maximizing the lifetime of wsn using vbs
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Maximizing the lifetime of WSN using VBS. Yaxiong Zhao and Jie Wu Computer and Information Sciences Temple University. Road map. Introduction and background Centralized scheduling STG-based approach VSG-based approach Distributed implementation Iterative local replacement - PowerPoint PPT PresentationTRANSCRIPT
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Maximizing the lifetime of WSN using VBS
Yaxiong Zhao and Jie WuComputer and Information Sciences
Temple University
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Road map
Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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Road map Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
![Page 4: Maximizing the lifetime of WSN using VBS](https://reader036.vdocuments.net/reader036/viewer/2022081515/5681355c550346895d9cc2a1/html5/thumbnails/4.jpg)
Introduction
The need of reducing energy consumption and extending the network lifetime The most important challenge
We have only one general technique Duty-cycling To exploit the redundancy in sensors
Traffic is low Letting sensors work all the time is redundant for
transmitting data
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The redundancy in the network level
Usually there are more-than-enough sensors deployed in the network For reliability and QoS
The same degree of redundancy is not necessary for communication Low traffic Static network 99.8% delivery ratio
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Our idea
Scheduling multiple backbones to maintain the connectivity
Backbone sensors use duty-cycling to further reduce energy consumption
Turn off other sensors' radios The independent backbones is not
optimal In the example overlapped backbones help
further extend network lifetime
0 1
2 3 4
sink
0 1
2 3 4
sink
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Maximum lifetime backbone scheduling
An example {Sink, 0, 1} work for 1 unit {Sink, 0, 3} work for 1 unit {Sink, 1, 3} work for 2 units Total network lifetime of 4 units of time
Find a schedule <b0, t0> … <bi, ti>
A backbone bi works for ti round(s) Has the longest network lifetime
NP-hard Reduce from the maximum set cover (MSC)
problem
0 1
2 3 4
sink
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Road map
Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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Scheduling Transition Graph
The time is divided into multiple rounds A backbone is selected at each round
The residual energy of each sensor is recorded with each backbone at each round
A fixed amount of energy is consumed in each round
Enumerate candidate backbones Form a graph representing the schedule
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STG (cont'd)
{B1, E1}
{B2, E2}
{B3, E3}
{Bp, Ep}
{B1, E1}
{B2, E2}
{B3, E3}
{Bp, Ep}
{B1, E1}
{B2, E2}
{B3, E3}
{Bp, Ep}
Round 1 Round 2 Round i ……
Backbone transition
Initial
Round 0 {B, E} are: The backbone The associated residual
energy of all the sensors in the network
A path in the STG represents a schedule
Path ends when at least one sensor depletes energy
The purpose of our algorithm is to find the longest path
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Road map Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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Virtual Scheduling Graph
Transform a sensor into multiple virtual nodes Each virtual node represents a fixed amount of energy
And has a virtual ID The energy consumed in each round
Virtual nodes are connected based on several rules The virtual nodes of the same sensor form a clique The virtual nodes of the neighboring sensors connect
correspondingly with increasing order
virtual node of C
virtual node of A
virtual node of B
0
0
1
0
1CB
A
2
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VSG (cont’d)
VSG works by sequentially finding the CDS Then remove the selected nodes Until a sensors' virtual nodes have all been removed
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Road map Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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Iterative local replacement
Let each sensor find replacements locally Sensors that have less energy should have a
higher chance to switch than those that have more energy Ec is the energy consumed since the last time
working as a backbone Er is the current residual energy
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Experiment results
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Conclusion and future work
A new scheduling method Two centralized approximation algorithms A distributed implementation
More theoretical inquires are needed Testbed implementation