Truthful Channel Sharing for Self Coexistence of Overlapping Medical Body Area Networks

Gengfa Fang; Mehmet A Orgun; Rajan Shankaran; Eryk Dutkiewicz; Guanglou Zheng

doi:10.1371/journal.pone.0148376

Truthful Channel Sharing for Self Coexistence of Overlapping Medical Body Area Networks

PLoS One. 2016 Feb 4;11(2):e0148376. doi: 10.1371/journal.pone.0148376. eCollection 2016.

Authors

Gengfa Fang¹, Mehmet A Orgun², Rajan Shankaran², Eryk Dutkiewicz³, Guanglou Zheng¹

Affiliations

¹ Department of Engineering, Macquarie University, Sydney, NSW 2109, Australia.
² Department of Computing, Macquarie University, Sydney, NSW 2109, Australia.
³ School of Computing and Communications, University of Technology, Sydney, Broadway, NSW 2007, Australia.

Abstract

As defined by IEEE 802.15.6 standard, channel sharing is a potential method to coordinate inter-network interference among Medical Body Area Networks (MBANs) that are close to one another. However, channel sharing opens up new vulnerabilities as selfish MBANs may manipulate their online channel requests to gain unfair advantage over others. In this paper, we address this issue by proposing a truthful online channel sharing algorithm and a companion protocol that allocates channel efficiently and truthfully by punishing MBANs for misreporting their channel request parameters such as time, duration and bid for the channel. We first present an online channel sharing scheme for unit-length channel requests and prove that it is truthful. We then generalize our model to settings with variable-length channel requests, where we propose a critical value based channel pricing and preemption scheme. A bid adjustment procedure prevents unbeneficial preemption by artificially raising the ongoing winner's bid controlled by a penalty factor λ. Our scheme can efficiently detect selfish behaviors by monitoring a trust parameter α of each MBAN and punish MBANs from cheating by suspending their requests. Our extensive simulation results show our scheme can achieve a total profit that is more than 85% of the offline optimum method in the typical MBAN settings.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Models, Theoretical*
Monitoring, Ambulatory*
Wireless Technology*

Grants and funding

The work of MAO and RS was partially funded by a Macquarie University Research Development Grant (MQRDG). The work of GZ was partially funded by a Macquarie University Research Excellence Scholarship (MQRES). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. None of the authors received any other funding for the work reported in this paper.