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Energy-Efficient Forwarding Strategies for Geographic Routing in Lossy Wireless Sensor

Networks

Wireless and Sensor Network SeminarDec 01, 2004

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From

SenSys’04• November 03 ~ 05, 2004, Baltimore, Maryland, USA.

University of Southern California• Authors: Karim Seada, Marco Zuniga, Ahmed Helmy,

Bhaskar Krishnamachari

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Outline

Background of Geographic (Position-based) Routing Problem in Lossy Wireless Sensor Networks Proposed Optimal Forwarding Strategies Analysis and Performance Comparison

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Geographic Routing A commonly used paradigm in wireless ad-hoc

sensor networks when location information is available • Advantage: Conserve energy and bandwidth since discover

floods only within a single hop• Main component: A greedy forwarding mechanism – each

node forwards a packet to the neighbor that is closest to the destination.

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Greedy Forwarding Strategies(Basic geographic routing algorithm)

Forward the packet to the neighbor that makes the most progress towards (is closest to) the destination

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Problem Assumptions for greedy routing:

(i) sufficient network density(ii) accurate localization(iii) high link reliability independent of distance within the

physical radio range

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Problem (Cont.) Wireless links in real sensor networks can be

extremely unreliable, deviating to a large extent from the idealized perfect-reception within-range models used in simulation

This weak-link problem and the related distance hop trade-off, whereby energy efficient geographic forwarding must strike a balance between shorter, high-quality links, and longer lossy links.

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Sample of a Realistic Analytical Link Loss Model

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Contributions Detailed study of geographic routing in the context of

lossy wireless sensor networks:• A mathematical analysis of the optimal forwarding distance

for both ARQ and No-ARQ scenarios is presented,

Detailed simulation and analysis in several novel blacklisting and neighbor selection / geographic forwarding strategies.

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Key Result The product of the packet reception rate (PRR)

and the distance traversed towards destination is the optimal forwarding metric for the ARQ case, and is a good metric even without ARQ.

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Analytical Link Model

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A Realistic Channel Model for LossyWireless Sensor Networks

where d is the transmitter-receiver distance, γ is the signal to noise ratio (SNR), ρ is the encoding ratio and f is the frame length.

Packet Reception Rate (PRR)

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Metrics In order to evaluate the energy efficiency of different

strategies, the following metrics are used:• Delivery Rate (r): percentage of packets sent by the source which

reached the sink.• Total Number of Transmissions (t): total number of packets sent by

the network, to attain the delivery rate described above.

• Energy Efficiency (Eeff ): number of packets delivered to the sink for each unit of energy spent by the network.

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Assumptions in This Analysis The analysis is based on the following assumptions:

• Nodes know the location and the link’s PRR of their neighbors.

• Nodes are randomly distributed (i.e. the number of neighbors of certain node is constant).

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Total amount of energy consumed by the network for each transmitted packet is:

Energy Efficiency Model

rerxtxtotal eneee )1( etx and erx: amount of energy required by a node to transmit

and receive a packet ere: the energy used to read only the header of the packet (for

early rejection). n: number of neighbors (considered as a constant)

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Energy Efficiency Model (Cont.)

ktrpE src

eff

psrc is the number of packets sent by the source k is a constant which includes etotal and a conversion

factor for energy units.

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Analysis for ARQ Case

ktpE src

effARQ

Assume no a-priori constraint on the maximum number of retransmissions:• Infinite retransmissions can be performed, therefore, r is

equal to 1

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Analysis for ARQ Case (Cont.)

Thus, in order to maximize EeffARQ, we need to maximize the PRR×Distance product. Hence, we define the metric Xd as PRR(d)×d.

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PMF of Optimal Forwarding Distance in ARQ

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Different Forwarding Strategies PRR×DIST: the packet is forwarded through neighbors

that have the highest PRR×distance metric. BR: the packet is forwarded through the neighbor that has

the highest PRR. If several nodes have the same PRR, the node closest to the sink is chosen.

CONN: the packet is forwarded through the node in the connected region that is closest to the sink.

GR (1%): greedy forwarding through nodes that have PRR above 1%.

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Relative Energy Efficiency for Different Forwarding Strategies

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Analysis for No-ARQ Case

Because in systems without ARQ, the distance between the source and the sink influences the election of the optimal forwarding distance.

h is hop count

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PMF of Optimal Forwarding Distance in No-ARQ

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Relative Energy Efficiency for Different Forwarding Strategies No-ARQ

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Useful Insights First, the optimal forwarding decision based on the

PRR×distance metric in the ARQ case and in the No-ARQ case, strikes a balance between the two extremes of forwarding to the farthest (likely worst reception) neighbor and the nearest (likely best reception) neighbor.

Second, the best candidate neighbor for forwarding often lies in the transitional region.

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Simulation and Comparison First, show the results for the blacklisting strategies: distance-

based, absolute reception based, and relative reception-based at an entire range of blacklisting thresholds and different densities. • The goal is to identify the optimum thresholds and their

characteristics.

Then, compare the different strategies by picking the optimum blacklisting threshold for each density and

include also the original greedy, best reception policy, and the best PRR×distance policy in the comparison.

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Performance of Greedy Forwarding With and Without ARQ at Different Network Sizes

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Performance of PRR×Distance With and Without ARQ at Different Network Sizes

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Real Experiments with Motes

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Conclusion A mathematical analysis of the optimal forwarding

distance for both ARQ and No-ARQ scenarios is presented

The common greedy forwarding approach would result in very poor packet delivery rate.

An important forwarding metric that arose from our analysis, simulations and experiments, is PRR×Distance, particularly when ARQ is employed.