  Wireless body sensors are becoming popular in healthcare applications. Since
they are either worn or implanted into human body, these sensors must be very
small in size and light in weight. The energy consequently becomes an extremely
scarce resource, and energy conservation turns into a first class design issue
for body sensor networks (BSNs). This paper deals with this issue by taking
into account the unique characteristics of BSNs in contrast to conventional
wireless sensor networks (WSNs) for e.g. environment monitoring. A
prediction-based data transmission approach suitable for BSNs is presented,
which combines a dual prediction framework and a low-complexity prediction
algorithm that takes advantage of PID (proportional-integral-derivative)
control. Both the framework and the algorithm are generic, making the proposed
approach widely applicable. The effectiveness of the approach is verified
through simulations using real-world health monitoring datasets.
