Keynote


Andreas Terzis

Andreas Terzis is an Assistant Professor in the Department of Computer Science at Johns Hopkins University, where he heads the Hopkins InterNetworking Research (HiNRG) Group. His research interests are in the broad area of wireless sensor networks, including protocol design, system support, and data management. Dr. Terzis is a recipient of the NSF CAREER award.


Abstract

MEDiSN: Medical Emergency Detection in Sensor Networks

Staff shortages and an increasingly aging population are straining the ability of emergency departments to provide high quality care. At the same time, there is a growing concern about the hospitals’ ability to provide effective care during disaster events. For these reasons, tools that automate patient monitoring have the potential to greatly improve efficiency and quality of health care. Towards this goal, we have developed MEDiSN, a wireless sensor network for monitoring patients’ physiological data in hospitals and during disaster events. MEDiSN comprises Physiological Monitors (PMs) which are custom-built, patient-worn motes that sample, encrypt, and sign physiological data and Relay Points (RPs) that self-organize into a multi-hop wireless backbone for carrying physiological data. Moreover, MEDiSN includes a back-end server that persistently stores medical data and presents them to authenticated GUI clients. The combination of MEDiSN’s two-tier architecture and optimized rate control protocols allows it to address the compound challenge of reliably delivering large volumes of data while meeting the application’s QoS requirements. Results from extensive simulations, testbed experiments, and multiple pilot hospital deployments show that MEDiSN can scale from tens to at least five hundred PMs, effectively protect application packets from congestive and corruptive losses, and deliver medically actionable data. I will also present results that characterize the radio channel conditions inside a busy emergency department and describe our second generation medical sensing platform.