In-sensor analytics and energy-aware self-optimization in a wireless sensor node

With the proliferation of distributed sensors and Internet of Thing end-nodes, aggregate data transfer to the back-end servers in the cloud is expected to become prohibitively large which not only results in network congestion, but also high energy expenditure a the sensor nodes. This motivates in-sensor data analytics that can perform context-aware acquisition and processing of data; and transmit data only when required. This paper presents a camera based wireless sensor node with in-sensor computation, wireless communication and end-to-end system optimization. Depending on the amount of information content and the wireless channel quality, the system chooses the minimum-energy operating-point by dynamically adjusting the processing depth (PD) and power ampUfier (PA) gain. We demonstrate a complete end-to-end system and measure 3.7 χ reduction in energy consumption compared to a baseline design where only rudimentary image compression is performed.