Compression, correlation and detection for energy efficient wireless sensor networks

Wireless sensor networks are enabling embedded sense-and-respond applications that were previously unimaginable. A high degree of spatial and temporal correlation exists in sensor readings. Data communication and storage costs can be reduced by effectively exploiting these correlations which can achieve high compression. Another source of energy savings is duty-cycling, which aims to extend the system lifetime by reducing the fraction of time a sensor node is on.; In this research, we devised algorithms and protocols to improve energy efficiency of wireless sensor networks. The focus was on three related areas: (i) compression of sensor readings, (ii) correlation of sensor readings, (iii) detection and cueing of events. Our techniques made it possible to keep the average power consumption for a typical target tracking application in the 10-mW regime.; The results can be summarized as follows. Our compression scheme for exploiting the spatio-temporal correlations uses set-partitioning on wavelet transformed data to extract correlated bitplanes. The spatial correlation is further exploited by the low-density parity-check based Slepian-Wolf codes at the bitplane levels. This scheme has also been extended to support the source broadcast problem where one sensor needs to send its readings to multiple receivers. A power aware joint coding scheme for correlated sensor readings has been developed that takes into account transmission power, channel coding rate and packet retransmission to minimize energy per transmitted bit. It is crucial for distributed compression to be able to correlate sensor readings. We have developed an energy efficient scheme to quantify and track spatial correlation of sensor readings. This scheme uses linear prediction to establish initial correlation and tracks the correlation using a Kalman-filter based approach. Finally, a tripwire cueing scheme has been developed, which uses detection predicates to wake up signal-processing nodes only when necessary. This multi-hop wake-up protocol enables a two-tier heterogeneous sensor network for greater energy savings, and coupled with a distributed two-stage detection algorithm, it can solve both the false alarm problem and the exposed alarm problem.