the university of iowa. copyright© 2005 a. kruger, r. abel, c. mueller, m. karson 1 introduction to...

1The University of Iowa. Copyright© 2005 A. Kruger, R. Abel, C. Mueller, M. Karson

Introduction to Wireless Sensor Networks

Smart Dust

4 April 2005


Imagine if you will…• Two opposing military forces, Alpha and

Omega, are separated by a portion of jungle.

• Each wants to locate and identify enemy positions and movements.

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• Alpha wants a safer, more efficient means of performing reconnaissance– Human resources for intelligence

gathering are non-optimal• Costly

– Money– Human life

• Human error• Non-persistent


Deployment• Army Alpha deploys an unmanned aerial vehicle

– Ejects tens of thousands of various kinds of rice sized motes

• Terrestrial based• Air based• Water based α

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• Motes automatically form a sensor field– Light, temperature,

vibration, radar, magnetic, acoustic, seismic or a miniature camera.

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Effect of Sensor Network• Army Omega dispatches intelligence

officers and equipment into sensor field

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What is Smart Dust?• Cute name for a network of miniscule motes

– Term “smart” comes from abilities of individual motes as well as overall function of network

– Term “dust” comes from the goal of packaging a fully functional mote in a 1mm3 package

• Project started at the University of California at Berkeley– Funded by DARPA (Defense Advanced Research

Projects Agency)• Most research aimed at military and defense applications


Vision• Think pixie dust - Scatter hundreds of

sensors which are nearly un-noticeable

• The size of a grain of sand complete with sensors, CPU, receiver, transmitter, antenna and a power supply

• Communication ranges of 1000 ft. or more


Long Term Goals of Project• Autonomous sensing and communications in 1mm3

• Optimize every aspect of WSN– Battery life ( several years ~5 )– Size (1mm3)

• Contains all elements of the mote– Range

• Some sources predict up to 1 km– Processing power

• On board motes• In networking messaging• Billions of computations requiring only picowatts (10-12)

– Communications• Laser

– Power Consumption– Deployment

• “Floating” motes• UAV deployment


History• Invented by Kris Pister (University of California,

Berkley) in 1992• Smart Dust started as a joke when everyone was

talking about smart homes, smart buildings, smart bombs…

• Smart Dust was the start of WSNs– In 1994 Pister started his research on Smart Dust and began

developing Motes (Hardware)– ~2001, Jason Hill, and David Culler (both at Berkley) worked

together to develop TinyOS for Pisters hardware. The resulting mote was called: MICA

– [TinyOS let] the mote’s hardware perform only critical functions, which in turn extends the mote’s lifetime

– “It’s all about energy.” (Pister)

• Partner in Dust Inc with Jason Hill (2002).


Are we there yet?• Short answer, not quite

– Minute motes have been developed in academic labs

– Larger motes have been used in WSNs

• How close?– Dust™ Networks is trying to produce

practical motes that are approaching the size of an Aspirin pill

– Package size seems to be main hurdle


Problems with Size• Package size

– Need to integrate sensor, CPU, transmitter, receiver, antenna onto a single chip

– Currently size is about 5 mm cube– Dust Inc mote is 1 inch square


A case study SPEC

• The first Single Chip Mote– 2mm2.5mm– AVR-like RISC core– 3k memory– 8-bit on chip ADC– FSK transmitter (19,200 kbps @ 40 ft)– SPI programming

• Serial Peripheral Interface (For in-system programming)

– RS232 compatible UART– 4-bit input port, 4-bit output port– $0.30 in quantity


CPU Size/Power Considerations• RISC processors

– employed due to their small die size, and their ability to run in low power modes.

– Code density is of crucial importance• The ARM7TDMI is a 32 bit processor with an additional 16

bit instruction set– The instruction set can be switched by the software to adapt to

current circumstances.

– Power Saving Solutions• Active

– Fixed Frequency

– Frequency Scaling

– Dynamic Voltage Scaling (DVS)

• Power Saving (i.e. sleep, hibernate…)


Problems with Programming• Mass programming

– Smart Dust networks may involve thousands of nodes

– Programming them individually is not practical

• Embedded systems solution– Update firmware

• Wirelessly• Automatically• When update available


Problems with Cost• Manufacturing costs increase as size

decreases with computer chips

• Large scale networks– The cost of each mote must be very small

for costs of a practical system to remain realistic

– Predictions are $1/mote within 5 years


Power Consumption Solutions• Ultralow-Energy ADC

– Sampling Rate of 100 kHz– Power dissipation is 3.1 μW– Standby power is 70 pW– Energy per 8-bit sample is 31 pJ

• 1 kWH = 3.6 million J

– Die area is 0.053mm2

• Used onboard mote shown in previous


Zero Power Communication• Optical communication is possible using Microactuators

(MEMS) (Karakehayov).– Active-Steered Laser Systems

• Needs power to generate a beam– Passive reflective systems

• Can modulate an existing beam using very little power

• Can be done with a Corner Cube

Retroreflector (CCR), three mutually orthogonal mirrors

• Modulation is accomplished by slightly turning a mirror such that the light is no longer reflected towards the information sink

• Mirror rotation can be accomplished 1000 times per second at a cost of less than one nanoJoule per transition.

• CCRs can be roughly oriented using a magnetic compass


The Sleep-Awake Protocol• Uses 2 Modes of Laser Communication

– Broadcast Beacon Mode (low energy short length communication)

– Point Directed Mode (data transmission)

• Assumptions– No geolocation capabilities assumed (GPS)– No communication (transmitted or received)

during sleep cycle, sensors may be active


The Protocol•Search Phase: Uses a periodic low

energy broadcast of a beacon of angle towards the wall in order to discover a particle nearer to the Wall than itself.

•Direct Transmission Phase: 2 Sends info( ) to 3 via a direct line (laser) and sends a success message to 1 (i.e. the particle that it received the information from).

•Backtrack Phase: If the Search Phase fails to discover a particle nearer to ,then sends a fail message to .


Analysis• This Technique is quite new and a

thorough comparison is not available.

• BUT– Sparse Topology and Energy Management

(STEM) uses a similar technique (actively puts nodes to sleep) and performs nearly two orders of magnitude better then Sensor Networks without Topology Management


Possible Applications• Military applications

– Remote vehicle & personnel sensing/monitoring

– Missile guidance

• Civilian applications– Ambient environment monitoring– Long range, ubiquitous communications– Power grid monitoring and maintenance

• Boost power transmission


Sources• Scott, M.D., Boser, B.E., Pister, K.S.J., “An ultralow-energy ADC

for Smart Dust”, IEEE Journal of Solid-State Circuits, V. 38, Issue 7, July 2003, pgs 1123-1129

• Karakehayov, Z.; “Zero-power design for Smart Dust networks”, Intelligent Systems, 2002. Proceedings. 2002 First International IEEE Symposium, Volume 1, 10-12 Sept. 2002 Page(s):302 - 305 vol.1

• Chatzigiannakis, I.; Nikoletseas, S., “A sleep-awake protocol for information propagation in smart dust networks”, Parallel and Distributed Processing Symposium, 2003. Proceedings. International 22-26 April 2003.

• Frost Gorder, P., “Sizing up smart dust”, Computing in Science

& Engineering, Volume 5, Issue 6, Nov.-Dec. 2003 Page(s):6 - 9


Thank You

the university of iowa. copyright© 2005 a. kruger, r. abel, c. mueller, m. karson 1 introduction to...

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