Abstract:
Wireless Body Area Network (WBAN) is a time critical remote network where various
physiological information of patients are sent through this sensitive network satisfying
stringent quality of service (QoS) requirements. The performance of WBAN
may degrade severely due to the coexistence problem which is a well known incident
where the overlapped frequency bands are shared by di erent wireless channel access
technologies. However, the coexistence problem of WBAN is classi ed into two types
which are addressed as mutual and cross interference. A WBAN may dynamically
coexist with other WBANs, and thus they may su er from signi cant performance
deterioration because of interference among themselves which is known as mutual
interference. Apart from the lack of available spectrum, the technologies used by
the WBAN as well as other devices may operate in the same 2.4 GHz unlicensed
industrial, scienti c, and medical (ISM) bands which is de ned as cross interference.
In order to guarantee reliable communication for sensor nodes of WBAN, it requires
the prediction of coexistence condition. Existing research works de ne three different
coexistence mitigation schemes (beacon shifting, channel hopping, and active
superframe interleaving) to handle coexistence conditions. However, there is no speci
c detailed algorithm for these coexistence mitigation and handling schemes.
In order to overcome the shortcomings of coexistence mitigation schemes in the
WBAN and to satisfy requirements of WBANs, this thesis proposes two di erent
coexistence mitigation schemes. The rst scheme is to mitigate mutual interference
using fuzzy logic based algorithm where the state of the sensor nodes is detected.
Packet Error Rate (PER), Signal to Interference Noise Ratio (SINR) and Received
Signal Strength Indicator (RSSI) are the key parameters as membership function
of this algorithm for detecting the state of nodes. Moreover, the second scheme focuses on channel agility technique to mitigate the cross interference, where nonoverlapping
free channel is predicted based on Naive Bayes Classi er algorithm.
PER, RSSI, SINR and previous state of the channel are the attributes of this classi-
er in order to predict the state of the channel. A simulation model using Castalia
and Matlab is designed to show the e ectiveness of the proposed schemes. Simulation
results show that our proposed techniques e ectively mitigate the coexistence
problem in WBAN scenarios.
