An energy efficient MAC protocol for mobile in-vivo body sensor networks

Lin Lin*, Kai Juan Wong, Arun Kumar, Su Lim Tan, Soo Jay Phee

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

Body sensor networks (BSNs) is a challenging research area that have attracted many researchers in recent times. Examples of BSN applications include those that are used to detect vital signs of the human body. As these body sensor devices are typically powered by batteries that are difficult to replace, energy efficient MAC protocols play an important role in BSNs. In this paper, we propose a novel TDMA-based energy efficient MAC protocol for in-vivo communications between mobile nodes in BSNs using an uplink/downlink asymmetric network architecture. A TDMA scheduling scheme and changeable frame formats are also proposed. An adaptive power control scheme is designed especially for the mobile sensor scenarios presented. Simulation results based on the random mobility pattern show that the power consumption of the proposed MAC protocol is much lesser than that of the star topology TDMA. Furthermore, it is also shown that the latency performance can be improved by reducing the data slot duration.

Original languageEnglish
Title of host publicationICUFN 2011 - 3rd International Conference on Ubiquitous and Future Networks
Pages95-100
Number of pages6
DOIs
Publication statusPublished - 2011
Externally publishedYes
Event3rd International Conference on Ubiquitous and Future Networks, ICUFN 2011 - Dalian, China
Duration: Jun 15 2011Jun 17 2011

Publication series

NameICUFN 2011 - 3rd International Conference on Ubiquitous and Future Networks

Conference

Conference3rd International Conference on Ubiquitous and Future Networks, ICUFN 2011
Country/TerritoryChina
CityDalian
Period6/15/116/17/11

ASJC Scopus Subject Areas

  • Computer Networks and Communications

Keywords

  • body sensor networks
  • MAC protocol
  • TDMA
  • Wireless sensor networks

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