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Design Principles for Efficient Smart Sensor Systems

Modular Platform for High Density Wireless Sensing. Problem Overview. Sample Problem & Benefits. Solution Overview & Domains.

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Design Principles for Efficient Smart Sensor Systems

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  1. Modular Platform for High Density Wireless Sensing Problem Overview Sample Problem & Benefits Solution Overview & Domains • The goal of this project is to create a set of design principles to aid in the creation of efficient smart sensor systems. We define such systems as the general class of sensors systems that • have the ability to analyze their state and surroundings • and • respond to them by changing their mode of operation in some beneficial fashion. • Noting that power cell density is advancing at a particularly slow rate (doubling every 15 years), we wish to optimize with respect to power usage. This: • Increases life of the system(i.e. time between battery change) • Makes the system more useful in large variety of situations • Therefore, our goal is to design smart sensors systems to accomplish their given task using as little power as possible (on average). The following procedure will be followed for any given problem: • Identify world states of interest for the system. • Determine the parameters to be calculated in each state. • Build recognition system to determine current state. • The system will be dynamically optimal, such that at any given time, it is collecting and processing data based on its current (calculated) state. This will provide performance which far exceeds our current systems, which are optimized for a single state. • We consider the following sample scenario: • Medical logging device • Stand-alone system (ie no external data sources) • Collects and logs data over extended time • Sample states: • Still: Sample tilt switch to make sure state doesn’t change. • Twitching: Sample accelerometer at lower rate to ensure no forward progress is being made. • Walking: Sample accelerometer at full rate and calculate gait parameters • For this system, we examine its power usage in each of the states, assuming AXDL202 accelerometers and a Cygnal P: State 1. Walking (~2% of time) • Collect useful data & check state • 12bit ADC samples from ADXL • at 50Hz23.25 mW • State 2. Twitching (~8% of time) • Check state • 6bit ADC samples from ADXL • at 25Hz7.16 mW • State 3. Static (~90% of time) • Check state • 1bit tilt switch sample • at 1Hz1.65 nW • Average power usage of 1 mW compared to 42.9 mW for continuous full analysis • Equivalent of transmission of ~18000 bytes a second • Almost 50 times improvement over current system (cf Gait Shoe) As a first platform for testing these ideas, we have designed and built a modular architecture based on 1.4” sq. boards. Each board has a pair of 26 (14 + 12) pin headers to connect signals and power to other panes in the stack. The simplicity will which sensors can be added and removed with greatly aid in prototyping new designs as described at left. • Overview of Solution: • Create a tabulation of optimal solutions for common sensing problems • For a given phenomena/task, this will include: • Best sensor choice(s) (bits vs. power) • Analog and digital signal processing • Algorithms to extract key data features • Enumeration of states • Will be limited to HCI domain to remain tractable • Solution will draw upon three key domains: • Sensor Technologies • Use knowledge in field to choose best sensor to measure a phenomena based on: • Power usage, bandwidth, accuracy and size • Must include signal processing to connect to P • Filtering and gain to maximize dynamic range • Analog/digital processing divide based on power efficiency • Switch between above solutions based on state • Low Power Design • Use transmitter with maximum efficiency at desired transmit power (based on range) • Structure packets to send as much data at once as possible to overcome wakeup penalty • Re-order data analysis routines to allow monotonicpower vs. accuracy trade-off • Application Design • Must take into account useable form factors and input modalities as suggested by human factors research • Sensor systems which are too large, cumbersome or noticeable will not get used • Analysis cannot be so complex that data arrives too late to give user meaningful feedback • E.g. Info about balance shift must be reported before user is on floor • Counter clockwise from bottom left: • Main board • 22 MIPS processor with 12 bit analog to digital converter • 115.2 kBps wireless transceiver with TDMA channel sharing • Inertial Measurement board • Full 6 DOFs in flat package • 3 axes of accelerometers • 3 axes of gyros • Tactile board • Inputs & signal processing for • FSRs • PVDFs • Bend Sensors • Power regulation board • Provides for battery input • Outputs of 3.3V and 5V Design Principles for Efficient Smart Sensor Systems

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