title: Integrated scheduling algorithm for personalized disease management applications.
abstract: The rapid escalation of American health care costs compels a new approach to chronic disease. Personalizing chronic disease management can be improved by using biosensors and advanced communication technology. Recent advances in miniature wireless sensors supported by ubiquitous computing have fostered a growth of interest in wellness and illness management based on distributed sensor networks. A variety of factors need to be considered for effective integrated scheduling scheme that can assign sensing, computation, and communication task to different sensor nodes to assist them set priority of the task at hand. In this paper, we present a framework for integrated scheduling algorithm for sensing, computation, and communication tasks within a distributed intelligent sensor network. Preliminary simulation results are presented for linear daisy chain network with different number of sensor nodes that consider various sensing and communication speeds.
authors.affiliation: Houston Univ., College of Tech., TX 77204, USA. xyuan@uh.edu
authors.forename: X
authors.initials: X
authors.lastname: Yuan
authors.forename: M
authors.initials: M
authors.lastname: Moges
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D002908:Chronic Disease / Q000628:therapy*; D003195:Computer Communication Networks; D003198:Computer Simulation; D003201:Computers; D019468:Disease Management; D004867:Equipment Design; D006801:Humans; D015233:Models, Statistical; D012984:Software; D017216:Telemedicine / Q000295:instrumentation* / Q000379:methods; D013686:Telemetry / Q000295:instrumentation / Q000379:methods*; D014159:Transducers