Low-Cost Radio and TO as an Enabling Technology Presented by - - PowerPoint PPT Presentation
Low-Cost Radio and TO as an Enabling Technology Presented by - - PowerPoint PPT Presentation
Low-Cost Radio and TO as an Enabling Technology Presented by Bernhard Firner And many more, including Richard Howard, Yanyong Zhang, Richard Martin, Giovanni Vannuci, and Robert Moore Simplification and Enabling Technologies The
Simplification and Enabling Technologies
years Computers Per Person 103:1 1:106
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1:1 1:103
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The evolution of computing systems is a story of miniaturization and cost reduction Bell's Law: “Roughly every decade a new, lower priced computer class forms based upon a new programming platform, network, and interface”
(Wikipedia)
The Vision of Tiny Computers
Healthcare Environmental Monitoring Tracking and Logistics
This Vision Is Old News
- 1999 Smart Dust
- 2000 Sensor Networks
- 2004 Internet of Things
- 2005 Ambient Intelligence
- 2009 Swarms
So what is holding us back?
Wireless Sensors Are Small Enough
Integrated circuits have followed Moore's law
So what is missing? Micro controller Radio Battery Antenna TO-PIP(2013) TelosB (2004)
Some Insights
- After deploying our own systems we encountered a few
problems
– Sensors are too expensive for application domains – Deployment and Maintenance – No general purpose solutions – Each application is unique – New software effort with each deployment – Infrastructure brittle, difficult to re-use
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The Biggest Show-Stopper Is Cost
- Sensor node cost is a limitation for many applications
– Applications enabled at sensor cost of $100, $10, $1, 10¢, 1¢ ?
- Cost assumptions based on scaling Moore's law real omit real
constraints
– Cost is driven by the number and type of components, not Moore's law!
- Cost of maintenance is driven by sensor lifetime
– But batteries aren't getting any smaller!
- We need a simpler sensor, which means a simpler MAC
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Transmit Only Approach
- Key insight: sensed data is in a class where small losses can
be tolerated. Probabilistic reception is OK.
– Similar to audio, video, and multi-player games, not documents.
- Sensors only sense and transmit with specified periods
– Sensors are at most 1 hop – Add small amount of randomization to prevent collision periodicity.
- A small set of receivers cooperate to reconstruct sensed data
– Connected by a powerful back-haul network – Back-haul bandwidth > sensor bandwidth
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TO - Less Is Better
- Everything that doesn't transmit an application bit is overhead
- Removed:
– Sensing the channel before transmission (for CSMA protocols ) – Acknowledgements (for RTS/CTS protocols ) – Precise clocks and synchronization ( for TDMA protocols ) – Signal feedback ( MIMO physical layers )
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Transmit Only as Less Is Better
- Focus on getting the sensed data:
– Everything else is overhead
- Saves energy on the sensor
– Receiving has similar energy costs per bit-time as transmit
- Simplify the sensors
– Fewer components – Cheaper components – Smaller sensors
Enabled a 10 year wireless sensor!
TO Enables New Kinds of Deployments
- Sensors connect to an
intermediate layer that hides details
- Solvers build higher-level
representations from low-level
- nes
- A uniform model of the world
allows sharing
- Applications run in standard
environments in the cloud
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A Success Story
- Ongoing Deployment in Rutgers Laboratory Animal Services
– 24/7 monitoring of environmental conditions – temperature, light level, human activity – Real-time alerts when conditions go out of acceptable bounds – Long-term data logging – Reduces operating cost and improves quality of service
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Conclusions
- Needed to reduce sensor cost
- Holding back envisioned wireless sensor deployments
- Found a way to simplify the sensor
- TO protocol increases lifetime while reducing complexity
- More cost-efficient to overcome problems in software of in receivers
- This has allowed us to create a real-world-ready system
- Deployment is fast
- Data is managed through a cloud platform
- Can offer deployments with positive value propositions