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Maximizing the lifetime of WSN using VBS

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

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Maximizing the lifetime of WSN using VBS

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  1. Maximizing the lifetime of WSN using VBS Yaxiong Zhao and Jie Wu Computer and Information Sciences Temple University

  2. Road map • Introduction and background • Centralized scheduling • STG-based approach • VSG-based approach • Distributed implementation • Iterative local replacement • Conclusion and future work

  3. Road map • Introduction and background • Centralized scheduling • STG-based approach • VSG-based approach • Distributed implementation • Iterative local replacement • Conclusion and future work

  4. 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

  5. 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

  6. 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

  7. 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

  8. Road map • Introduction and background • Centralized scheduling • STG-based approach • VSG-based approach • Distributed implementation • Iterative local replacement • Conclusion and future work

  9. 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

  10. STG (cont'd) • {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

  11. Road map • Introduction and background • Centralized scheduling • STG-based approach • VSG-based approach • Distributed implementation • Iterative local replacement • Conclusion and future work

  12. 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

  13. VSG (cont’d) • VSG works by sequentially finding the CDS • Then remove the selected nodes • Until a sensors' virtual nodes have all been removed

  14. Road map • Introduction and background • Centralized scheduling • STG-based approach • VSG-based approach • Distributed implementation • Iterative local replacement • Conclusion and future work

  15. 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

  16. Experiment results

  17. 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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