Highly reliable energy-saving MAC for wireless body sensor networks in healthcare systems

  • Authors:
  • Begonya Otal;Luis Alonso;Christos Verikoukis

  • Affiliations:
  • Centre Tecnològic de Telecomunicacions de Catalunya, Castelldefels, Barcelona, Spain;Signal Theory & Communications Department, Universitat Politèècnica de Catalunya, Barcelona, Spain;Centre Tecnològic de Telecomunicacions de Catalunya, Castelldefels, Barcelona, Spain

  • Venue:
  • IEEE Journal on Selected Areas in Communications - Special issue on wireless and pervasive communications for healthcare
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

Abstract

Wireless Body Sensor Networks (BSNs) in health-care systems operate under conflicting requirements. These are the maintenance of the desired reliability and message latency of data transmissions, while simultaneously maximizing battery lifetime of individual body sensors. In doing so, the characteristics of the entire system, including physical, medium access control (MAC), and application layers have to be considered. The aim of this paper is to develop a new MAC model for BSNs to fulfill all these specific rigorous requirements under realistic medical settings. For that purpose, a novel cross-layer fuzzy-rule scheduling algorithm and energy-aware radio activation policies are introduced. The main idea is to integrate a fuzzy-logic system in each body sensor to deal with multiple cross-layer input variables of diverse nature in an independent manner. By being autonomously aware of their current condition, body sensors are able to demand a "collision-free" time slot, whenever they consider it strictly required (e.g. high system packet delay or low body sensor residual battery lifetime). Similarly, they may refuse to transmit, if there is a bad channel link, thus permitting another body sensor to do so. This results in improving the system overall performance. The proposed MAC model is evaluated by computer simulations in terms of quality of service and energy consumption under specific healthcare scenarios.