A Distributed Algorithm for Joint Sensing and Routing in Wireless Networks with
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A Distributed Algorithm for Joint Sensing and Routing in Wireless Networks with Non-Steerable Directional Antennas. Chun Zhang * , Jim Kurose + , Yong Liu ~ , Don Towsley + , Michael Zink + * IBM T.J. Watson Research Center + Dept of Computer Science, University of Massachusetts at Amherst

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Chun Zhang * , Jim Kurose + , Yong Liu ~ , Don Towsley + , Michael Zink +

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Chun zhang jim kurose yong liu don towsley michael zink

A Distributed Algorithm for Joint Sensing and Routing in Wireless Networks with Non-Steerable Directional Antennas

Chun Zhang*, Jim Kurose+, Yong Liu~, Don Towsley+, Michael Zink+

* IBM T.J. Watson Research Center

+ Dept of Computer Science, University of Massachusetts at Amherst

~ Dept of Electrical & Computer Engineering, Polytechnic University

Nov 14, 2006 ICNP


Outline

Outline

  • motivation

  • problem formulation

  • distributed algorithm

  • result

  • summary


Multi hop wireless sensor networks

Multi-hop wireless sensor networks

applications: weather monitoring

sink A

  • sensor nodes

  • directional-antenna links

  • link capacity constraints

    • 802.11 protocol: 2/5.5/11Mbps

  • energy constraints

    • energy supplied by solar panel

sink B

  • performance metric

    • amount of information delivered to sinks


Interesting problem

application layer

radio layer

sensing energy

communication energy

sensing rate (information)

link capacities

routing solution ?

Interesting problem ?

limited energy

capacity generator

demand generator

or more capacity?

more demand ?

network layer


Our contribution

Our contribution

  • joint optimization problem formulation for energy allocation (between sensing, data transmission, and data reception), and routing

  • distributed algorithm to solve the joint optimization problem, with its convergence proved

  • simulation to demonstrate the energy balance achieved in a network of X-band radars, connected via point-to-point 802.11 links with non-steerable directional antennas


Related work

Related work

  • [Lin,[email protected]] [Eryilmaz,[email protected]]

    • joint rate control, resource allocation, and routing in wireless networks

  • our work further considers energy consumption for

    • data sensing

    • data reception


Outline1

Outline

  • motivation

  • problem formulation

  • distributed algorithm

  • result

  • summary


Resource model

Resource model

  • power resource

    • three power usages: data sensing, data transmitting, data reception

    • power is a convex and increasing function of data rate

    • constraint: consumption rate ≤ harvest rate

  • link capacity resource

    • constraint: link data rate ≤ link capacity

  • resource constraints satisfied by penalty functions


Goal information maximization

Goal : information maximization

informationmodeled by utility function

  • : node i sensed and delivered data rate

  • node i collected information

assumption: is a concave

and increasing function


Optimization problem formulation

Optimization problem formulation

Joint sensing and routing problem

s: sensing rates; X: data routes

routes X deliver sensing rates s to data sink


Transforming joint sensing routing problem to routing problem with fixed demands

difference link

sensing link

i’

Transforming joint sensing/routing problem to routing problem with fixed demands

sensing power ->

reception power

i

wireless sensor network

idea: treat data sensing as data reception through sensing link


Transformed problem

Transformed problem

Routing problem with fixed traffic demand

fixed demand: maximum sensing rates; X: data routes

routes X deliver maximum sensing rates to data sink


Outline2

Outline

  • motivation

  • problem formulation

  • distributed algorithm

  • result

  • summary


Distributed algorithm generalize gallager77 wired network algorithm

Distributed algorithm: generalize [Gallager77] wired network algorithm

  • wired network

    • link-level resource constraint

  • wireless network

    • node-level resource constraint

How to generalize from link-level to node-level?


Generalized distributed algorithm

Generalized distributed algorithm

  • generalize algorithm from wired network (link-level) to wireless network (node-level)

    repeat, until all traffic loaded on optimal path

    • each link locally compute gradient information

    • gradient information propagated from downstream to upstream in accumulative manner

    • routing fractions adjustment from non-optimal path to optimal path

  • for generalized gradient-based algorithm:

    • prove convergence

    • provide step-size for routing fraction adjustment


Outline3

Outline

  • motivation

  • problem formulation

  • distributed algorithm

  • result

  • summary


Simulation scenario

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Simulation scenario

goodput rate

From CASA student testbed

  • energy harvest rate: 7-13W

  • X-band radar-on power: 34W

  • radar-on rate 1.5Mbps

  • link-on trans power: 1.98W

  • link-on receive power: 1.39W

  • link-on goodput rate: as shown

  • Utility function

1Mb

2Mb

5.5Mb

2Mb

1Mb


Optimization results for different energy harvest rates

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Optimization results for different energy harvest rates

  • As power budget increases

  • utility and sensing power increase

  • communication power first increases, then decreases and flats out


Node level energy balance for different energy harvest rates

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Node level energy balance for different energy harvest rates

  • power rich network: max-min fair (single-sink) : sensing rates not affected by choice of utility functions

power budget = 13W

power budget = 9W

  • power constrained network: close to sink nodes spend less energy on sensing


Summary a distributed algorithm for joint sensing and routing in wireless networks

Summary: a distributed algorithm for joint sensing and routing in wireless networks

Goal : a distributed algorithm for joint sensing and routing

Approach :

  • mapping joint problem to routing problem

  • proposed a distributed algorithm with convergence proof and step size

    Simulation to demonstrate energy balance for different energy harvest rates:

  • energy rich: proven max-min fairness (for single sink)

  • energy constrained: close-to-sink nodes spend more energy on communication, and thus less energy on sensing


Thanks questions

Thanks !Questions ?


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