Multipath TCP is a backwards-compatible TCP extension that enables using multiple network paths between two end systems for a single TCP connection, increasing performance and reliability. It can also be used to "shift" active connections from one network path to another without breakage. This feature is especially attractive on mobile devices with multiple radio interfaces, because it can be used to continuously shift active connections to the most energyefficient network path. This paper describes a novel method for deriving such a multipath scheduler using MPTCP that maximises energy savings. Based on energy models for the different radio interfaces as well as a continuously accumulated communication history of the device user, we compute schedulers for different applications by solving a Markov decision process offline. We evaluate these schedulers for a large number of random application models and selected realistic applications derived from measurements. Evaluations based on energy models for a mobile device with Wifi and 3G radio interfaces show that it performs comparably in terms of energy efficiency to a theoretically optimal omniscient oracle scheduler.

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Mobile phones have recently been used to collect largescale continuous data about human behavior. This people centric sensing paradigm is useful not only from a scientific point of view: Contextual user data has pragmatic value, too. Individuals whose data is collected in such long-term people centric sensing projects can be engaged in user centric design activities aiming to generate data driven services that benefit the end user. This paper demonstrates the value of such user centric approach. In a two-stage approach, we analyse mobile phone data to extract mobile phone usage categories. We then go on to interview the participants concerning their perceptions toward contextaware services. The two stages, combined as we present here, offer a clear value in terms of providing complementary insights, both to researchers and users, about the feasibility of and the expectations about personalized mobile services.

Background: Up to 90% of strokes could be prevented by effective treatment of the risk factors. However, there are major problems with the implementation of prevention. For example, only 40% of patients taking medication have blood pressure (BP) at treatment goals and 60% of patients with atrial fibrillation (AF) use anti-coagulant medication. Hypothesis: Remote home monitoring of risk factors after minor stroke or TIA may lead to better control of risk factors by increasing measurements and patient awareness and uncovering undetected risk factors. This pilot study investigates the feasibility of home monitoring of risk factors after minor stroke or TIA. Methods and Patients: Patients (n=30, mean age 57 yrs, range 34-79, 37% females) with recent minor stroke or TIA were supplied with a remote home monitoring system at discharge. The system comprised of a cloud backend for data storage and processing, patient user interface (UI), and wireless BP meter and light-weight EKG device with secured connection to clinician UI, through which BP and EKG could be followed real-time and individualized alarm limits could be set. EKG was automatically analyzed in the cloud backend to detect AF. The patients were contacted by phone at two weeks by stroke nurse and they returned the remote home monitoring system at final visit at three months. Additionally they were contacted if AF was detected or their BP required medication adjustment. Results: Twenty-nine (97%) patients completed the study. One patient discontinued due to unrelated serious illness. One patient did not follow the monitoring program. Of the remaining 28 patients (93%) with complete monitoring data, BP medication needed adjustment in 11 patients (39%) and new AF was detected in 3 patients (11%). Patients appraised that the home monitoring system was easy to use (score 8.6/10) and most would recommend it to peers (score 8.9/10). Conclusions: Remote home monitoring of risk factors after minor stroke or TIA is feasible and may be an efficient way to improve secondary prevention.