Abstract:
Sensor nodes attached with the human body continuously sense
and transfer real-time raw data to the coordinator node. The continuous process of sensing generally produces redundant data, which
drains energy of the nodes. We study real medical dataset of diabetes, heart rate and body temperature to analyze redundancy in
sensed data. We also collected and studied the human pulse rate of
different subjects gathered by using pulse sensor for the duration
of 20 minutes. This study confirms that redundancy is present in
all datasets. Therefore, we propose an auto-regression based prediction algorithm to decide the minimum sampling frequency (rate
with which node will sense data) of sensor nodes. This sampling
frequency could provide reliable medical information with tolerable
prediction error. Additionally, the line-of-sight of communications
(LOS) also plays a significant role in the energy consumption of
the network. We design Scenario 1 (human standing) and Scenario
2 (walking posture) to study the effect of LOS on the lifetime of the
network. In order to address the non-LOS communication, we propose an opportunistic communication protocol for intra-WBANs.
The protocol exploits human mobility and data redundancy to maximize the lifetime of the network. Our proposed approach ensures
that the remaining lifetime of the nodes is higher compared to other
existing relay-based intra-WBAN communication protocols, such
as SIMPLE and ATTEMPT. The proposed method
