The coexistence problem occurs when a single wireless body area network (WBAN) is located within a multiple-WBAN
environment. This causes WBANs to suffer from severe channel interference that degrades the communication performance of each
WBAN. Since a WBAN handles vital signs that affect human life, the detection or prediction of coexistence condition is needed to
guarantee reliable communication for each sensor node of a WBAN. Therefore, this paper presents a learning-based algorithm to
efficiently predict the coexistence condition in a multiple-WBAN environment. The proposed algorithm jointly applies PRR and
SINR, which are commonly used in wireless communication as a way to measure the quality of wireless connections. Our extensive
simulation study using Castalia 3.2 simulator based on the OMNet++ platform shows that the proposed algorithm provides more
reliable and accurate prediction than existing methods for detecting the coexistence problem in a multiple-WBAN environment.This is the publisher’s final pdf. The published article is copyrighted by the author(s) and published by the Hindawi Publishing Corporation. The published article can be found at: http://www.hindawi.com/journals/ijdsn/
