Efficient duty cycling through prediction and sampling in wireless sensor networks

We present BoostMAC, a CSMA-based MAC layer protocol for wireless sensor networks that provides an adjustable interface to achieve ultra-low power operation. To reach low power operation, we adaptively set two radio parameters. First, BoostMAC implements a preamble sampling scheme that allows a mote to dynamically set the length of its duty cycles. Second, we apply machine learning such that a sender can predict its destination’s channel polling time and set each outgoing packet’s preamble length accordingly. Our two improvements require no extra communication overhead, and each mote in the network implements simple, local models. Energy conservation is one of the most fundamental problems in sensor networks, and by optimizing low level operation at the MAC layer, sensor network applications realize an increase in overall performance. We choose habitat monitoring as a motivating sensor network application because it has unique MAC layer demands, such as fluctuating levels of network traffic. Static configuration at the MAC layer inhibits optimal energy conservation within habitat monitoring applications. We implemented BoostMAC in TOSSIM, and we compare its performance to B-MAC, a well-known low power MAC protocol with static behavior. We found that BoostMAC saves energy relative to B-MAC in bursty networks without affecting latency.