1. Gathering Data in Wireless Sensor Networks Madhu K. Jayaprakash

2. Definition of Wireless Sensor Network Network formed by Nodes that are comprised of sensors, communication subsystems, storage and processing that are used to observe phenomena and answer user requests about the phenomena.

3. WSN Uses Traffic Control Observation of Natural phenomena (Zebra Net) Environmental Control Safety Military

4. Papers Reviewed Epidemic Routing for Partially- Connected Ad Hoc Networks [1] Energy-Efficient Computing for Wildlife Tracking: Design Tradeoffs and Early Experiences with ZebraNet [2] Data MULEs: Modeling a Three-Tier Architecture for Sparse Sensor Networks [3]

5. Focus of Papers - Routing Problem Statement How do we efficiently (and to a lesser extent timely) gather data from sensors that are widely dispersed in sub-optimal conditions and run on limited source of power. Goal Deliver data with high probability even when there is never a fully connected path from source to destination

6. Traditional Routing Methods Existing Infrastructure Base Station/Client Client uses high powered radio to communicate with Base Station No Pre-Existing Infrastructure Ad-Hoc Network Nodes connect to each other in a point to point fashion and route messages to other nodes

7. Drawbacks to Traditional Methods Existing Infrastructure Expensive to increase coverage area Powerful radio decreases battery life No Pre-Existing Infrastructure Higher density required to create robust network Partitioning is possible

8. Common Approaches in Paper Group Use Ad Hoc AND Base Station/Client architectures AND Buffering AND Mobility

9. Epidemic Routing (1) – System Design Parameters Sender is not in range of base station Sender does not know where receiver is currently located (Receiver may move) Pairs of nodes periodically come into communication range through node mobility

10. Epidemic Routing (2) - Protocol Nodes come into contact with each other Initiate dialog and transfer new messages Receiver determines if they have enough room

11. Epidemic Routing (3) – Analysis Radio power Higher Power – majority of messages delivered faster TTL Higher count – majority of messages delivered faster Buffer Space Higher space – majority of messages delivered faster Confirms Intuition Tradeoff to all three – More aggregate system resources used For Latency tolerable applications, power consumption can be mitigated by tweaking these three parameters

12. Epidemic Routing (4) – Future Work Hybrid Routing Route Discovery using GPS Queue Optimization Data structure Optimization

13. Zebra Net (1) – System Design Parameters Monitor Zebras over large distance Collect observations in field for 1 year Cannot place base stations in field Domain specific problems – Zebra behavior

14. Zebra Net (2) – System Architecture Node Collar with battery and solar cell Processing unit with 640kb flash (300 days of data) GPS unit two radios (100m and 8km) Base Station Mobile (car or plane)

15. Zebra Net (3) – Protocols Flooding History Based Flooding Successful transfers determine metric Metric decays over time

16. Zebra Net (4) – Analysis Unlimited resources Flooding - fastest and highest rate message delivery Flooding - Most aggregate system resource usage Storage Constraint Flooding – adversely affected History based flooding more perform Radio Power Constraint Peer to Peer needed a less powerful radio achieve 100% data delivery Confirms Intuition

17. Zebra Net (5) – Future Work Position-based routing Self-adaptive decisions on the number of nodes to forward to Mobility Models

18. Mule (1) – System Design Parameters Sensors are stationary Power Consumption at sensors is overriding concern Application can tolerate latency

19. Mule (2) – System Architecture Mobile node Large Storage Capacities Renewable power Can communicate with sensors and base station

20. Mule (3) – System Performance Modeling Modeled Data Success Rate Sensor buffer size Mule buffer size Number of Sensors Number of Mules Number of Access Points

21. Mule (4) – System Performance Modeling Results – Verify Intuition Buffer at sensor needs to scale with grid size Latency increases with grid size Both i and ii can be addressed by adding more Mules Mule buffer needs to increase with grid size Access Points need to increase with grid size Increasing Access Points allow a reduction in Number of mules and mule buffers.

22. Mule(5) – Future Work Improve Model Assumption Mobility Models Error free communications Infinite bandwidth Model end to end latency

23. Paper Group Summary Use Ad Hoc AND Base Station/Client approach AND Buffering AND Mobility TO Deliver data with high probability even when there is never a fully connected path from source to destination

24. Related Works Smart DUST Directed Diffusion TAG: In network processing