Sensor Networks JP Vasseur, Josh Bers, Yingying Chen Pandurang Kamat, Chip Elliott.

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Presentation transcript:

Sensor Networks JP Vasseur, Josh Bers, Yingying Chen Pandurang Kamat, Chip Elliott

General Observations Sensor networks have some research overlap with other wireless networks, but as general characteristics Are strongly tied to the real world and subject phenomena outside of CS/EE... Are highly varied (turtles, radars, traffic, indoor localization,...)... Yet nonetheless have certain representative interests & core needs.

Common Experimental Needs Good space/time localization (e.g. differential GPS with good clocks) Communications in a range of disadvantaged modes Strong concerns with energy husbanding In-network / backend processing of data queries Security in all its aspects Experiment setup, debugging, data gathering Spatially oriented visualization

Discussion on Sensors Is there a common sensor for “baseline GENI”? Maybe RF sensor, since baseline will have a radio? Desirable to have an abstraction layer to plug in arbitrary sensors at data layer How exactly would multiple “slices” share a common sensor? At the device level? Or is sensor data published? All in all, it may be best to have a baseline sensor node with typical functionality, with easy extension to specialized forms of sensors

Discussion on Virtualization Many typical sensor nodes will be too small for virtualization RF virtualization will be challenging; may be simpler just to allocate spatial clusters “Stargate” type architecture seems generally suitable; small, unique sensor nets can be plugged in “behind” a GENI sensor node

Other Discussion Need for “ground truth” to compare against experimental results, e.g., –Accurate RF measurements –Accurate localization / time –Questions of device calibration, error, etc. Experimental infrastructure must include “ground truth” components Metro or building deployment must answer the question: “what’s in it for them”

Representative Experimental Usage Scenarios Within a large building (localization of moving objects or people) –RF tags –Active tags for localization research Metro area, e.g., –Traffic patterns (microwave/seismic) –Weather (temp, humidity, wind,..., particulate, O^3, tornados!) –RF environment (RF network as first class sensor) Agricultural, or other outdoors

Multi-tiered Urban sensing environment Rooftop & streetside sensors Indoor sensor deployment Sensors in public places Courtesy Pandurang Kamat