An Empirical Study of Epidemic Algorithms in Large Scale Multihop Wireless Networks Authored by...

An Empirical Study of Epidemic Algorithms in Large Scale Multihop Wireless Networks

Authored by Deepak Ganesan, Bhaskar Krishnamachari, Alec Woo, David Culler, Deborah Estrin, Stephen Wicker

Presented by Tibor HorvathCS851 Fall 2003, University of Virginia

11/17/2003

Outline

Introduction Related Work Experiments Analysis of Results Conclusion, Opinions

Introduction

A large amount of communication algorithms already exist for Wireless Sensor Networks (WSN-s)

Most existing algorithms were validated by idealized simulations or small-scale real experiments

This paper describes real large-scale WSN experiments

Results show that many previous design assumptions were unrealistic

A foundation work to support future algorithm design

Introduction: Contributions

Characterizes radio communication properties to be expected in real WSN-s Asymmetric links can be significant at large scale Irregular propagation causes uneven distribution

of loss rate over distance Obstacles and collisions can cause unexpected

black holes with flooding Our simulators should be capable of modeling

these properties


Provides insight into some tradeoffs between different transmission power settings High power can cause very high contention But high power can also save energy by reducing

multi-hop communication (network diameter) Low power can increase the number of

asymmetric links present But low power also has more regular propagation

and much less collisions It would be nice to have measurements of energy

usage of nodes with each power setting


Shows experimental evidence of the “broadcast storm” generated by flooding Even at low contention levels, the percentage of useless

broadcasts is over 60% Higher radio power makes the overhead even worse:

Each node reaches much more other nodes many collisions → long backoffs → late broadcasts

Even with this overhead, there will always be nodes that do not receive the flood (stragglers)

Do algorithms that rely on flooding really accept or tolerate these properties? How does it affect their scalability and robustness?


Demonstrates the non-optimality of routing protocols that use shortest reverse hop-count paths Asymmetric links alone defeat some of the protocols (e.g.

AODV) Long links cause highly clustered trees with low robustness

and uneven energy depletion Collisions can cause creation of trees with backward links:

routing path flows away from the destination We should be very careful when and how to use hop-

counts resulting from flooding in our algorithms

Related Work

Prior experimental studies with lack of infrastructure DSR: 8 laptops with 802.11, moved in a 300×700m area AODV: 1 desktop and 5 laptops with 802.11 Data aggregation in Directed Diffusion:

14 PC/104 (Embedded PC) sensor nodes Radiometrix RPC modems: Reliable 30m in-building range,

120m open ground

13.5mm 16mm

54mm

32mm

Related Work

Prior small-scale experimental studies MAC adaptive rate control: 11 Berkeley motes S-MAC (energy-efficient MAC): 5 Berkeley motes

Simulation analyses Realistic modeling is very challenging Cannot be considered as final validation

Related Work

Broadcast dissemination algorithms Sophisticated epidemic protocols

Probabilistic rebroadcasting (Gossip) Counter-, distance-, location-, cluster-based

rebroadcasting Other mechanisms

SPIN (energy-efficient) Minimum connected dominating sets (virtual backbone)

Experiment Scenarios: Algorithm

Generic epidemic algorithm Retransmission decision is a randomized function of local

state Algorithm used for analysis: Flooding

Retransmission decision is always true.

Experiment Scenarios: Algorithm Why evaluate simple flooding?

Many dissemination schemes still rely on flooding. (e.g. Maté)

Although more sophisticated alternatives exist, flooding adequately demonstrates the same physical and link layer issues

Experiment Scenarios: Platform

Rene Mote 916 MHz single channel 10 kbps raw bandwidth Dynamically tunable

transmission power:

Experiment Scenarios: Platform Calibration

Fresh batteries Same antenna length Vertical orientation

TinyOS CSMA with random backoff (6ms-100ms) No packet dropping

Experiment Set 1: Link Characteristics 169 nodes on a 13×13 grid, 2-feet spacing Nodes transmit sequentially to the base

station at 16 different power levels Collisions were eliminated About 54,000 messages total

Receivers log message data for later reconstruction

The results are packet loss statistics

Experiment Set 2: Flood Propagation 156 nodes on a 13×12 grid, 2-feet spacing Open parking lot, no obstacles 8 different power levels Base station in the middle of the grid’s base Nodes log data in all layers for reconstruction of

propagation ID of sender → Propagation tree MAC Layer timestamps → Backoff time, Collisions Link Layer timestamps → Minimize receiver delay

Reconstruction error under a bit-time per hop

Experiment Set 2: Observations

Flood initiatedStep 1.


Flood initiated Failed nodes

Step 1.


Flood initiated Failed nodes Long links

Cell region is far from a simple disc Physical/Link level effect

Step 1.


Rebroadcasts Backward links

The flood extends towards the source

Step 2.


Rebroadcasts Backward links


Stragglers MAC-level collisions

Step 3.


Final state Backward links


Stragglers MAC-level collisions

High clustering Most nodes have few

descendants A significant few have

many children

Step 4.

Analysis of Results

Physical and Link Layer Effective communication radius Packet loss statistics Bidirectional and asymmetric links

Medium Access Layer Contention, collisions Hidden terminal effect

Network and Application Layer Propagation structure

Analysis: Physical and Link Layer

High transmit power

Packet reception map 90% 80% 70% 60% 50%


Low transmit power

Packet reception map 90% 80% 70% 60% 50%


Distribution of packet loss over distance is non-uniform

Throughput is lower than 100% even at short distances Insufficient signal

processing and error correction

Reception decrease not as sharp as signal strength decay (exponential)


Distribution of packet loss over distance is non-uniform

Throughput is lower than 100% even at short distances Insufficient signal

processing and error correction

Reception decrease not as sharp as signal strength decay (exponential)

Good

Bad

Neither


Connectivity Radius Radius R of the smallest

circle that covers 75% of the “good links”:

High Med Low V.Low Good link

Neither good nor bad link

Analysis: Physical and Link Layer Asymmetric Links: 5-15%

“good” link in one direction, “bad” link in the other Bidirectional Links

“good” link in both directions


High transmit power Very low transmit power Percentage of asymmetric links grows with distance The growth is greater at lower transmit power Small differences in reception sensitivity, hardware, and energy level

dominate at the fading edge

Analysis: Physical and Link Layer How would SPEED perform in large-scale?

Original experiments use 25 motes on a 5×5 grid Is it sensitive to long links? Can it form backward links? Does it accept asymmetric links?

Analysis: Medium Access Layer Contention

Communication range increases with transmit power Interference range is often greater than the communication

range Thus, contention increases with transmit power

Backoff delay Higher transmit power leads to longer backoff durations However, it is not fully deterministic due to the random

backoff implemented in the TinyOS MAC protocol.

Analysis: Medium Access Layer

Analysis: Medium Access Layer Maximum backoff interval

Captures contention level within interference cells Reflects the largest contention time Approximate, because the starting time of backoffs is not

the same among the nodes.


Reception Latency Definition: The amount of

time it takes for each node to receive the flooded packet.

Significant fraction of time taken to reach last few (5%) nodes → Stragglers

Reception latency increases with network diameter (maximum hop count) → Higher transmit power yields lower latency

Analysis: Medium Access Layer Settling time

Definition: Combination of the reception latency and the time taken for all retransmissions to complete throughout the network

ReceptionLatency

MaxBackoffTime

Minimum Settling Time

ReceptionLatency

MaxBackoffTime

Maximum Settling Time

Analysis: Medium Access Layer Low transmit power

Settling time is dominated by reception latency because of larger diameter

High transmit power Settling time is dominated

by maximum backoff time because of high overall contention

Nodes keep retransmitting the message long after 95% reached

Metric relations: Timings vs. Transmit Power


Observe the fraction of Reception Latency and Settling Time

Analysis: Medium Access Layer Useless Broadcasts

Definition: A rebroadcast that only delivers the message to nodes already reached

Note that simple flooding has an implicitly high percentage of useless broadcasts (60%+)

Analysis: Medium Access Layer Collision

Appearance of stragglers and backward links can be explained with collisions Stragglers likely form backward links if ever reached

later Hidden terminal problem

A node is unable to receive most messages due to an obstacle

This likely defeats its collision avoidance algorithm Its transmissions likely cause many collisions

Analysis: Medium Access Layer Higher transmit power results in more

hidden terminals and thus more collisions

Analysis: Medium Access Layer How much does the high backoff impact the

real-time performance of SPEED? High miss ratio

What power setting should it choose?

Analysis: Network and Application Layer Dissemination Tree Characteristics

Reverse path may fail due to asymmetric links Long links exacerbate this effect as they are more likely

asymmetric Long links are likely preferred by applications (routing)

Backward links cause suboptimal behavior E.g. sensor data flows away from base station

Earliest-first parent selection results in clustered tree Large clusters occur frequently irrespective of transmit power Clustered trees suffer large connectivity loss from orphaning

Analysis: Network and Application Layer Dissemination Tree Characteristics

Tree level only loosely corresponds to distance:

Stragglers

Long links

Analysis: Network and Application Layer How all this affects existing localization

schemes? GPS-less... localization paper argues that the

idealized radio model “compares quite well to outdoor radio propagation…” Because they use the Radiometrix RPC-s: 120m reliable

open ground range in a 10m×10m test area!


schemes? Range-Free… localization paper assumes an

irregular radio pattern But it is still not fully realistic: assumes 100% reception

rate within a lower bound distance, 0% beyond an upper bound.


schemes? Range-Free… Approximate PIT Test:

How do long links affect localization accuracy?


schemes? Hop-count distance based localization schemes

The relation of hop-count to distance is very far from being linear:

Conclusion

Even simple distributed WSN communication algorithms show very complex behavior Probabilistic connectivity Unexpected links (long, backward) Stragglers Asymmetric links are frequent

It is imperative to validate all communication algorithms by performing: Non-idealized simulation based on real data Real large-scale experiments

Opinion: About the results

Which properties may be improved? Physical Layer properties?

Calibration already near ideal Newer radios (e.g. Mica 40 kbps) have better error correction

MAC Layer properties? Rudimentary MAC implementation of TinyOS Directional radios may reduce contention

Application Layer properties? Simple Flooding: Broadcast storm problem: Flooding may

result in excessive redundancy, contention, and collision. (S.-Y. Ni, Y.-C. Tseng, Y.-S. Chen and J.-P. Sheu, The broadcast storm problem in a mobile ad hoc network)

Hierarchical, energy-efficient dissemination protocols

Opinion: About the paper

Although results are subject to significant improvements in all layers, the paper: Makes the case for real large-scale validation Shows that only probabilistic communication

models will lead to realistic analyses It would be good to explore the transmit

power setting tradeoffs further Node energy consumption measurements would

have been especially valuable

End

Thank You!

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