Adaptive Sampling for Marine Microorganism Monitoring

We describe the design and construction of an underwater sensor actuator network to detect extreme temperature gradients. We are motivated by the fact that regions of sharp temperature change (thermoclines) are a breeding ground for certain marine microorganisms. We present a distributed algorithm using local communication based on binary search to find a thermocline by using a mobile sensor network. Simulations and experiments using a mote test bed demonstrate the validity of this approach. We also discuss the improvement in energy efficiency using a submarine robot as a data mule. Comparisons between experimental data with and without the data mule show that there are considerable energy savings in the sensor network due to the data mule