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Sensor networks
· Ad-hoc networks for environmental monitoring

· Wireless sensor networks

· Mobile sensing platform

Sensor networks architecture

Internet, Satellite, UAV

Sink

Sink Database

Formatting Processing M ining Visualization Cloud Services

Jim Gray and Alex Szalay Life under your feet JHU

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Wireless Sensor Networks (WSN)
· A WSN can consist of 10 to 1000 of sensor nodes (motes) that communicate through wireless channels for information sharing and cooperative processing With low-power circuit and networking a mote powered by 2 AA batteries can last for 3 years with a 1% low duty cycle working mode After the initial deployment (ad-hoc), motes are responsible for selforganizing into a network with multi-hop connections The onboard sensors then start collecting acoustic, seismic, infrared or magnetic information about the environment, using either continuous or event driven working modes Location and positioning information can also be obtained through the global positioning system (GPS) or local positioning algorithms The basic philosophy behind WSNs is that, while the capability of each individual sensor node is limited, the aggregate power of the entire network is sufficient for the required mission

· · ·

· ·

Difference from ad-hoc networks
· · · · · · · Number of sensor nodes can be several orders of magnitude higher Sensor nodes are densely deployed and are prone to failures The topology of a sensor network may change frequently due to node failure and node mobility Ad-hoc network cables are prone to environmental impact such as lightning Sensor nodes are limited in power, computational capacities, and memory May not have global ID like IP address Need tight integration with sensing tasks

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Sensor network node
Location and Time Sync
SENSING UNIT PROCESSING UNIT

Mobilizer

Small Low power Low bit rate High density Low cost (dispensable) Autonomous Adaptive

Processor
Sensor ADC Transceiver

Memory

ANTENNA

Power Unit
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Telos platform
Robust
· · · · · · · · · · · · · · USB interface Integrated antenna (30m-125m) External antenna capabie (~500m) 10kB RAM, 48 KB ROM 12-bit ADC and DAC Hardware link-layer encryption TI MSP430 (16bit) @8MHz 6A sleep 460A active 1.8V operation E E E 802. 15. 4 CC2420 radio 250kbps 2.4GHz ISM band

High Performance

Processor:

Radio:

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Evolution of Telos platform
2nd generation MSP430 ~50% less power consumption in stand-by and off-mode faster wake-up: 1µs vs. 6µs 2x speed (16MHz vs. 8MHz), ~2x Flash (92KB vs. 48KB), 8KB vs. 10KB RAM Programmable internal pull-ups.

~2x External Flash Memory (16Mbit vs. 8Mbit) Sensors 3-axis digital accelerometer and temperature sensor vs. light, temperature and humidity sensors. Ziglet sensors product-line under development.

Development system
Virtual Machine: Ubuntu 9.10 in VirtualBox TinyOS 2.1.1 synchronized with CVS repository Eclipse IDE with YETI 2 plugin for TinyOS

Power consumption
· Need long lifetime with battery operation
No infrastructure, high deployment & replenishment costs

· Challenges
Energy to wirelessly transport bits is ~constant (Shannon, Maxwell) Fundamental limit on ADC speed*resolution/power No Moore's law for battery technology ~ 5%/year

· How is power consumed
CPU (Moor 's law!) Radio

WSN applications
· CLASS 1: Data collection
Entity monitoring with limited signal processing in a relatively simple form, such as temperature and humidity Sampling period from days to minutes Environmental monitoring and habitat study

· CLASS 2: Computationally intensive
Require processing and transportation of large volumes of complex data 10 Hz 100 KHz sampling frequency Seismic, industrial monitoring and video surveillance

KOALA: ultra-low power data retrieval in wireless sensor networks
· · , ( ) · Flexible control protocol ( ) · low power probing

KOALA:
· , USB . · , flash . · ( TinyOS) (LPP ), (FCP ), ,

· :

· «» :
KOALA - ()

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· · · · · · · · · : 03.11.2010 : 07.12.2010 : 3,2 3 ( , Koala, ) : , , (2880 ) . LPP : , , , , 10-20 . 10-20 () , , .

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, WSN
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Automatic vs. manual seismic event detection Piton de la Fournaise volcano

Spatio-temporal clustering of seismic waveforms

Advanced Land Imager (ALI) on NASA's Earth Observing-1 satellite captured this image of Piton de la Fournaise on January 16, 2009

The April 2007 eruption and the Dolomieu crater collapse, two major events at Piton de la Fournaise

Piton de la Fournaise eruption January 2, 2010

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