mobile networking; quality of experience; human factors; mobile Internet; mobility; quality of service; machine learning
Symul Laura, Wac Katarzyna, Hillard Paula, Salathé Marcel (2019), Assessment of menstrual health status and evolution through mobile apps for fertility awareness, in npj Digital Medicine
, 2(1), 64-64.
Wulfovich Sharon, Fiordelli Maddalena, Rivas Homero, Concepcion Waldo, Wac Katarzyna (2019), “I Must Try Harder”: Design Implications for Mobile Apps and Wearables Contributing to Self-Efficacy of Patients With Chronic Conditions, in Frontiers in Psychology
, 10, 1.
Masi Alexandre De, Wac Katarzyna (2019), Predicting Quality of Experience of Popular Mobile Applications from a Living Lab Study (Best Student Paper Award), in 2019 Eleventh International Conference on Quality of Multimedia Experience (QoMEX)
, Berlin, GermanyIEEE, USA.
Manea Vlad, Berrocal Allan, De Masi Alexandre, Møller Naja Holten, Wac Katarzyna, Bayer Hannah, Lehmann Sune, Ashley Euan (2019), LDC: International workshop on longitudinal data collection in human subject studies, in the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2019 ACM
van der Mei Rob, van den Berg Hans, Ganchev Ivan, Tutschku Kurt, Leitner Philipp, Lassila Pasi, Burakowski Wojciech, Liberal Fidel, Arvidsson Åke, Hoβfeld Tobias, Wac Katarzyna, Melvin Hugh, Grbac Tihana Galinac, Haddad Yoram, Key Peter (2018), Autonomous Control for a Reliable Internet of Services, Springer International Publishing, Cham, 107.
Boillat Thomas, Rivas Homero, Wac Katarzyna (2018), “Healthcare on a Wrist”: Increasing Compliance Through Checklists on Wearables in Obesity (Self-)Management Programs, Springer International Publishing, Cham, 65.
Wac Katarzyna (2018), From Quantified Self to Quality of Life, Springer International Publishing, Cham, 83.
RivasHomero, WacKatarzyna (2018), Digital Health
, Springer International Publishing, Cham.
Unknown, van Berkel Niels, Hosio Simo, Goncalves Jorge, Wac Katarzyna, Kostakos Vassilis, Cox Anna (2018), MHC: International Workshop on Mobile Human Contributions: Opportunities and Challenges, in the 2018 ACM International Joint Conference and 2018 International Symposium
ThuemmlerC., WacK. (2018), White Paper: A New Generation of eHealth Systems Powered by 5G, Wireless World Research Forum Working Group on “e/m-Health and Wearables” Vertical Industries Platform (VIP)
, WWRF, Switzerland.
De Masi Alexandre, Wac Katarzyna (2018), You're Using This App for What?A mQoL Living Lab Study, in the 2018 ACM International Joint Conference and 2018 International Symposium
, Singapore, SingaporeIEEE, USA.
Wac Katarzyna, Rivas Homero, Fiordelli Maddalena (2017), Quality-of-Life Technologies, in Computer
, 50(3), 14-19.
Kaup Fabian, Michelinakis Foivos, Bui Nicola, Widmer Joerg, Wac Katarzyna, Hausheer David (2016), Assessing the Implications of Cellular Network Performance on Mobile Content Access, in IEEE Transactions on Network and Service Management
, 13(2), 168-180.
WacKatarzyna, CummingsMark, DeyJ (2016), E2eUberIM: end-to-end service management framework for anything-as-a-service, in IEEE Communications Magazine
, 54(3), 54.
Ballesteros Luis Guillermo Martinez, Ickin Selim, Fiedler Markus, Markendahl Jan, Tollmar Konrad, Wac Katarzyna (2016), Energy saving approaches for video streaming on smartphone based on QoE modeling, in 2016 13th IEEE Annual Consumer Communications & Networking Conference (CCNC)
, Las Vegas, NV, USAIEEE , USA.
De Masi Alexandre, Ciman Matteo, Gustarini Mattia, Wac Katarzyna (2016), mQoL smart lab: Quality of life living lab for interdisciplinary experiments, in the 2016 ACM International Joint Conference
, Heidelberg, GermanyACM, USA.
WacKatarzyna, De MasiAlexandre (2016), mQoL: Experimental Methodology for Longitudinal, Continuous Quality of Life Assessment via Unobtrusive, Context-Rich Mobile Computing in Situ
, ISQOLS, USA.
Kaup Fabian, Michelinakis Foivos, Bui Nicola, Widmer Joerg, Wac Katarzyna, Hausheer David (2015), Behind the NAT- A measurement based evaluation of cellular service quality, in 2015 11th International Conference on Network and Service Management (CNSM)
, BarcelonaIEEE , USA.
Wac Katarzyna, Pinar Gerardo, Gustarini Mattia, Marchanoff Jerome (2015), More mobile & not so well-connected yet: Users' mobility inference model and 6 month field study, in 2015 7th International Congress on Ultra Modern Telecommunications and Control Systems and Workshops
, Brno, Czech RepublicIEEE , USA.
WacKatarzyna, Quality of Life Technologies, in Gellman M, Turner J. (ed.), Springer, MY, USA, 100.
On a growing scale, we use mobile applications and services ‘on the move’ (e.g., in a car or a train) to fulfil critical goals that rely on prompt and to-the-point mobile interactions ranging from last minute information about the important meeting we are about to join, to a health procedure that can save our fellow train passenger’s life. The challenge arises from the fact that the service-ability of these applications and our experience rely on the connection quality provided by the underlying mobile networking infrastructure, supporting the data exchange between the mobile device we use (e.g., a smartphone) and some remote application server(s) (situated somewhere “in the cloud”). This underlying mobile networking infrastructure we denote as the Mobile Internet (MI), and its connection quality as MIQ. As research studies show, the MIQ is a best-effort level quality. Nevertheless, the users develop high MIQ expectations; the more they value the utility of the mobile application at hand, and the more critical the use of this application is to their personal goal, the more critical they become of accepting a given experienced MIQ. It is especially so, if the experienced MIQ is surprisingly different from their expected MIQ. The MIQ contributes to the overall user experience (QoE); the QoE also embraces the service design (user interface) and service information quality, which are not treated in here. The MIQ is defined by its speed, accuracy, dependability of the mobile connection establishment and then the subsequent MIQ stability. We denote these as Quality of Service (QoS) criteria for MI, which further depend on the user’s complex context (location, time, mobility speed and heading, access network, etc.). We present research project on developing timely and accurate contextual MIQ model that predict MIQ for mobile users in different context. We research the predictive accuracy, speed and computational complexity of the MIQ model, as well as we focus on precision and recall of this model to predict MIQ ‘surprises’, via modelling and predicting unexpected (by the user) MIQ conditions (e.g., bad access network). The model is based on a proactive assessment of QoS and MIQ context, and predictive modelling of MIQ for any future point in time. The MIQmodel is user-centric and enables to proactively control interactive applications, in order to meet the user’s expected MIQ levels and enable them to meet their goals. It contrasts with the current operator-based approaches - focusing on providing access to their wireless networks, yet not assuring the user’s experience for mobile applications. We evaluate our research with real network quality measurements data collected ‘in situ’ with a set of real mobile users ‘on the move’. The proposed project lies at the frontier of Human-Computer Interaction (HCI), pervasive and ubiquitous computing, network performance management, machine learning and predictive analytics. The project is timely - there is a significant need for research and commercial interest for mobile networking solutions, as smartphones became personal communication and computing devices affordable for the masses. Additionally, there is a growing number of applications providing time-constrained services to their mobile users, relying the success of their delivery, hence their users’ experience, on the ‘best-effort’ quality of the underlying wireless networks. To conduct the proposed research we have the necessary equipment including latest smartphones as well as infrastructure for collecting smartphone-based data from mobile users called mQoL Living Lab (mQoL-LLab). So far our research focused on the ‘basic’ system (QoSIS.com) for collecting network performance data from mobile users and using simple machine learning on that data. The next step taken in the project is to do in depth research and model the user’s experience and expectations in context, and its relation with mobile networking. Mr Fanourakis - the PhD student proposed for this project has already started some initial research towards it. There are various potential exploitation areas of the MIQmodel research, including novel application development leveraging MIQmodel in their application quality management, and mobile networking solutions by network operators, leveraging the model towards enhancing their users’ experience ‘on the move’. The MIQmodel will be initially exploited via our on-going research projects and collaborations in the area of mobile networking, especially applied in healthcare, where the lack of quality assurance for mobile services can even result in endangering the patient’s life. It is our moral obligation to ensure quality in this emerging area, propelled by the exponential needs of the ageing, and chronically ill population.