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Battery-Aware Router Scheduling in Wireless mesh Networks

Chi Ma, Zhenghao Zhang and Yuanyuan Yang

Keon JangSA Lab, KAIST

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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

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Introduction

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

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Battery Discharging and Recovery

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.

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Modeling Battery Diacharging Behavior (1/3)

: Current

: Residual Charge before epoch

: Residual Charge after epoch

: Duration

: Discharging Loss

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Modeling Battery Discharging Behavior (2/3)

Amount of battery discharging loss in the ith epoch.

The model that computes the energy dissipated by the battery during the ith epoch.

: Energy consumed by device

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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.

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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.

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Battery Lifetime Optimization Scheduling (BLOS) (2/3)

: minimum time interval

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Battery Lifetime Optimization Scheduling (BLOS) (3/3)

Using BLOS battery lifetime increased 14.7%

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Hot Spot Covering Under BLOS Policy

SCBP can be transformed to Subset Partition problem.

Subset Partition Problem is NP Hard.

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Spanning Tree Mesh Router Scheduling under BLOS Policy

This algorithm has O( r) time complexity.

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Spanning Tree Mesh Router Scheduling under BLOS Policy

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Performance Evaluations

BLOS shows up to 21% longer lifetime compare to GS

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Performance Evaluations

A :50 Routers, 15 Hot spotsB :100 Routers, 40 Hot spots

A :B :

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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.