MEDiSN: Medical emergency detection in sensor networks

  • Authors:

  • Jeonggil Ko;Jong Hyun Lim;Yin Chen;Rvăzvan Musvaloiu-E;Andreas Terzis;Gerald M. Masson;Tia Gao;Walt Destler;Leo Selavo;Richard P. Dutton

  • Affiliations:

  • Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Johns Hopkins University, Baltimore, MD;Aid Networks, Rockville, MD;Aid Networks, Rockville, MD;University of Latvia, Baltimore, MD;University of Maryland Medical Center

  • Venue:
  • ACM Transactions on Embedded Computing Systems (TECS)
  • Year:
  • 2010

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Abstract

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 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.