We present the design of the eXtreme Scale Mote, a new sensor network platform for reliably detecting and classifying, and quickly reporting, rare, random, and ephemeral events in a largescale, long-lived, and retaskable manner. This new mote was designed for the ExScal project which seeks to demonstrate a 10,000 node network capable of discriminating civilians, soldiers and vehicles, spread out over a 10km 2 area, with node lifetimes approaching 1,000 hours of continuous operation on two AA alkaline batteries. This application posed unique functional, usability, scalability, and robustness requirements which could not be met with existing hardware, and therefore motivated the design of a new platform. The detection and classification requirements are met using infrared, magnetic, and acoustic sensors. The infrared and acoustic sensors are designed for low-power continuous operation and include asynchronous processor wakeup circuitry. The usability and scalability requirements are met by minimizing the frequency and cost of human-in-the-loop operations during node deployment, activation, and verification through improvements in the user interface, packaging, and configurability of the platform. Recoverable retasking is addressed by using a grenade timer that periodically forces a system reset. The key contributions of this work are a specific design point and general design methods for building sensor network platforms to detect exceptional events.

This paper presents a frequency-selective RF vector predistortion linearization system for RF multicarrier power amplifiers (PAs) affected by strong differential memory effects. Differential memory effects can be revealed in two-tone experiment by the divergence for increasing tone-spacing of the vector Volterra coefficients associated with the lower and upper intermodulations tones. Using large-signal vector measurement with a large-singal network analyzer, a class-AB LDMOS RF PA is demonstrated to exhibit a strong differential memory effect for modulation bandwidth above 0.3 MHz. New frequency-selective RF and baseband predistortion linearization algorithms are proposed to separately address the linearization requirements of the interband and inband intermodulation products of both the lower and upper sidebands. Theoretical verification of the algorithms are demonstrated with MATLAB simulations using a Volterra/Wiener PA model with memory effects. The baseband linearization algorithm is next implemented in a field-programmable gate array and experimentally investigated for the linearization of the class-AB LDMOS PA for two carrier wideband code-division multiple-access signals. The ability of the algorithm to selectively linearize the two interband and four inband intermodulation products is demonstrated. Adjacent channel leakage ratio of up to 45 dBc for inband and interband are demonstrated experimentally at twice the typical fractional bandwidth.

Ultrawideband radar-enabled wireless sensor networks have the potential to address key detection and classification requirements common to many surveillance and tracking applications. However, traditional radar signal processing techniques are mismatched with the limited computational and storage resources available on typical sensor nodes. The mismatch is exacerbated in noisy, cluttered environments or when the signals have corrupted spectra. To explore the compatibility of ultrawideband radar and mote-class sensor nodes, we designed and built a new platform called the Radar Mote. An early prototype of this platform was used to detect, classify, and track people and vehicles moving through an outdoor sensor network deployment. This paper describes the sensor's theory of operation, discusses the design and implementation of the Radar Mote, and presents sample signal waveforms of people, vehicles, noise, and clutter. We demonstrate that radar sensors can be successfully integrated with mote-class devices and imbue them with an extraordinarily useful sensing modality.

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