  The research challenge of current Wireless Sensor Networks (WSNs) is to
design energy-efficient, low-cost, high-accuracy, self-healing, and scalable
systems for applications such as environmental monitoring. Traditional WSNs
consist of low density, power-hungry digital motes that are expensive and
cannot remain functional for long periods on a single power charge. In order to
address these challenges, a dumb-sensing and smart-processing architecture that
splits sensing and computation capabilities is proposed. Sensing is exclusively
the responsibility of analog substrate---consisting of low-power, low-cost
all-analog sensors---that sits beneath the traditional WSN comprising of
digital nodes, which does all the processing of the sensor data received from
analog sensors. A low-power and low-cost solution for substrate sensors has
been proposed using Analog Joint Source Channel Coding (AJSCC) realized via the
characteristics of Metal Oxide Semiconductor Field Effect Transistor (MOSFET).
Digital nodes (receiver) also estimate the source distribution at the analog
sensors (transmitter) using machine learning techniques so as to find the
optimal parameters of AJSCC that are communicated back to the analog sensors to
adapt their sensing resolution as per the application needs. The proposed
techniques have been validated via simulations from MATLAB and LTSpice to show
promising performance and indeed prove that our framework can support large
scale high density and persistent WSN deployment.
