Lightweight clustering in wireless sensor-actuator networks on obstructed environments

Wireless sensor-actuator networks (WSANs) can be useful to cope with the connectivity limitations of sparse networks by allowing powerful and mobile actuators periodically collect data from sensors. We propose a low-overhead algorithm that takes advantage of any potential connectivity present in sensors to form clusters that can expose single collection points, therefore, optimizing actuator data collection rates. No prior knowledge assumptions on the location of sensors, localization algorithms, or environment conditions are made in the design of the algorithm. Environment exploration is introduced as well as self-correcting tour mechanisms. Detailed simulations of high level statistical accuracy support our clustering approach and demonstrate the critical design issues of the algorithm.