| Summary: | In past decades, to achieve energy-efficient communication, many MAC protocols have been proposed for wireless sensor networks (WSNs). Particularly, asynchronous MAC protocol based on low power listening (LPL) scheme is very attractive in duty-cycled WSNs: it reduces the energy wasted by idle listening. In LPL scheme, a sensor node wakes up at every polling interval to sample the channel. If the channel is busy, the sensor node will stay in wake-up mode for receiving the data packet. Otherwise, it goes to sleep and saves power. However, wrong choice of polling interval in LPL scheme causes unexpected energy dissipation. This paper focuses on the polling interval adaptation strategy in LPL scheme with the aim of maximizing energy efficiency, defined as the number of packets delivered per energy unit. We propose a novel polling interval adaptation algorithm based on stochastic learning automata, where a sensor node dynamically adjusts its polling interval. Furthermore, our simulation results demonstrate that the polling interval asymptotically converges to the optimal value.
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