1. SENSOR SEnsor Network desigN and ORganization
Sameer Tilak
Advisor- Dr. Nael Abu-Ghazaleh
Committee Members-
Dr. Michael Lewis
Dr. Wendi Heinzelman
Copyright 2002 Sameer Tilak

2. Problem Overview Efficient protocol architectures for sensor networks
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3. Introduction to Sensor Nets
What is a sensor -network ?
What are real world applications ?
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4. Real World Applications
Remote surveillance
Temperature sensing
Animal tracking in a forest
Taxi-cab for traffic condition information
Tornado motion/pattern study
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5. Motivation
Embed numerous distributed devices to monitor and interact with physical world: hospitals, homes, vehicles, and “the environment”
Disaster Response
Circulatory Net
Network these devices so that they can coordinate to perform higher-level tasks.
Requires robust distributed systems of hundreds or thousands of devices.
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6. Challenges Low Battery power Low bandwidth Error-prone air medium
Low computing power and memory
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7. Unique Characteristics
No end-to-end communication
Co-operative operation
Redundancy in information
Application specific specialized networks
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8. End-to-end Communication
Client A
Elton John Music
Can you ???
Client B
Knuth’s book
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9. Co-operative + Redundancy
Sensor network for animal tracking
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10. Performance Metrics Life-time Accuracy Latency Scalability
Fault-tolerance
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11. Taxonomy Need Application specific nature
Systematic classification to aid development of protocols
Helpful to sensor network designer
Insight into sensor-network working model
Isolation of important factors
Fair comparison of different protocols
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12. Components of Sensor-Net
Observer
Phenomenon
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13. Taxonomy Sensor Network Architecture Communication Models
Data-delivery Models
Network Dynamics / Mobility
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14. Sensor Network Architecture
Infrastructure:- Sensors and current deployment, H/W properties of sensors
Network Protocol:- Creating paths for communication
Observer/Application:- “Interested” in phenomenon.
We classify issues that influence Network Protocol.
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15. Communications Models
Application Level Communication:- Transferring information regarding Phenomenon towards observer.
Infrastructure level communication:- Communication for configure, maintain and optimize sensor network itself. (Overhead).
Network protocol should support both.
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16. Data-delivery Model Continuous:- example temperature sensing
Event-driven:- example animal tracking
Observer-driven:- request/reply
Hybrid:- Combination of any of the above.
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17. Network dynamics
Important from comm. since degree and type of comm. Is dependant on dynamics.
Mobile observer (Plane flying)
Mobile phenomenon (Animal tracking)
Mobile sensors (taxicab)
Each of the above requires different organization, data delivery models and thus different protocols.
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18. Addressing Mobility in SN
Again no End-to-End communication so solutions for ad-hoc mobility are not applicable.
Reactive approach e.g. Observer initiated.
Proactive approach e.g. sensor initiated patching procedure similar to soft handoff.
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19. Importance of these factors
Protocol Development heavily depends on traffic characteristics
Efficient/optimized protocols are possible with insight of accurate model
Application specific knowledge and infrastructure knowledge both used
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20. Infrastructure tradeoffs study
Why ? Non-Intuitive results
Expectations:-
High Accuracy, low delay, longer lifetime
Solution:- More sensors -> More information, high accuracy, high fault tolerance and longer life-time….
Is this solution right ???
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21. Infrastructure Features
Sensor Capabilities
Number of Sensors
Deployment strategies
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22. Number of Sensors
Simple Analysis
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23. Meaning of equations Channel capacity upper bound
Application specific criteria (e.g., accuracy lower bound).
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24. Deployment strategies
Grid like
Random uniform
Biased deployment
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25. Sensor Network As 15x15 Grid
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26. Random Deployment of 100 Sensors
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27. Biased Deployment of 100 Sensors
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28. Evaluation Environment
Framework extends ns-2
Flexibility, realistic modeling
Separation of Phenomenon, sensors and observer
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29. Parameters to study Continuous and phenomenon driven model
Traditional:- Goodput, delay, energy expenditure
New:- accuracy as a proof of concept for any application specific performance metric.
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30. Accuracy model Relative distance determines accuracy Lion(10,15)
Someone(25,30)
I enjoy my sleep
Sensing range
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31. Goodput Study
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Grid
Random

32. Delay Study
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Grid
Random

33. Accuracy Study
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34. Energy Expenditure study
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35. Energy Depletion Study
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36. Continuous data model
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10 x 10 Grid

37. Biased Versus Uniform
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38. Copyright 2002 Sameer Tilak
Effect of Bandwidth
Goodput
Accuracy

39. Capacity versus Path length
Goodput drops with increase in avg. path length
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40. Network Protocol Effect
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DSR, AODV similar DSDV Bad

41. Congestion Management
Defining congestion management
Same problem as in internet then why not use the same solution ??? Again no end-to-end communication.
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42. Background for CM
Desired goals:- Internet:- Maximize aggregate throughput with fairness. Sensor Networks:- Get information that satisfies application requirements.
Why is this so different then ?
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43. Internet Congestion Copyright 2002 Sameer Tilak Elton John Music
Client A
Elton John Music
Can you cut off either one???
Client B
Knuth’s book

44. Co-operative + Redundancy
Sensor network for animal tracking
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45. Optimal region of operation
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46. Optimal region of operation
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Optimal region of operation
To keep the system operating in this optimal region

47. CM Alternatives
Feedback based (some communication) Who detects congestion ??? 1. Observer based feedback 2. In-network feedback 3. Hybrid
No feedback [No communication]
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48. Basic points Co-operative communication pattern.
Redundant information.
Basically As long as lower threshold determined by application is maintained observer is happy.
Fairness in not the goal.
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49. Our Solution Congestion Avoidance
Unbiased Algorithm: No application knowledge is used. Every sensor is treated accuracy.
Biased Algorithm: Application knowledge is used. Application specific policy is used. We just provide mechanism to do it. Policy versus mechanism separation, cleaner solution.
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50. Unbiased Algorithm Probabilistic algorithm.
All sensors are equal. Each sensor after sensing the phenomenon sends with certain probability. However this sending probability is same for all sensors.
Is this the best way to do ???
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51. Problem with Unbiased model
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Problem with Unbiased model
Relative distance determines accuracy
Don’t send
Lion(10,15)
Send
Lion(5,5)
Lion(4,4)
Someone(25,30)
I enjoy my sleep
Sensing range

52. Biased model Relative distance determines accuracy
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Biased model
Relative distance determines accuracy
Send
Lion(10,15)
Don’t send
Lion(5,5)
Lion(4,4)
Someone(25,30)
I enjoy my sleep
Sensing range

53. Advantages Completely localized solution.
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Advantages
Completely localized solution.
No local/global communication required.
Minimum overhead
simplicity desirable for SN.
Does this solve all problems ???

54. Disadvantages
No guarantee that at least one sensor will report because: Probabilistic nature. Does not adopt dynamically to the changing conditions very much.
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55. Copyright 2002 Sameer Tilak
Preliminary results

56. Future work for CM
Feed-back based methods: More adoptive. More complex. Require communication overhead.
Tradeoff might be worth studying…..
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57. Future Work Making taxonomy more comprehensive.
Development of better Congestion Management strategies (discussion and Implementing phase).
Exploring novel protocols, that support data-fusion (Discussion phase).
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58. Conclusions
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Overall communication in sensor network is application driven and data-centric. Taxonomy will be helpful in designing and evaluation of protocols for sensor-nets.
Taxonomy will assist development of simulation models to study performance of different sensor-net organization and making design and deployment decisions.

59. Conclusions continued.
Copyright 2002 Sameer Tilak
Conclusions continued.
SN organization: Infrastructure, Network protocol, Application/Observer.
Communication models, data delivery models, Network dynamics influence network protocol.
Mobility is SN should be handled differently than in Ad-Hoc e.g. reactive, proactive approach...

60. Conclusions continued….
Copyright 2002 Sameer Tilak
Conclusions continued….
Infrastructure tradeoffs study shows: If not properly managed, deploying more sensors may end-up in harming performance of network according to both network and application metrics.
Channel capacity id the upper bound on collective data communication and application requirements is the lower bound.

61. Conclusions continued….
Different deployment strategies studied show: No appreciable difference in terms of application and n/w metrics between grid and uniform random then random = low cost and effort. Biased network deployment if managed properly can be a better alternative.
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62. Conclusions continued...
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Congestion could be a real problem for SN.
Network protocol should do “Intelligent management of sensors” e.g., Congestion Management.
There exists an optimal region of operation from both application and network point of view. Network protocol should operate SN in this region.