnal pattern. Based on detailed logs from 29,279 mobile phones and tablets representing a total of 5,811 years of usage time, we identify and analyze 52.2 million usage sessions with some participants providing data for more than four years. Our results show that context has a highly significant effect on both frequency and extent of mobile device usage, with mobile phones being used twice as much at home compared to in the office. Interestingly, devices are unlocked for only 46% of the interactions. We found that with an average of 60 interactions per day, smartphones are used almost thrice as often as tablet devices (23), while usage sessions on tablets are three times longer, hence are used almost for an equal amount of time throughout the day. We conclude that usage session characteristics differ considerably between tablets and smartphones. These results inform future approaches to mobile interaction as well as security.

@inproceedings{findling2016mobile,
  title={Mobile gait match-on-card authentication from acceleration data with offline-simplified models},
  author={Findling, Rainhard Dieter and H{\"o}lzl, Michael and Mayrhofer, Ren{\'e}},
  booktitle={Proceedings of the 14th International Conference on Advances in Mobile Computing and Multi Media},
  pages={250--260},
  year={2016},
  organization={ACM},

group = {ambience}}

@article{findling2016shakeunlock,
  title={Shakeunlock: Securely transfer authentication states between mobile devices},
  author={Findling, Rainhard Dieter and Muaaz, Muhammad and Hintze, Daniel and Mayrhofer, Ren{\'e}},
  journal={IEEE Transactions on Mobile Computing},
  volume={16},
  number={4},
  pages={1163--1175},
  year={2016},
  publisher={IEEE}
}

@inproceedings{Nguyen:2015:BAR:2829875.2829930,
 author = {Le Ngu Nguyen and Rodr\'{\i}guez-Mart\'{\i}n, Daniel and Catal\`{a}, Andreu and P{\'e}rez-L\'{o}pez, Carlos and Sam\`{a}, Albert and Cavallaro, Andrea},
 title = {Basketball Activity Recognition Using Wearable Inertial Measurement Units},
 booktitle = {Proceedings of the XVI International Conference on Human Computer Interaction},
 series = {Interacci\ón '15},
 year = {2015},
 isbn = {978-1-4503-3463-1},
 articleno = {60},
 numpages = {6},
 doi = {10.1145/2829875.2829930},
group = {ambience}}

@inproceedings{hintze2015confidence,
  title={Confidence and risk estimation plugins for multi-modal authentication on mobile devices using cormorant},
  author={Hintze, Daniel and Muaaz, Muhammad and Findling, Rainhard D and Scholz, Sebastian and Koch, Eckhard and Mayrhofer, Ren{\'e}},
  booktitle={Proceedings of the 13th International Conference on Advances in Mobile Computing and Multimedia},
  pages={384--388},
  year={2015},
  organization={ACM},
group = {ambience}}

@inproceedings{hintze2015cormorant,
  title={Cormorant: towards continuous risk-aware multi-modal cross-device authentication},
  author={Hintze, Daniel and Findling, Rainhard D and Muaaz, Muhammad and Koch, Eckhard and Mayrhofer, Ren{\'e}},
  booktitle={Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers},
  pages={169--172},
  year={2015},
  organization={ACM},
group = {ambience}}

@inproceedings{findling2015towards,
  title={Towards device-to-user authentication: Protecting against phishing hardware by ensuring mobile device authenticity using vibration patterns},
  author={Findling, Rainhard Dieter and Mayrhofer, Rene},
  booktitle={Proceedings of the 14th International Conference on Mobile and Ubiquitous Multimedia},
  pages={131--135},
  year={2015},
  organization={ACM},
group = {ambience}}

@inproceedings{mayrhofer2014optimal,
  title={Optimal derotation of shared acceleration time series by determining relative spatial alignment},
  author={Mayrhofer, Rene and Hlavacs, Helmut and Findling, Rainhard Dieter},
  booktitle={Proceedings of the 16th International Conference on Information Integration and Web-based Applications \& Services},
  pages={71--78},
  year={2014},
  organization={ACM},
group = {ambience}}

@inproceedings{hintze2014diversity,
  title={Diversity in locked and unlocked mobile device usage},
  author={Hintze, Daniel and Findling, Rainhard D and Muaaz, Muhammad and Scholz, Sebastian and Mayrhofer, Ren{\'e}},
  booktitle={Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication},
  pages={379--384},
  year={2014},
  organization={ACM},
  group = {ambience}
}

@inproceedings{He_2014_crowdsensing,
author={Jiyin He and Kai Kunze and Christoph Lofi and Sanjay K. Madria and Stephan Sigg},
title={Towards Mobile Sensor-Aware Crowdsourcing: Architecture, Opportunities and Challenges},
booktitle={UnCrowd 2014: DASFAA Workshop on Uncertain and Crowdsourced Data},
year={2014},
abstract={The recent success of general purpose crowdsourcing platforms like Amazon Mechanical Turk paved the way for a plethora of crowd-enabled applications and workflows. However, the variety of tasks which can be approached via such crowdsourcing platforms is limited by constraints of the web-based interface. In this paper, we propose mobile user interface clients. Switching to mobile clients has the potential to radically change the way crowdsourcing is performed, and allows for a new breed of crowdsourcing tasks. Here, especially the ability to tap into the wealth of precision sensors embedded in modern mobile hardware is a game changer. In this paper, we will discuss opportunities and challenges resulting from such a platform, and discuss a reference architecture},
doi={http://dx.doi.org/10.1007/978-3-662-43984-5_31},
group = {ambience}}

The recent success of general purpose crowdsourcing platforms like Amazon Mechanical Turk paved the way for a plethora of crowd-enabled applications and workflows. However, the variety of tasks which can be approached via such crowdsourcing platforms is limited by constraints of the web-based interface. In this paper, we propose mobile user interface clients. Switching to mobile clients has the potential to radically change the way crowdsourcing is performed, and allows for a new breed of crowdsourcing tasks. Here, especially the ability to tap into the wealth of precision sensors embedded in modern mobile hardware is a game changer. In this paper, we will discuss opportunities and challenges resulting from such a platform, and discuss a reference architecture

@article{mayrhofer2014determining,
  title={Determining Relative Spatial Alignment based on Shared Acceleration Time Series},
  author={Mayrhofer, Rene and Hlavacs, Helmut and Findling, Rainhard Dieter},
  year={2014},
group = {ambience}}

@inproceedings{hintze2014mobile,
  title={Mobile device usage characteristics: The effect of context and form factor on locked and unlocked usage},
  author={Hintze, Daniel and Findling, Rainhard D and Scholz, Sebastian and Mayrhofer, Ren{\'e}},
  booktitle={Proceedings of the 12th international conference on advances in mobile computing and multimedia},
  pages={105--114},
  year={2014},
  organization={ACM},
group = {ambience}}

@inproceedings{findling2014shakeunlock,
  title={Shakeunlock: Securely unlock mobile devices by shaking them together},
  author={Findling, Rainhard Dieter and Muaaz, Muhammad and Hintze, Daniel and Mayrhofer, Ren{\'e}},
  booktitle={Proceedings of the 12th International Conference on Advances in Mobile Computing and Multimedia},
  pages={165--174},
  year={2014},
  organization={ACM},
  group = {ambience}
}

@inproceedings{findling2013range,
  title={Range face segmentation: Face detection and segmentation for authentication in mobile device range images},
  author={Findling, Rainhard D and Wenny, Fabian and Holzmann, Clemens and Mayrhofer, Ren{\'e}},
  booktitle={Proceedings of International Conference on Advances in Mobile Computing \& Multimedia},
  pages={260},
  year={2013},
  organization={ACM},
group = {ambience}}

@article{dieter2013towards,
  title={Towards pan shot face unlock: Using biometric face information from different perspectives to unlock mobile devices},
  author={Dieter Findling, Rainhard and Mayrhofer, Rene},
  journal={International Journal of Pervasive Computing and Communications},
  volume={9},
  number={3},
  pages={190--208},
  year={2013},
  publisher={Emerald Group Publishing Limited},

group = {ambience}}

@inproceedings{findling2013towards,
  title={Towards secure personal device unlock using stereo camera pan shots},
  author={Findling, Rainhard D and Mayrhofer, Rene},
  booktitle={International Conference on Computer Aided Systems Theory},
  pages={417--425},
  year={2013},
  organization={Springer},
  group = {ambience}
}

@inproceedings{palipana2012localization,
  title={Localization of a mobile robot using ZigBee based optimization techniques},
  author={Palipana, S and Kapukotuwe, C and Malasinghe, U and Wijenayaka, P and Munasinghe, SR},
  booktitle={2012 IEEE 6th International Conference on Information and Automation for Sustainability},
  pages={215--220},
  year={2012},
  organization={IEEE},
group = {ambience}}

@inproceedings{findling2012towards,
  title={Towards face unlock: on the difficulty of reliably detecting faces on mobile phones},
  author={Findling, Rainhard D and Mayrhofer, Rene},
  booktitle={Proceedings of the 10th International Conference on Advances in Mobile Computing \& Multimedia},
  pages={275--280},
  year={2012},
  organization={ACM},
  group = {ambience}
}

%%% 2011 %%%

@ARTICLE{5710939,
author={Stephan Sigg and Rayan M. El Masri and Michael Beigl},
journal={IEEE Transactions on Mobile Computing},
title={Feedback-Based Closed-Loop Carrier Synchronization: A Sharp Asymptotic Bound, an Asymptotically Optimal Approach, Simulations, and Experiments},
year={2011},
volume={10},
number={11},
pages={1605-1617},
keywords={array signal processing;closed loop systems;feedback;optimisation;synchronisation;telecommunication control;wireless sensor networks;asymptotically optimal approach;closed-loop feedback-based distributed adaptive beamforming;feedback-based closed-loop carrier synchronization;individual carrier signal;optimization energy consumption;optimization time consumption;randomized black box optimization technique;sharp asymptotic bound;wireless sensor networks;Adaptive systems;Array signal processing;MIMO;Optimization;Receivers;Synchronization;Wireless sensor networks;Analysis of algorithms;wireless communication;wireless sensor networks.},
doi={10.1109/TMC.2011.21},
ISSN={1536-1233},
month={Nov},
group = {ambience}
}

@InProceedings{ 4032,
	title = "An asymptotically optimal approach to the distributed adaptive transmit beamforming in wireless sensor networks",
	booktitle = "Proceedings of the 16th European Wireless Conference",
	author = "Rayan Merched El Masri and Stephan Sigg and Michael Beigl",
	year = "2010",

group = {ambience}}

@InProceedings{ 4025,
	author = "Stephan Sigg and R.M. Masri and Julian Ristau and Michael Beigl",
	title = "Limitations, performance and instrumentation of closed-loop feedback based distributed adaptive transmit beamforming in WSNs",
	booktitle = "Fifth International Conference on Intelligent Sensors, Sensor Networks and Information Processing - Symposium on Theoretical and Practical Aspects of Large-scale Wireless Sensor Networks",
	year = "2009",
	group = {ambience}
	}

%%% 2008 %%%