Maximizing the lifetime of WSN using VBS

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