Enhancing battery efficiency for pervasive health-monitoring systems based on electronic textiles

  • Authors:
  • Nenggan Zheng;Zhaohui Wu;Man Lin;Laurence Tianruo Yang

  • Affiliations:
  • College of Computer Science and Technology, Zhejiang University, Hangzhou, China;College of Computer Science and Technology, Zhejiang University, Hangzhou, China;theDepartment of Computer Science, St. Francis Xavier University, Antigonish, Canada;theDepartment of Computer Science, St. Francis Xavier University, Antigonish, Canada

  • Venue:
  • IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
  • Year:
  • 2010

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Abstract

Electronic textiles are regarded as one of the most important computation platforms for future computer-assisted health-monitoring applications. In these novel systems, multiple batteries are used in order to prolong their operational lifetime, which is a significant metric for system usability. However, due to the nonlinear features of batteries, computing systems with multiple batteries cannot achieve the same battery efficiency as those powered by a monolithic battery of equal capacity. In this paper, we propose an algorithm aiming to maximize battery efficiency globally for the computer-assisted health-care systems with multiple batteries. Based on an accurate analytical battery model, the concept of weighted battery fatigue degree is introduced and the novel battery-scheduling algorithm called predicted weighted fatigue degree least first (PWFDLF) is developed. Besides, we also discuss our attempts during search PWFDLF: a weighted round-robin (WRR) and a greedy algorithm achieving highest local battery efficiency, which reduces to the sequential discharging policy. Evaluation results show that a considerable improvement in battery efficiency can be obtained by PWFDLF under various battery configurations and current profiles compared to conventional sequential and WRR discharging policies.