Introduction to Wireless Sensor Networks

1. Introduction to Wireless Sensor Networks Smart Dust 4 April 2005

2. 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. • 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 α Ω

3. 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 • Motes automatically form a sensor field • Light, temperature, vibration, radar, magnetic, acoustic, seismic or a miniature camera. α α α α α α α α α α α Ω

4. Effect of Sensor Network • Army Omega dispatches intelligence officers and equipment into sensor field α α α α α α α α α α Ω α α α α α α α α α α α Ω α α α α α α α α α α Ω α Ω

5. 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

6. 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

7. 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

8. 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).

9. 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

10. 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

11. A case study SPEC • The first Single Chip Mote • 2mm2.5mm • 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

12. 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…)

13. 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

14. 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

15. 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

16. 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 Zero Power Communication • 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

17. 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

18. 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 3via 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 .

19. 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

20. 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

21. 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

22. Thank You