an empirical study of epidemic algorithms in large scale multihop wireless networks authored by...
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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
Introduction: Contributions
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
Introduction: Contributions
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?
Introduction: Contributions
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
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.
Experiment Set 2: Observations
Flood initiated Failed nodes
Step 1.
Experiment Set 2: Observations
Flood initiated Failed nodes Long links
Cell region is far from a simple disc Physical/Link level effect
Step 1.
Experiment Set 2: Observations
Rebroadcasts Backward links
The flood extends towards the source
Step 2.
Experiment Set 2: Observations
Rebroadcasts Backward links
The flood extends towards the source
Stragglers MAC-level collisions
Step 3.
Experiment Set 2: Observations
Final state Backward links
The flood extends towards the source
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%
Analysis: Physical and Link Layer
Low transmit power
Packet reception map 90% 80% 70% 60% 50%
Analysis: Physical and Link Layer
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)
Analysis: Physical and Link Layer
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
Analysis: Physical and Link Layer
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
Analysis: Physical and Link Layer
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.
Analysis: Medium Access Layer
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
Analysis: Medium Access Layer
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!
Analysis: Network and Application Layer How all this affects existing localization
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.
Analysis: Network and Application Layer How all this affects existing localization
schemes? Range-Free… Approximate PIT Test:
How do long links affect localization accuracy?
Analysis: Network and Application Layer How all this affects existing localization
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!