The drive toward the implementation and massive deployment of wireless sensor networks calls for ultra-low-cost, low-power and ever smaller nodes. While the digital subsystems of the nodes are still experiencing exponential reduction of all of these metrics as described by Moore's Law, there is no such trend regarding the performance of analog components needed for the radios that enable the nodes to communicate wirelessly with one another. This dissertation presents a two part approach to reducing the energy consumption of the radios. First, a new radio architecture is presented that greatly reduces the power required to operate a transceiver, as well as reducing the cost and size of the nodes. Secondly, a novel distributed compression scheme is introduced that allows the sensor nodes to compress their data in order to reduce the amount of communication that the radios must perform.

The dissertation presents a fully integrated architecture of both digital and analog components (including local oscillator) that offers significant reduction in cost, size and power consumption of the overall node. Even though such a radical architecture cannot offer the reliable tuning of standard designs, it is shown that by using random network coding, a dense network of such nodes can achieve throughput linear in the number of channels available for communication. Moreover, the ratio of the achievable throughput of the untuned network to the throughput of a tuned network with perfect coordination is shown to be close to 1/e. By contrast, it is also shown that if coding is not used (i.e. if nodes are only allowed to forward packets without processing them), the performance does not improve with increased density and available spectrum.

To reduce the amount of communication among nodes required, a novel approach to reducing energy consumption in sensor networks using a distributed adaptive signal processing framework and efficient algorithm is proposed. Specifically, the dissertation presents a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles. This approach enables sensor nodes to blindly compress their readings with respect to one another without the need for explicit and energy-expensive inter-sensor communication to effect this compression. Furthermore, the distributed algorithm used by each sensor node is extremely low in complexity and easy to implement (i.e., one modulo operation), while an adaptive filtering framework is used at the data-gathering unit to continuously learn the relevant correlation structures in the sensor data.

Applying the algorithm to testbed data resulted in energy savings of 10%-65% for a multitude of sensor modalities. Both the network coding for communication with untuned radios and the distributed source coding schemes require minimal complexity from the lowpower sensor nodes. Instead, the complexity of the system is pushed toward the edge of the network where a gateway between the wireless network and the wired world resides.