maximizing the lifetime of wsn using vbs
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Maximizing the lifetime of WSN using VBS
Yaxiong Zhao and Jie WuComputer and Information Sciences
Temple University
Road map
Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
Road map Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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
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
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
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
Road map
Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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
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
Road map Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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
VSG (cont’d)
VSG works by sequentially finding the CDS Then remove the selected nodes Until a sensors' virtual nodes have all been removed
Road map Introduction and background Centralized scheduling
STG-based approach VSG-based approach
Distributed implementation Iterative local replacement
Conclusion and future work
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
Experiment results
Conclusion and future work
A new scheduling method Two centralized approximation algorithms A distributed implementation
More theoretical inquires are needed Testbed implementation
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