E-health has raised a great deal of expectations on improving the quality of health services while simultaneously enabling health services cost reductions. To advance towards those visions, it is imperative to gain the trust of the involved stakeholders, doctors and other medical personnel, patients, families, health care providers and regulators. Even though one critical requirement is interoperability among the various systems involved, currently existing solutions are still vertical silos to a large extent. In this paper, we present an E-health solution that results from the integration of components that comply with rising standards at the various levels of the ICT infrastructure: Machine-to-Machine (M2M) communications for interconnecting devices and services, Health Level 7 (HL7) for communicating with health platforms and openEHR for data semantics, storing and making data available. Concretely, we provide an interoperable and extensible e-health service following these three uprising standards and present the architecture design. We map the service to the various components of the infrastructure building blocks, thus demonstrating how the integration can be successfully accomplished. We are currently developing a prototype solution to be used in a pilot project with 15 elders.

We expose an USB vulnerability in some vendors’ customization of the android system, where the serial AT commands processed by the cellular modem are extended to allow other functionalities. We target that vulnerability for the specific vendor system and present a proof of concept of the attack in a realistic scenario environment. For this we use an apparently inoffensive smartphone charging station like the one that is now common at public places like airports. We unveil the implications of such vulnerability that culminate in flashing a compromised boot partition, root access, enable adb and install a surveillance application that is impossible to uninstall without re-flashing the android boot partition. All these attacks are done without user consent or knowledge on the attacked mobile phone.

Mobile AdHoc NETworks (MANETs) have been identified as a key emerging technology for scenarios in which IEEE 802.11 or cellular communications are either infeasible, inefficient, or cost-ineffective. Smartphones are the most adequate network nodes in many of these scenarios, but it is not straightforward to build a network with them. We extensively survey existing possibilities to build applications on top of ad-hoc smartphone networks for experimentation purposes, and introduce a taxonomy to classify them. We present AdHocDroid, an Android package that creates an IP-level MANET of (rooted) Android smartphones. AdHocDroid supports standard TCP/IP applications, providing real smartphone IEEE 802.11 MANET and the capability to easily change the routing protocol. We validate the MANET with off-the-shelf applications and experimental performance evaluation, including network metrics and battery discharge rate.

We describe the architecture, problems and lessons learned from building a Health Kiosk from commercial, off-the shelf Personal Health Devices and a computer with a touch-screen interface. The kiosk is used autonomously by patients to measure vital data prior to a consultation, in the scope of a population screening, or for routinely monitoring. The prototype was tested in multiple events with different user groups, aided by observer reporting and user questionnaires to assess problems and difficulties with the interface and device usage.

Diabetes management is a complex and a sensible problem as each diabetic is a unique case with particular needs. The optimal solution would be a constant monitoring of the diabetic's values and automatically acting accordingly. We propose an approach that guides the user and analyses the data gathered to give individual advice. By using data mining algorithms and methods, we uncover hidden behaviour patterns that may lead to crisis situations. These patterns can then be transformed into logical rules, able to trigger in a particular context, and advise the user. We believe that this solution, is not only beneficial for the diabetic, but also for the doctor accompanying the situation. The advice and rules are useful input that the medical expert can use while prescribing a particular treatment. During the data gathering phase, when the number of records is not enough to attain useful conclusions, a base set of logical rules, defined from medical protocols, directives and/or advice, is responsible for advise and guiding the user. The proposed system will accompany the user at start with generic advice, and with constant learning, advise the user more specifically. We discuss this approach describing the architecture of the system, its base rules and data mining component. The system is to be incorporated in a currently developed diabetes management application for Android.