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 … A backbone b i works for t i 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 E c is the energy consumed since the last time working as a backbone E r 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