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Emerging healthcare applications rely on personal mobile devices to monitor and transmit patient vital signs to hospital-backend servers for further analysis. However, these devices have limited resources that must be used optimally in order to meet the application user requirements (e.g. safety, usability, reliability, performance). This paper reports on a case study of a Chronic Obstructive Pulmonary Disease telemonitoring application delivered by the mobihealth system. This system relies on a commercial mobile device with multiple (wireless) Network Interfaces (NI). Our study focuses on how NI activation strategies affect the application end-to-end data delay (important in case of an emergency situation) and the energy consumption of the device (important for device sustainability while a patient is mobile). Our results show the trade-off between end-to-end delay and battery life-time achieved by various NI activation strategies, in combination with application-data flow adaptation for realtime and near real-time data transmission. For a given mobile device, our study shows an increase in battery life-time of 40- 90 %, traded against higher end-to-end data delay. The insights of our studies can be used for application-data flow adaptation aiming to increase battery life-time and device sustainability for mobile patients; which effectively increases the healthcare application usability.

Emerging healthcare applications rely on personal mobile devices to monitor patient vital signs and to send it to the hospitals-backend servers for further analysis. However, these personal mobile devices have limited resources that must be used optimally in order to meet the requirements of healthcare applications end-users: healthcare professionals and their patients. This paper reports on a case study of a cardiac telemonitoring application delivered by the so-called MobiHealth system. This system relies on a commercial personal mobile device with multiple (wireless) network interfaces (NI). The study focuses on how the choice of a NI affects the end-to-end application’s data delay (extremely important in case of patient’s emergency) and the energy consumption of the device (relating to the service sustainability while a patient is mobile). Our results show the trade-off between battery savings and the delay achieved by various NI activation strategies in combination with application-data flow adaptation. For a given mobile device, our study shows a gain of 40-90% in battery savings, traded against the higher delays (therefore applicable mainly in non-emergency cases). The insights of our studies can be used for application-data flow adaptation aiming at battery saving and prolonging device’s operation while patients being mobile.

In many cases, application-level measurements can be the only way for an application to evaluate and adapt to the performance offered by the underlying networks. Applications perceive heterogeneous networking environments spanning over multiple administrative domains as ��?black boxes��? being inaccessible for lower-level measurement instrumentation. However, application-level measurements can be inaccurate and differ significantly from the lower-level ones, amongst others due to the influence of the protocol stacks. In this paper we quantify and discuss such differences using the Distributed Passive Measurement Infrastructure (DPMI), with Measurement Points (MPs) instrumented with DAG 3.5E cards for the reference link-level measurements. We shed light on various impacts on timestamp accuracy of application-level measurements. Moreover, we quantify the accuracy of generating traffic with constant inter-packettimes (IPTs). The latter is essential for an accurate emulation of application-level streaming traffic and thus for obtaining realistic end-to-end performance measurements.

Inevitably, healthcare goes mobile. Recently developed mobile healthcare (i.e., m-health) services allow healthcare professionals to monitor mobile patient's vital signs and provide feedback to this patient anywhere at any time. Due to the nature of current supporting mobile service platforms, m-health services are delivered with a best-effort, i.e., there are no guarantees on the delivered Quality of Service (QoS). In this paper, we argue that the use of context information in an m-health service platform improves the delivered QoS. We give a first attempt to merge context information with a QoS-aware mobile service platform in the m-health services domain. We illustrate this with an epilepsy tele-monitoring scenario.

In our current connected wireless world, mobile devices are enabled to use various networking facilities. Although this enables mobile users to communicate any time and any place, it may also be very intrusive. There is a high need to manage the information stream a user receives on his/her mobile device. Context-awareness seems to be a promising way to manage this information stream and to provide the means to communicate at the right time in the right way. Current context-aware applications benefit from the user context (e.g. location information), however, they do not consider the quality of service (QoS) offered by various networks (i.e. only best-effort QoS is considered). The research discussed in this paper focuses on a QoS- and context-aware service infrastructure supporting the development of mobile applications in a heterogeneous network environment. We argue that the use of context information helps to better capture the user’s required QoS and improves the delivered QoS.

The introduction of third generation (3G) public wireless network infrastructures, such as the Universal Mobile Telecommunications System (UMTS), enable the development of innovative mobile services. For example, deploying m-health services which embed tele-monitoring and tele-treatment services become feasible with the role-out of 3G networks. These services allow healthcare professionals to monitor a mobile patient's vital signs and provide feedback to this patient anywhere and any time. The performance of m-health services perceived by end-users depends on the serviceableness of 3G networks to support these services. Hence, the performance of 3G networks is a critical factor for successful development of m-health services. In this paper, we present a methodology for measurements-based performance assessment of 3G networks that aim to support m-health services. This methodology has been applied to evaluate end-user perceived service performance, in relation to the performance (i.e. serviceableness) of a 3G network. In addition, we analyse the measurements with the purpose to improve the end-to-end delay characteristics of the tele-monitoring service as well as optimize the (derived) goodput behaviour of this 3G network. Our results show that the goodput behaviour is asymmetric and depends on a bearer assignment policy of the network. Based on our results we provide guidelines for the design of application protocols for m-health services and how these protocols deal with the changing performance behaviour of 3G networks.