gathering data in wireless sensor networks madhu k. jayaprakash
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Gathering Data in Wireless Sensor Networks
Madhu K. Jayaprakash
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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.
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WSN Uses
Traffic Control Observation of Natural
phenomena (Zebra Net) Environmental Control Safety Military
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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]
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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
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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
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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
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Common Approaches in Paper Group
Use Ad Hoc AND Base Station/Client architectures AND
Buffering AND Mobility
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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
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Epidemic Routing (2) - Protocol
Nodes come into contact with each other Initiate dialog and transfer new messages Receiver determines if they have enough room
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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
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Epidemic Routing (4) – Future Work
Hybrid Routing Route Discovery using GPS Queue Optimization Data structure Optimization
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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
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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)
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Zebra Net (3) – Protocols
Flooding History Based Flooding
Successful transfers determine metric
Metric decays over time
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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
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Zebra Net (5) – Future Work
Position-based routing Self-adaptive decisions on the
number of nodes to forward to Mobility Models
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Mule (1) – System Design Parameters
Sensors are stationary Power Consumption at sensors is
overriding concern Application can tolerate latency
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Mule (2) – System Architecture
Mobile node Large Storage Capacities Renewable power Can communicate with sensors and base station
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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
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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.
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Mule(5) – Future Work
Improve Model Assumption Mobility Models Error free communications Infinite bandwidth
Model end to end latency
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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
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Related Works
Smart DUST Directed Diffusion TAG: In network processing