Energy efficiency is one of the most critical issues in wireless sensor networks, since the sensor nodes are usually battery powered. These energy-constrained sensor nodes are usually densely distributed in indoor environments, which leads to spatially correlated sensor data and low network efficiency. Thus, one way to improve energy efficiency is to reduce the redundancy caused by the correlated data. In this paper, a new sensor scheduling algorithm, based on data correlation, is proposed. The sensor nodes are clustered into groups by a new adaptive dual-metric K-means (DK-means) algorithm.

Within each group, the sensor nodes take turns to work as a group representative and transmit data to the sink. Thus, the energy consumed by the redundant transmissions of the correlated sensor data is saved. Performance evaluation of the proposed mechanism is conducted through OPNET simulations. The simulation results show that the adaptive DK-means algorithm significantly improves data reliability, as compared to the adaptive K-means algorithm. Furthermore, this improvement in reliability is achieved with minimal cost in terms of complexity. Finally, it is shown that the proposed sensor scheduling algorithm achieves energy savings of up to 58%, as compared to the baseline ZigBee protocol.

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