Widespread acceptance and use of personalized mobile devices facilitates the provision of the ubiquitous mobile communications and computing applications that enable the human Quality of Life (QoL) improvement. However, there are multiple human factors influencing these applications, stemming from their users' needs, expectations, and ways of maximizing their experience and the impact on their QoL. For a successful adoption of these applications, our mobile technologies lab, mQoL, employs an iterative, user-centric, Living Lab approach for the applications' design and evaluation. This paper introduces the concept of mobile technologies for QoL specifically aiming to first understand the individual's QoL from information available in mobile devices, i.e., assess physical, psychological, social or environmental aspects of a daily life of an individual, and, based on this understanding, provide services to improve this individual's QoL. This paper also explains the methodological aspects of our research, including transdisciplinarity aspects, and delineates the research challenges for a Living Lab approach at large.

The emerging interactive mobile applications rely the effectiveness of their delivery and their users quality of experience (QoS) on the performance of the underlying heterogeneous networking infrastructures, i.e., their quality of service (QoS). However, the performance that the users will be provided with is a priori unknown, depending on their specific context (e.g., location, time, operator). Moreover, some interactive applications require a particular QoS to be provided, in order to assure their users QoE, e.g., a conversational voice (VoIP) and video-session requires Round Trip Time (RTT) a no higher than 150 ms. In this paper we report the results from a six-months field study involving 15 users of three Swiss network operators where we quantified their networks QoS and context on a minute basis. We then analyse the duration of time intervals and context in which the users are provided with `VoIP-friendly RTT', thus emulating VoLTE. On average, in daily life environments, a mobile user would be able to use a VoIP for 8 minutes for all operators and would need to wait for 3 minutes in between the calls. These numbers reduce to 2-3 minutes each while user is a highly mobile state; only 20% of the VoIP calls over 3G/4G would be able to last 19 minutes. We discuss the implications for mobile network operators focusing on their intend for the deployment of VoLTE services, implications for users themselves, as well as implications for service providers striving to maximise users interactive mobile applications experience.

Use of public transportation is on a rise, enabling greener cities and improvement of the quality of life of the city residents. The challenge for the public transportation system is that its demands are non linear and the passengers' experience depends on many human factors. UnCrowdTPG is a mobile service and a research platform that has been developed for understanding the mobility patterns of the users of public transportation services in Geneva (TPG), as well as their experience with respect to the crowds perceived aboard the vehicles. UnCrowdTPG models algorithmically this experience using crowdsourcing techniques and it provides to the individual estimations of the future expected experiences aboard, thus enabling them to better plan their journeys with respect to the crowds. In this paper we position the UnCrowdTPG service as a research platform. Additionally we discuss the research questions in areas like local governance and public healthcare supported by the data analysed collectively from all UnCrowdTPG users. We also discuss the challenges for implementing the truly smart transportation system in a small-sized European city like Geneva.

People-centric sensing using people's smartphones offers new research opportunities for large case studies. It presents many challenges, e.g., efficient capture of person's mobility, understanding of context changes and preservation of user privacy. We propose an accurate and energy-efficient method able to capture user's mobility, thus the context changes, while preserving his/her privacy. Our solution can be applied to systems that aim to efficiently sense context on smartphones to study large scale phenomena or perform location management.