The wireless sensor nodes used in a growing number of remote sensing applications are deployed in inaccessible locations or are subjected to severe energy constraints. Audio-based sensing offers flexibility in node placement and is popular in low-power schemes. Thus, in this paper, a node architecture with low power consumption and in-the-field reconfigurability is evaluated in the context of an acoustic vehicle detection and classification (hereafter “AVDC”) scenario. The proposed architecture utilizes an always-on field-programmable analog array (FPAA) as a low-power event detector to selectively wake a microcontroller unit (MCU) when a significant event is detected. When awoken, the MCU verifies the vehicle class asserted by the FPAA and transmits the relevant information. The AVDC system is trained by solving a classification problem using a lexicographic, nonlinear programming algorithm. On a testing dataset comprising of data from ten cars, ten trucks, and 40 s of wind noise, the AVDC system has a detection accuracy of 100%, a classification accuracy of 95%, and no false alarms. The mean power draw of the FPAA is 43 μ W and the mean power consumption of the MCU and radio during its validation and wireless transmission process is 40.9 mW. Overall, this paper demonstrates that the utilization of an FPAA-based signal preprocessor can greatly improve the flexibility and power consumption of wireless sensor nodes.
Low-power analog integrated circuits (ICs) can be utilized at the interface between an analog sensor and a digital system's input to decrease power consumption, increase system accuracy, perform signal processing, and make the necessary adjustments for compatibility between the two devices. This interfacing has typically been done with custom integrated solutions, but advancements in floating-gate technologies have made reconfigurable analog ICs a competitive option. Whether the solution is a custom design or built from a reconfigurable system, digital peripheral circuits are needed to configure their operation for these analog circuits to work with the best accuracy. Using an analog IC as a front end signal processor between an analog sensor and wireless sensor mote can greatly decrease battery consumption. Processing in the digital domain requires more power than when done on an analog system. An Analog Signal Processor (ASP) can allow the digital wireless mote to remain in sleep mode while the ASP is always listening for an important event. Once this event occurs, the ASP will wake the wireless mote, allowing it to record the event and send radio transmissions if necessary. As most wireless sensor networks employ the use of batteries as a power source, an energy harvesting system in addition to an ASP can be used to further supplement this battery consumption. This thesis documents the development of mixed-signal integrated circuits for use as interfaces between analog sensors and digital Wireless Sensor Networks (WSNs). The following work outlines, as well as shows the results, of development for sensor interfacing utilizing both custom mixed signal integrated circuits as well as a Field Programmable Analog Array (FPAA) for post fabrication customization. An Analog Signal Processor (ASP) has been used in an Acoustic Vehicle Classification system. To keep these interfacing methods low power, a prototype energy harvesting system using commercial-off-the-shelf (COTS) devices is detailed which has led to the design of a fully integrated solution. I would like to begin by thanking Dr. David W. Graham for all of his guidance and direction, as well as for giving me the opportunity to work in his laboratory while completing my research. I would also like to thank Dr. Brandon Rumberg for helping to answer my countless questions and for acting as a second mentor during my studies. My thanks also go to the other gentlemen who I have worked with these past few years for all of their help and willingness to listen: Brandon Kelly, Mir Mohammad Navidi, and Alex Dillelo. I also need to thank my friends and family for providing me with an amazing support system. Special thanks go out to my mother, Cindy and my father, Steve. I cannot express how much they have helped me get to this point in my life. Lastly, I would like to thank my girlfriend, Brittany, who has been there with me every step of the way.
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