Gathering Data in Wireless Sensor Networks Madhu K. Jayaprakash

Gathering Data in Wireless Sensor Networks

Madhu K. Jayaprakash

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.

WSN Uses

Traffic Control Observation of Natural

phenomena (Zebra Net) Environmental Control Safety Military

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]

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

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

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

Common Approaches in Paper Group

Use Ad Hoc AND Base Station/Client architectures AND

Buffering AND Mobility

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

Epidemic Routing (2) - Protocol

Nodes come into contact with each other Initiate dialog and transfer new messages Receiver determines if they have enough room

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

Epidemic Routing (4) – Future Work

Hybrid Routing Route Discovery using GPS Queue Optimization Data structure Optimization

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

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)

Zebra Net (3) – Protocols

Flooding History Based Flooding

Successful transfers determine metric

Metric decays over time

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

Zebra Net (5) – Future Work

Position-based routing Self-adaptive decisions on the

number of nodes to forward to Mobility Models

Mule (1) – System Design Parameters

Sensors are stationary Power Consumption at sensors is

overriding concern Application can tolerate latency

Mule (2) – System Architecture

Mobile node Large Storage Capacities Renewable power Can communicate with sensors and base station

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

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.

Mule(5) – Future Work

Improve Model Assumption Mobility Models Error free communications Infinite bandwidth

Model end to end latency

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

Related Works

Smart DUST Directed Diffusion TAG: In network processing