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Energy-Efficient Geographical Forwarding Algorithm for Wireless Ad Hoc and Sensor Networks

Presenter: Zhendong Lun

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Contents

Introduction 1) Energy-efficient routing

2) Location-based (position-based) routing

3) Energy-efficient location-based routing

Proposed Algorithm 1) Network model

2) Algorithm design

Simulation Results Conclusion Reference

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Energy-efficient routing

Goal: to achieve power efficient, multi-hop communication

in ad hoc and sensor networks.

Types:1. Topology Control: dynamically chooses the transmit range of each node

in such a way that energy consumption is reduced.

2. Power Aware Routing: using some power-aware metrics for determining routes to save energy for multi-hop packet delivery.

3. Sleep Scheduling: chooses some sensors to sleep in order to reduce the energy wasted in an idle state.

4. Globalized Approach: integrates different states of the network(i.e., transmission/reception/idle) into a joint optimization problem, in order to minimize energy consumption.

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Location-based (position-based) routing

Goal: make routing decision to the destination based on

node geographic position and the position of its

one-hop neighbors.

Types:1. Basic distance, progress, and direction based methods

2. Partial flooding and multi-path based path strategies

3. Depth first search based routing with guaranteed delivery

4. Nearly stateless routing with guaranteed delivery

5. Power and cost aware routing

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Energy-efficient location-based routing

Goal: makes local routing decisions in order to build a

near-optimal power-efficient end-to-end path.

Extra information needed:i.e., energy cost for each path,

node residual energy

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Network Model

Graph G=(V,E)V: set of nodes, E: set of links connecting nodes : residual energy for node x V(G)

xE

One-hop topology

N(x) is the set of one-hop neighbors of x

xG

( ) ( ) { }xV G N x x

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Algorithm Design

The network lifetime of a WSN is basically determined

by two factors:1) The energy consumed for per packet end-to-end delivery

2) The energy draining rates at individual nodes

Minimize the energy loss at nodes for packet delivery

(min-power routing issue)

Select the paths with the maximal residual energy

Network lifetime highly depends on how these two measurements can

be compromised with the assistance of the limited local state information

kept at nodes.

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Algorithm Design(cont.)

Routing Algorithm

1) Simple mechanisms for energy criticality determining

Select the paths with the maximal residual energy

2) Next hop selection using localized Dijkstra’s algorithm

Minimize the energy loss at nodes for packet delivery

3) Integration of energy criticality avoidance and localized

Dijkstra’s algorithm

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Energy Criticality Determining

Each node can independently determine if it is currently an

energy-critical node in the network.

This procedure has a little communication overhead.

1) the full energy space is divided into L equally-

space intervals

2)

3) a node floods its energy index value across the

network during the following conditions:

a) when the network is initially deployed

or

b) when its energy index changes(drops) into

the energy-critical region.

( )

full energy

residual energy

L a small positive integer

energy index with node x

r A small number, 0<r<1

K A positive integer

maxE

xE

xL

max* /x xL L E E

xL K

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Next Hop Selection Using Localized Dijkstra’s Algorithm

Procedure for a packet holder (either an intermediate node or the

source node) to select its next hop.

Each packet holder applies Dijkstra’s algorithm to its local topology

built as follows:

P(u,v)=

( )x V G 'xG

( ') { | ( ) } { } { }x ut xtV G u u N x d d x t

( ') {( , ) | ( ') ( ')}x x xE G u v u V G v V G

( * ) / euv va d c E v t

1 1

* *( *( 1) / ) * *( ( 1) / )

( )ut ut

ev

d c a c d a a c

E

v t

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Next Hop Selection Using Localized Dijkstra’s Algorithm (cont.)

• Implement Dijkstra’s algorithm on , in order to find the next hop of x.• Upon receiving the packet, the next hop will repeat the same operations.• This behavior repeats until the destination t is reached.

Based on the localized Dijkstras’s algorithm, the chosen path is:

x u v t, total weight is 12.5

'xG

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Integration of Energy Criticality Avoidance and Localized Dijkstra’s Algorithm

• Define a set of energy criticality ratios as{r1, r2, …, rk}, sorted in a decreasing

order.• For an node x to choose its next hop, these ratios will be enforced sequentially.• First round, only consider the neighbor nodes whose residual energy above the

energy criticality level determined by r1.• If no next hop is found using localized Dijkstra’s algorithm, r2 is then

enforced.• This process continues until all neighbor nodes of x are considered as next hop

candidates.

However, if no next hop that makes positive progress can be found, one-hop local

flooding of the packet is used for a rescue to overcome the local maxima issue.

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Simulation Results

Compare the average network lifetime between this proposed

algorithm (DECA) and the power-cost2 algorithm.

The network lifetime is measured as the time when the first node

runs out of its energy.

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Simulation Results (cont.)

Single-sink WSNS

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Simulation Results (cont.)

Four-sink WSNS

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Conclusion

To achieve prolonged network lifetime, the proposed algorithm design

assumes network nodes keep their respective one-hop neighborhood view

and employs the strategies of localized implementation of Dijkstra’s

algorithm and energy-criticality avoidance in next hop selection for

packet forwarding.

Simulation results demonstrate that this designed algorithm can prolong

the network lifetime as compared with related work.

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Reference

Q. Yu, B. Zhang, C. Liu, and H.T. Mouftah, “Energy-Efficient Geographical

Forwarding Algorithm for Wireless Sensor Networks,” Proceedings IEEE

Wireless Communications and Networking Conference WCNC2008

(Networking Track), Las Vegas, Nevada, April 2008, pp. NET16.1.1-NET16.1.6

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Questions/Comments???