Wireless Sensing: the Internet's Front-Tier

1. Wireless Sensing:the Internet's Front-Tier David Culler Deborah Estrin Federated Computing Research Conference June 12, 2007

2. The Internet

3. The Internet Front-Tier

4. Embedded Networked Sensing embedded in the physical environment (soil, canopy, rivers, groundwater, coastal) networked to share information and adapt function (data, system status, control) sensing measurement instruments (sensors, transducers) an “internet” of sensors

5. Save Resources Improve Productivity Protect Health Improve Food & H20 Why “Real” Information is so Important? Enable New Knowledge Increase Comfort Enhance Safety & Security Preventing Failures High-Confidence Transport

6. Introduction Technological Foundations Unprecedented Information Participatory Sensing Internet Front-Tier – really A Broader Sense Outline

7. Today: 1 million transistors per $ Computers Per Person 1:106 Mainframe Mini 1:103 Workstation PC Laptop 1:1 PDA Cell 103:1 Mote! years Broad Technology Trends Moore’s Law:# transistors on cost-effective chip doubles every 18 months Bell’s Law: a new computer class emerges every 10 years Same fabrication technology provides CMOS radios for communication and micro-sensors

8. Microcontroller Flash Storage Radio Communication Sensors Enabling Technology Network IEEE 802.15.4

9. Grand challenge visions of microscopic computing everywhere Lots of Linux/Wince ARM/x86/68k + radio prototypes Estrin’s PC104 testbed showed that “idle listening” in 802.11 MAC dominated ALL else Huge emergence of interesting papers solving hypothetical problems Create a platform that would expose the community to real problems Share a lot of the solutions (and development overhead) Unconstrained by past 40 years of OS and Networking abstractions Silicon World Enabling Systems Research Physical World

10. LWIM-III (UCLA) WSN Research Phenomenon… LEAP WINS(UCLA/ROckwell) zeevo BT Intel MOTE2 Intel iMOTE Intel rene’ Intel/UCB dot BTNode Eyes Intel cf-mica trio SmartDust WeC Rene Mica Telos XBOW mica XBOW mica2 XBOW cc-dot XBOW rene2 XBOW micaZ digital sun rain-mica Bosch cc-mica Dust Inc blue cc-TI 00 01 02 03 04 05 06 07 97 98 99 LWIM Expedition NEST Cyber-Physical SENSIT CENS STC NETS/ NOSS DARPA NSF

11. Applications and Services Over-the-air Programming Network Protocols Blocks, Logs, Files Streaming drivers Scheduling, Management Link Radio Serial Flash MCU, Timers, Bus,… ADC, Sensor I/F Sensors Wireless Processing Storage (Re)discovering the Boundaries TinyOS 2.0 WSN mote platform Communication Centric Resource-Constrained Event-driven Execution

12. NEST SmartDust Wireless Sensor Networks Sensors Storage Wireless Processing A worldwide community

13. 2 2 2 2 1 1 2 Self-Organized Mesh Routing - nutshell 0

14. * System design * Leakage (~RAM) * Nobody fools mother nature What we mean by “Low Power” • 2 AA => 1.5 amp hours (~4 watt hours) • Cell => 1 amp hour (3.5 watt hours) Cell: 500 -1000 mW => few hours active WiFi: 300 - 500 mW => several hours GPS: 50 – 100 mW => couple days WSN: 50 mW active, 20 uW passive 450 uW => one year 45 uW => ~10 years Ave Power = fact * Pact + fsleep * Psleep + fwaking * Pwaking

15. What WSNs really look like Deploy Query Command Visualize Client Tools External Tools Excel, Matlab Enshare, etc. Internet GUI Legacy Data analysis Field Tools Gateway Embedded Network

16. Len SFD Fchk FCF 127 bytes Towards the Internet Frontier – 6LoWPAN: IPv6 over IEEE 802.15.4 IEEE 802.15.4 Frame Format D pan Dst EUID 64 S pan Src EUID 64 preamble Dst16 Src16 DSN Network Header Application Data dsp IETF 6LoWPAN Format 01 0 0 0 0 0 1 Uncompressed IPv6 address [RFC2460] 40 bytes 01 0 0 0 0 1 0 HC1 Fully compressed: 1 byte Source address : derived from link address Destination address : derived from link address Traffic Class & Flow Label : zero Next header : UDP, TCP, or ICMP

17. Unprecedented Information

18. CENS, UCLA Dawson, UCB Science application drivers explore complexspatial variation and heterogeneity P. Davis, UCLA

19. Johnny Appleseed deployment myth Spatial sampling challenges Difficult to assess spatial variability and model patterns in complex, dynamic media (soil, water, air) Over-deployment not a general solution: minimum spacing constraints, installation difficulty, settling time Geometrically-determined locations/metrics don’t capture environment’s complex topology obstacles, inputs (sun, precipitation, currents) Calls for model and data-informed placement, iterative and adaptive sampling Temporal sampling more elastic Many temporal signals can be fully sampled with existing platforms Calls for runtime adjustments to live within energy constraints "Soil microorganisms mediate below- and aboveground processes, but it is difficult to monitor such organisms because of the inherent cryptic nature of the soil. Traditional 'blind' sampling methods yield high sample variance...." [Kliornomos99] Hansen, Harmon, Schoellhammer, et al.

20. New themes Heterogeneity Combine in situ and server processing to optimize system Inevitable under-sampling with static sensing: mobility Exploit multiple modalities (e.g. imagers), multiple scales Interactivity Coupled human-observational systems: tasking, analysis,vis. In-network processing, system transparency for responsiveness, data integrity, rapid-iterative deployment Participatory sensing systems leveraging cellphone, gps,web. Lessons from the field... • Early themes • Thousands of small devices • Minimize individual node resource needs • Exploit large numbers • Fully autonomous systems • In-networkand collaborative processing for longevity: optimizecommunication

21. Slope (Spatial Analyst) Aspect (Spatial Analyst) Daily Average Temperature(Geostatistical Analyst) Elevation (Calculated from Contour Map) Aerial Photograph (10.16cm/pixels) Coupled Human-Observational Systems • Transform physical observations from batch to interactive process • Rapid deployments are high value. • Interactive systems take advantage of human observation, actuation, and inference • Addresses critical issues such as adaptive sampling, topology adjustment, faulty sensor detection • Requires real time data access, model based analysis, system transparency, visualization in the field Hamilton, Hansen, et al Hamilton, Kaiser, Hansen, Kohler, et al.

22. Data, Data, Data: Increasing role of statistical models and methods • Data integrity: Robust procedures for analysis in the presence of sensing and environmental uncertainty • Multiple scales: Designing experiments/analyses to match observation of multi-scale phenomena • Opportunistic measures and models: Integrating available measurements with available data sources and models Hansen, et al

23. Data Integrity focused on maximizing “data return” • Confidence: Tool for detecting and diagnosing network faults with an online-variant of K-means clustering to identify outliers in specially crafted feature space • Model-based detection: Hidden-Markov model complete with stochastic descriptions of system fault learned for detection with data from NAMOS • Signatures: Short description of multivariate probability distribution maintained for each sensor or cluster of sensors; likelihood ratio test used to flag readings that appear more faulty than normal • Blind calibration: New approach makes use of projections into function spaces (smoothness classes) Hansen, Kohler, Ramanathan, Golubcik, Nair, Balzano

24. Multi-Scale terrestrial carbon fluxes Fine scale Effects of roots, organic particles, and soil structure Soil CO2 concentration Soil respiration Plot-to field scale Effect of group of plants, and gradients in soil texture Large-scale Effect of vegetation systems, and topography Canopy photosynthesis Allen, Graham, Hamilton,et al

25. Commercially available autonomous devices available for physical and chemical measures only System designs need to compensate for lack of sensor specificity, sensitivity, availability…particularly wrt biological response variables Leverage proxy sensors and model based signal interpretation present future Physical Sensors: Microclimate above and below ground abiotic Chemical Sensors: gross concentrations Chemical Sensors: trace concentrations Acoustic, Image sensors with on board analysis Acoustic and Image data samples DNA analysis onboard embedded device biotic Organism tagging, tracking Sensor triggered sample collection If you can’t go to the field with the sensor you want, go with the sensor you have

26. Imagers as biological sensors: Heartbeat of a Nestbox Leverage context to apply server-side, and ultimately, on-board processing to infer “interesting behavior” (sensor output) Blue lines: output of automatic image processing algorithms applied to cyclops images over 5 minute intervals; Red/Green line: temperature. Ahmadian, Ko, Rahimi, Soatto, Estrin

27. Reddy, Burke, Hansen, Parker et al

28. “Footprint” calculators inform long-term choices using coarse-grained models impact Personalized, real-time assessment to help individuals reduce impact and minimize exposure by viewing their own practices and habits as seen in data and inferred from models Employ built-in capabilities of mobile handsets to scale without specialized hardware Leverage model-based analyses with location traces generated using GPS, cell tower, WiFi Merging models and sensing Personalized Environmental Impact Report (PEIR) System components GPS-equipped mobile handset. Custom handset softwarefor automatic location time-series collection, robust upload, over-the-air upgrade/tasking, just-in-time annotation with voice or text. Server side toolsto analyze individual spatio-temporal patterns and calculate corresponding impact and exposure metrics to inform and advise users. Web-based interfaces informing and advising users, which provide reports, real-time feedback, visualizations and exploratory data analysis tools for non-professional users. (For handsets and workstations.) SensorBase Campaignr Trace, audio, image N80, N95 + = Activity type inference Impact / Exposure Model Server-side classifier Burke, Estrin, Hansen, et al

29. Enabled by Over 2 x 109 users worldwide of cell phones. Automated geo-coding and pervasive connectivity Image and acoustic as data and metadata Bluetooth connected external sensors Local processing for data quality and triggering Spatial interface to data and authoring Applications Self-administered health diagnostics Public health/epidemiology: Water and Air Civic concerns (transportation, safety…) Personal Environmental Impact Report Challenges Mechanisms for selective sharing, verified location Inference from sensor streams (gps,image,sound) Campaign framework, data quality, incentives Participatory Urban Sensing: combines users, mobility, context participatory sensing data promises to make visible human concerns that were previously unobservable…or unacceptable

30. A Broader Sense

31. The Internet Front-Tier

32. Web Services XML / RPC / REST / SOAP / OSGI HTTP / FTP / SNMP TCP / UDP IP 802.11b 802.11 802.11g 802.11a Ethernet RFM,CC10k,…,802.15.4 Enet 100M Enet 1G Enet 10M Enet 10G GPRS THE Question If Wireless Sensor Networks represent a future of “billions of information devices embedded in the physical world,… why don’t they run THE standard internetworking protocol? ? Sonet Serial Self-Contained Plugs and People

33. Appln Transport Routing Scheduling Topology Link Phy Sensor Network “Networking” EnviroTrack Hood TinyDB Regions FTSP Diffusion SPIN TTDD Trickle Deluge Drip MMRP Arrive TORA Ascent MintRoute CGSR AODV GPSR DSR ARA GSR GRAD DBF DSDV TBRPF Resynch SPAN GAF FPS ReORg PC Yao SP100.11a SMAC WooMac PAMAS BMAC TMAC WiseMAC Pico 802.15.4 Bluetooth eyes RadioMetrix CC1000 nordic RFM wHART Zigbee Zwave

34. The Answer They should • Substantially advances the state-of-the-art in both domains. • Implementing IP requires tackling the general case, not just a specific operational slice • Interoperability with all other potential IP network links • Potential to name and route to any IP-enabled device within security domain • Robust operation despite external factors • Coexistence, interference, errant devices, ... • While meeting the critical embedded wireless requirements • High reliability and adaptability • Long lifetime on limited energy • Manageability of many devices • Within highly constrained resources

35. Proxy / Gateway Making sensor nets make sense • LoWPAN – 802.15.4 • 1% of 802.11 power, easier to embed, as easy to use. • 8-16 bit MCUs with KBs, not MBs. • Off 99% of the time Web Services XML / RPC / REST / SOAP / OSGI HTTP / FTP / SNMP TCP / UDP IP 802.11 802.15.4, … Ethernet Sonet IETF 6lowpan

36. Thinking about the Physical World as “Signals” • What is the bandwidth of the weather? • What is the nyquist of the soil? • What is the placement noise? • What is the sampling jitter error?

37. Application drivers: From Condition based maintenance to Precision Living… • The maturing technology will transform the business enterprise, environmental resource management, human interaction • Industrial and civil infrastructure • Individual Health and wellness • Planet health and wellness: water, carbon, pollution, waste • Science is our early adopter because the technology is transformative and research tolerates risk • Important historical precedents • Weather modeling--early computing • Scientific collaboration--Internet • Experimental physics (CERN)--WWW • Computational science--Grid Early embedded sensing applications • Biological and Earth Sciences • Environmental, Civil, Bio Engineering • Public health, Medical research • Agriculture, Resource management • Embeddable device developments • Energy-conserving platforms, radios • Miniaturized, autonomous, sensors • Standardized software interfaces • Self-configuration algorithms • Adaptive, iterative sampling • Cognitive sensors

38. “Early-and-really-to-application” deployments and resulting data provide feedback to system innovation… from theory to system architecture Multidisciplinary research means taking turns Training a generation of Eco-Geeks Mining for geeks w/diversity: gender, nationality, … Training for the mundane and the magnificent Funding and sustainability Research Ecosystem Challenges

39. An(other) inconvenient truth Many critical issues facing science, government, and the public call for high fidelity and real time observations of the physical world Networks of smart, wireless sensors, forming the Front-Tier of the Internet can help reveal the previously unobservable But an inconvenient truth is that the field does not lend itself to familiar abstractions and research practices…

40. To two special people who we would have wanted here above all … … to give us a dozen pointed criticisms … and two dozen wonderful new ideas. Richard A. Newton Jim Gray Dedication