1. SENSOR NETWORKS
-Praveena
-Omar

2. What is a Sensor???
A device that responds to a physical stimulus such as thermal energy, electromagnetic energy, acoustic energy, pressure, magnetism or motion by producing a signal, usually electric.

3. Vision
Embed numerous distributed devices to monitor and interact with physical world
Exploit spatially and temporally dense, in situ, sensing and actuation
Network these devices so that they can coordinate to perform higher-level tasks.
Requires robust distributed systems of hundreds or thousands of devices.

4. Applications Scientific: eco-physiology,
Infrastructure: Contaminant flow monitoring
Engineering: adaptive
structures
Model Development

5. Challenges
Tight coupling to the physical world and embedded in unattended “control systems”
Different from traditional Internet, PDA, Mobility applications that interface primarily and directly with human users
Untethered, small form-factor, nodes present stringent energy constraints
Living with small, finite, energy source is different from traditional fixed but reusable resources such as BW, CPU, Storage
Communications is primary consumer of energy in this environment
Sending a bit over 10 or 100 meters consumes as much energy as thousands/millions of operations (R4 signal energy drop-off; Pottie-etal00).

6. Why cant we simply adapt Internet protocols and “end to end” architecture?
Internet routes data using IP Addresses in Packets and Lookup tables in routers
Many levels of indirection between name and IP address
Works well for the Internet, and for support of Person-to-Person communication
Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems cant tolerate communication overhead of indirection

7. Energy is the bottleneck resource
Communication VS Computation Cost
Avoid communication over long distances
Cannot assume global knowledge, cannot pre-configure networks
Achieve desired global behavior through localized interactions
Empirically adapt to observed environment.

8. What is Localization
A mechanism for discovering spatial relationships between objects

9. Why is Localization Important?
Large scale embedded systems introduce many fascinating and difficult problems…
This makes them much more difficult to use…
BUT it couples them to the physical world
Localization measures that coupling, giving raw sensor readings a physical context
Temperature readings  temperature map

10. Variety of Applications
Passive habitat monitoring:
Where is the bird?
What kind of bird is it?

11. Variety of Application Requirements
Outdoor operation
Weather problems
Bird is not tagged
Birdcall is characteristic but not exactly known
Accurate enough to photograph bird
Infrastructure:
Several acoustic sensors, with known relative locations; coordination with imaging systems

12. Returning to our Application..
Choice of mechanisms differs:
Passive habitat monitoring:
Minimize environ. interference
No two birds are alike

13. Variety of Localization Mechanisms
Bird is not tagged
Passive detection of bird presence
Birdcall is characteristic but not exactly known
Passive target localization
Requires
Sophisticated detection
Large data transfers

14. Taxonomy of Localization Mechanisms
Active Localization
System sends signals to localize target
Cooperative Localization
The target cooperates with the system
Passive Localization
System deduces location from observation of signals that are “already present”
Blind Localization
System deduces location of target without a priori knowledge of its characteristics

15. Active Mechanisms Non-cooperative Cooperative Target
Synchronization channel
Ranging channel
Non-cooperative
System emits signal, deduces target location from distortions in signal returns
e.g. radar and reflective sonar systems
Cooperative Target
Target emits a signal with known characteristics; system deduces location by detecting signal
Cooperative Infrastructure
Elements of infrastructure emit signals; target deduces location from detection of signals
e.g. GPS

16. Passive Mechanisms Passive Target Localization Blind Localization
Synchronization channel
Ranging channel
Passive Target Localization
Signals normally emitted by the target are detected (e.g. birdcall)
Several nodes detect candidate events and cooperate to localize it by cross-correlation
Blind Localization
Passive localization without a priori knowledge of target characteristics ?

17. Embedded Networked Sensing (ENS):
Imagine if
High-rise buildings in Los Angeles were able to detect their own structural faults (e.g., weld cracks or plumbing infrastructure)
Buoys along the coast could alert surfers, swimmers, and fisherman to dangerous bacterial levels
An earthquake-rubbled building could be infiltrated with robots and sensors to locate signs of life and evaluate structural damage
We could infuse complex and endangered ecosystems with a plethora of chemical, physical, acoustic, and image sensors to track global change parameters continuously.
Dangerous bacterial and contaminant levels could be detected “on the farm”.
Embedded Networked Sensing (ENS):

18. Embedded Networked Sensing (ENS)
Embed numerous distributed devices to monitor and interact with physical world
Network devices to coordinate and perform higher-level tasks
Embedded
Networked
Exploit collaborative
Sensing, action
Control system w/ Small form factor Untethered nodes
CENS
Sensing
Tightly coupled to physical world
Exploit spatially and temporally dense, in situ, sensing and actuation

19. Transforming Research Agenda: From virtual to physical systems
Sensor-rich systems have very noisy inputs, outputs, and environmental dynamics
System implications of shift from macro to micro, and tethered to un-tethered, components
Application-driven, experimental research essential due to noisy (hard to model) inputs, outputs, and environment

20. Embedded Networked Sensing will reveal previously unobservable phenomena
Seismic Structure response
Contaminant Transport
Marine Microorganisms
Ecosystems, Biocomplexity

21. Example Applications Application Comparison
Seismic Sensing and Structure Response
Contaminant Transport Monitoring
Marine Microorganism Monitoring
Terrestrial Habitat Monitoring
Summary

22. Commonality
High spatial density (relative to characteristic dimensions).
Large number of sensors (100s  1000s).
Too many data to stream to central location.
Local processing at nodes to combat data size, energy constraints and latency.
Resource limitations.
Real-time monitoring.

23. Diversity

24. Seismic modeling and structure response: The Problem
Earthquake impact
Injury and loss of life
Financial loss (e.g., 1994 Northridge EQ: $20 B)
Interaction between ground motions and structure/foundation response not well understood.
Current seismic networks not spatially dense enough
to monitor structure deformation in response to ground motion.
to sample wavefield without spatial aliasing.

25. Challenges Real-time analysis for rapid response.
Massive amount of data  Smart, efficient, innovative data management and analysis tools.
Poor signal-to-noise ratio due to traffic, construction, explosions, ….
Insufficient data for large earthquakes  Structure response must be extrapolated from small and moderate-size earthquakes, and force-vibration testing.

26. Contaminant Transport Monitoring: The Problem
humans and ecosystems at risk.
Insufficient resources to fully mitigate pollution.
Need effective sensor networks to insure against exposure. 12
Responsible Party contributions
for cleanup of “Superfund” sites
(source: U.S. EPA, 1996) 10 8
Billions of dollars 6 4 2 1 9 8 1 9 8 5 1 9 9 1 9 9 5

27. Goals Science Technology/Applications
Understand intermedia contaminant transport and fate in real systems.
Identify risky situations before they become exposures.
Technology/Applications
Eliminate expensive, labor-intensive, error-prone field sampling.
Real-time accurate assessment of human and ecosystem risks (vs. best estimates based on sparse data).
Set resource allocation priorities according to quantitative criteria.
gas sampling probes
Belmont School (LA) mothballed before completion ($150M)--risks from toxic gas too uncertain.

28. Challenges Subterranean deployment.
Sensor modalities that lack commercial appeal (“exotic” contaminants).
Micro sizes for some applications (e.g., pesticide transport in plant roots).
Tracking contaminant “fronts”.
At-node interpretation of potential for risk (in field deployment).
Air Emissions
Water Well
Soil Zone
Spill
Path
Volatization
Dissolution
Groundwater

29. ENS Research Implications
Environmental Micro-Sensors
Sensors capable of recognizing phases in air/water/soil mixtures.
Sensors that withstand physically and chemically harsh conditions.
Microsensors.
Signal Processing
Nodes capable of real-time analysis of signals.
Contaminant
plume

30. Marine Microorganism Monitoring: The Problem
Marine microorganisms impact:
Human health (possible loss of life). (Drinking water ~ billions.)
Industries: fisheries and tourism. ( Algal blooms in US ~ $50M/yr.)
Problem becoming worse with human encroachment in coastal areas.
Conditions under which aquatic microorganisms develop not understood.
Methods for detecting microorganisms too slow and complex for timely intervention.

31. Goals Science Understand ecology of marine microorganisms.
Develop in situ observation.
Technology/Applications
Predict events involving proliferation of marine microorganisms, e.g. algal blooms.
Detection of harmful events.
Intervention to mitigate consequences
ENS will monitor marine environment at appropriate, yet unprecedented, spatial and temporal resolution

32. Challenges
Environmental data at spatial and temporal scale appropriate to organisms (micrometer sizes).
Rapid microorganism identification, eventually in situ. Typically, today, DNA sequencing.
Data acquisition without disturbing organisms.
Correlation of environmental and organismal data.
Detection and intervention methods suitable for field deployment.
Liquid environment: Communications, control, sensing.

33. ENS Research Implications
High spatial density (cm-mm), small sensors (cm-nm) of small size and limited capability.
Naive approach  Too many sensors, too many data.
Sensor-coordinated actuation and mobility, e.g., to deploy sensors and sample collectors where and when they are needed
Data processing inside network, e.g., trigger sensing and actuation.
Rapid microorganism identification in aquatic environment  New sensing techniques.

34. Terrestrial Habitat Monitoring: The Problem
Biodiversity and the supporting ecosystems
Contribute trillions of $ to global economy, directly (agriculture, fishing,...) and indirectly (flood control, CO2 removal, ...).
Changing rapidly with catastrophic consequences (e.g., desertification is estimated to cost $40B/year).
Biological and environmental complexity of ecosystems not well understood (e.g., effects of climate change, exotic species introduction, …).
Mitigation efforts costly and effects difficult to assess
Environmental laws.
Land acquisition (~ $900M/yr in the Land and Conservation Fund).

35. Goals
Science
Understand response of wild populations (plants and animals) to habitats over time.
Develop in situ observation of species and ecosystem dynamics.
Technology/Applications
Monitor ecosystem processes
Predict changes in populations and biodiversity with environmental change.
Accurately assess species presence and abundance, and changes to habitats.
1960
1995
A comparison of 25 years of vegetation change
ENS will reveal previously unobservable, complex patterns of organism interaction within dynamic ecosystems

36. Challenges
Data acquisition of physical and chemical properties, at various spatial and temporal scales, appropriate to the ecosystem, species and habitat.
Automatic identification of organisms (current techniques involve close-range human observation).
Measurements over long period of time, taken in-situ.
Harsh environments with extremes in temperature, moisture, obstructions, ...

37. ENS Research Implications
Diverse sensor sizes (1-10 cm), spatial sampling intervals (1 cm m), and temporal sampling intervals (1 ms - days), depending on habitats and organisms.
Naive approach  Too many sensors Too many data.
In-network, distributed signal processing.
Wireless communication due to climate, terrain, thick vegetation.
Adaptive Self-Organization to achieve reliable, long-lived, operation in dynamic, resource-limited, harsh environment.
Mobility for deploying scarce resources (e.g., high resolution sensors).

38. Model System
Environmental sensors in different habitats.
Multimedia sensors in natural habitats and artificial cavities (nest boxes).
Physiological sensors on trees and shrubs.
Primary nodes for higher level data processing and communications on towers.
Mobile platform for high resolution sensors and tele-robotic operation.

39. Field Experiments Monitoring ecosystem processes
Imaging, ecophysiology, and environmental sensors
Study vegetation response to climatic trends and diseases.
Species Monitoring
Visual identification, tracking, and population measurement of birds and other vertebrates
Acoustical sensing of birds for identification, spatial position, population estimation.
Education outreach
Bird studies by High School Science classes
Vegetation change detection
Avian monitoring
Virtual field observations

40. Ecosystems Monitoring
Dense network of physical, chemical sensors in soil and canopy
Measure and characterize previously unobservable ecosystem processes
Primary node
Secondary nodes

41. Species Monitoring
Video monitoring of habitat will quantify seasonal changes and life history of plants
Acoustical sensing of birds for species detection, identification spatial position, triggering.
Network of acoustical sensors with high spatial and temporal resolution.
Quantify spatial and temporal variation in call production over large scales
Spatial scale
Spectral pattern
Song and sonogram

42. Examples of Specific Applications
Real-time monitoring of
Microclimate
Hydrologic flow
Endangered species populations.
Forest health, spread of insect damage and disease.
Growth rates and reproduction.
In the context of
Compliance with environmental regulations.
K-12, and University science education.
Eco-tourism, public education.
National Forests management.
Parks and protected natural areas management.

43. Major Impact of ENS on Terrestrial Habitat Monitoring
ENS systems will transform the study of biocomplexity and global change by making it feasible to record detailed combinations and complex patterns of organism interaction within dynamic ecosystems.

44. Why databases? Sensor networks should be able to Users will need
Accept queries for data
Respond with results
Users will need
An abstraction that guarantees reliable results
Largely autonomous, long lived network

45. Why databases?
Sensor networks are capable of producing massive amounts of data
Efficient organization of nodes and data will extend network lifetime
Database techniques already exist for efficient data storage and access

46. Differences between databases and sensor networks
Static data
Centralized
Failure is not an option
Plentiful resources
Administrated
Sensor Network
Streaming data
Large number of nodes
Multi-hop network
No global knowledge about the network
Frequent node failure
Energy is the scarce
Resource, limited memory

47. Bridging the Gap
What is needed to be able to treat a sensor network like a database?
How should sensors be modeled?
How should queries be formulated?

48. Traditional Approach: Warehousing
Data is extracted from sensors and stored on a front-end server
Query processing takes place on the front-end.
Warehouse
Front-end
Sensor Nodes

49. What We’d Like to Do: Sensor Database System
Sensor Database System supports distributed query processing over a sensor network
Sensor DB
Sensor DB
Sensor DB
Sensor DB
Sensor DB
Front-end
Sensor DB
Sensor DB
Sensor DB
Sensor Nodes

50. Issues Representing sensor data Representing sensor queries
Processing query fragments on sensor nodes
Distributing query fragments
Adapting to changing network conditions
Dealing with site and communication failures
Deploying and Managing a sensor database system

51. Performance Metrics High accuracy Low latency Limited resource usage
Distance between ideal answer and actual answer?
Ratio of sensors participating in answer?
Low latency
Time between data is generated on sensors and answer is returned
Limited resource usage
Energy consumption

52. HOT HOT HOT!!!
This is a hot research area!!!People interested in publishing papers can make a head start..

53. References and Acknowledgements
DARPA SenseIT and NEST Programs
NSF Special Projects
Cisco, Intel
USC-ISI Collaborators Govindan, Heidemann, Silva
UCLA LECS members Bien, Bulusu, Braginsky, Bychkovskiy, Cerpa, Elson, Ganesan, Girod, Greenstein, Perelyubskiy, Yu,