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Battery-Aware Router Scheduling in Wireless mesh Networks

Battery-Aware Router Scheduling in Wireless mesh Networks. Chi Ma, Zhenghao Zhang and Yuanyuan Yang Keon Jang SA Lab, KAIST. Table of Contents. Introduction Battery Discharging and Recovery Modeling Battery Discharging Behavior Battery Lifetime Optimization Scheduling

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Battery-Aware Router Scheduling in Wireless mesh Networks

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  1. Battery-Aware Router Scheduling in Wireless mesh Networks Chi Ma, Zhenghao Zhang and Yuanyuan Yang Keon Jang SA Lab, KAIST

  2. Table of Contents • Introduction • Battery Discharging and Recovery • Modeling Battery Discharging Behavior • Battery Lifetime Optimization Scheduling • Hot Spot Covering Under BLOS Policy • Spanning Tree Mesh Router Scheduling under BLOS Policy • Performance Evaluation • Conclusion

  3. Introduction • When discharging, batteries tend to consume more power than needed, and can reimburse the over-consumed power later.

  4. Active species are consumed at the electrode surface and replenished by diffusion from the bulk of the electrolyte. Diffusion process cannot keep up with the consumption, and a concentration gradient builds up across the electrolyte. We refer to the unused charge as discharging loss. Battery Discharging and Recovery

  5. Modeling Battery Diacharging Behavior (1/3) : Current : Residual Charge before epoch : Residual Charge after epoch : Discharging Loss : Duration

  6. Modeling Battery Discharging Behavior (2/3) The model that computes the energy dissipated by the battery during the ith epoch. : Energy consumed by device Amount of battery discharging loss in the ith epoch.

  7. Modeling Battery Discharging Behavior (3/3) Residual discharging loss at time t. • Obviously, to recover the battery perfectly, it takes infinite amount of time. • Assume c is a fairly small constant, which is the power to transmit a packet. • If discharging loss is less than c, the battery can be considered as well-recovered.

  8. Battery Lifetime Optimization Scheduling (BLOS) (1/3) given and under optimal policy. As n increases, is increased. However, this increasing is not monotonic because the accumulation of overhead also increases.

  9. Battery Lifetime Optimization Scheduling (BLOS) (2/3) : minimum time interval

  10. Battery Lifetime Optimization Scheduling (BLOS) (3/3) • Using BLOS battery lifetime increased 14.7%

  11. Hot Spot Covering Under BLOS Policy • SCBP can be transformed to Subset Partition problem. • Subset Partition Problem is NP Hard.

  12. Spanning Tree Mesh Router Scheduling under BLOS Policy This algorithm has O( r) time complexity.

  13. Spanning Tree Mesh Router Scheduling under BLOS Policy

  14. Performance Evaluations BLOS shows up to 21% longer lifetime compare to GS

  15. Performance Evaluations A :50 Routers, 15 Hot spots B :100 Routers, 40 Hot spots A : B :

  16. Conclusion • Battery Life Optimization Scheduling to maximize the life time of battery. • Proved Spot Covering under BLOS Policy problem is NP Hard. • Presents Approximation algorithm (STS) to improve lifetime of battery powered mesh network.

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