In this paper, we discuss a low power embedded sensor node architecture we are developing for distributed sensor network systems deployed in a natural environment. In particular, we examine the sensor node for energy efficient processing-at-the-sensor. We analyze the following modes of operation; event detection, data acquisition, and data processing using low power, high performance embedded technology such as specialized embedded DSP processors and low power FPGAs at the sensing node. We use compute intensive sensor node applications: an acoustic vehicle classifier (frequency domain analysis) and a video license plate identification application (learning algorithm). We report performance and energy for these applications and discuss the system architecture design trade offs.

We address the problem of finding an energy efficient locomotion scheme for multilegged walking vehicles. Our approach makes use of compliant actuators to store and release the kinetic and potential energy of the body and legs during each gait cycle. An evolutionary strategy algorithm is used to search the initial condition parameter space until a nearly passive cyclic gait is found. The trajectory of this gait can then be used in a specific class of hexapods to significantly reduce the energy used over each gait cycle.