| Summary: | Wireless Sensor Networks (WSNs) offer a compelling solution for distributed sensing problems because they can be deployed rapidly and inexpensively, and are robust to failures. However, since they operate on batteries, they tend to have short lifetimes. We present several algorithmic techniques for reducing the power consumption of such networks, based on Algorithmic Data Reduction (ADR) and low-power Ultra-Wide-Band Impulse Radio (UWB-IR).
In the ADR approach, we minimize power-hungry communication out of the network via distributed in-situ broadcast `message-passing' algorithms for filtering, compression and model identification. These algorithms are scalable, power-efficient, stable, and computationally tractable. At the same time their performance is close to the respective ultimate theoretical limits. Specifically, the filter performs close to an optimal Bayesian recursion, the compressor approaches the rate-distortion and channel-capacity bound, and the identification scheme is asymptotically efficient in the Cramer-Rao sense.
The UWB-IR approach exploits a well-known tradeoff predicted by Shannon theory, namely that one can maintain reliable communication at a given data rate at a reduced transmit power provided the transmission bandwidth is requisitely increased. We propose a novel UWB-IR receiver, which is eminently suited to the bursty mode of operation of the WSN physical layer. The receiver is based on the principle of Compressed Sensing and offers a practical alternative to costly high-rate analog-to-digital conversion. It can tolerate strong inter-symbol interference and can therefore operate at high pulsing rates, which allows us to fully leverage the power-vs-bandwidth tradeoff. It is impervious to poor timing synchronization, which means that the transmitter can avoid sending training headers, thus further saving a significant amount of power. In addition, it is also robust to strong narrow-band interference from licensed systems like WiMAX.
With a synergy of the ADR and UWB-IR techniques, the communication related power consumption of the WSN can be reduced by about 30 dB or more in practical scenarios, which substantially alleviates the handicap of limited lifetimes. We study a practical application of these techniques in the problem of target tracking by interpreting the received signal strength of transmissions from RFID tags.
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