Performance of TDMA scheduling algorithms in the presence of data correlation in sensor networks

TDMA scheduling for data gathering in wireless sensor networks can potentially save energy by eliminating collisions and avoiding idle listening due to its built in duty cycle. Furthermore, temporal and spatial correlation in the sensed data gives room for better delay and energy efficiency. Several TDMA scheduling schemes have been suggested in the literature. However the impact of data correlation on those schemes is not widely reported. In this paper we study the effect of data aggregation on energy and delay performance of two scheduling schemes, namely, Interleaved and Non-interleaved scheduling. Through simulation we show that non-interleaved scheduling utilizes data aggregation more efficiently to reduce its delay by a factor of 2.13 to 4.9 compared to interleaved scheduling. However, its overall energy savings is minimal due to its short duty cycle. Interleaved scheduling shows a balanced performance in terms of energy and delay at different levels of data correlation. That could make it a more desirable choice for a wider range of sensor networks applications.