Mehmet C. Vuran Vehbi C. Gungor Özgür B. Akan School of Electrical & Computer Engineering Georgia Institute of Technology Atlanta, GA 30332 {mcvuran, gungor}@ece.gatech.edu.
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Electrical & Electronics Engineering Department
Middle East Technical University
06531, Ankara, Turkey
akan@eee.metu.edu.tr
School of Electrical & Computer Engineering
Georgia Institute of Technology
Atlanta, GA 30332
{mcvuran, gungor}@ece.gatech.edu
Özgür BÖzgür B.. Akan AkanMehmet C. VuranMehmet C. Vuran
Vehbi C. GungorVehbi C. Gungor
On the Interdependence of On the Interdependence of Congestion and Contention in Congestion and Contention in
Wireless Sensor NetworksWireless Sensor Networks
Outline
Wireless Sensor Networks (WSN) Congestion and contention in WSN Related Work Goals Evaluation Environment Results Conclusions
InternetInternet, , SatelliteSatellite, UAV, UAV
Sink
Sink
TaskManager
Wireless Sensor NetworksWireless Sensor Networks
Several thousand nodes
Distance of tens of feet
Densities as high as 20 nodes/m2
•I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci, I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci, ““Wireless Sensor Networks: A Survey”, Wireless Sensor Networks: A Survey”, Computer Networks JournalComputer Networks Journal, March 2002., March 2002.•I.F.Akyildiz, M.C. Vuran, O. B. Akan, W. Su,I.F.Akyildiz, M.C. Vuran, O. B. Akan, W. Su,““Wireless Sensor Networks: A Survey REVISITED” Wireless Sensor Networks: A Survey REVISITED” Computer Networks JournalComputer Networks Journal, 2005., 2005.
Wireless Sensor Networks (WSN)
Characterized by the collaborative information transmission of densely deployed nodes
High density leads to Local contention Network-wide congestion
In fact, the level of local contention and the network congestion are closely coupled due to the multi-hop nature of sensor networks
Network Congestion
Network congestion leads to waste of communication resources leads to waste of energy resources hampers event detection reliability at the sink
The WSN architecture employs unique sources for congestion Communication in a shared wireless medium Multi-hop nature of WSN Limited buffer size
Main Sources for Congestion
Channel Contention and Interference Contention occurs between
different flows different packets of a flow
Outgoing channel capacity becomes time variant High density exacerbates the impact of contention
Number of Event Sources Higher number of event sources improve event
detection efficiency Closely located source nodes increase contention Increased number of flows increase congestion
Main Sources for Congestion (2)
Packet Collisions Packet drops due to collisions may indicate lower
congestion level Reporting Rate
Increasing reporting rate causes network congestion even if local contention is minimized
Many-to-one Nature Event communication between multiple sources
and single sink causes bottleneck around the sink
A comprehensive analysis of A comprehensive analysis of network congestionnetwork congestion and and local contentionlocal contention is required for WSN is required for WSN
Related Work In [1], channel load information is incorporated into
congestion detection and control mechanisms. [2] proposes transmission control scheme for use at the
MAC layer. In [3], congestion detection is performed through buffer
occupancy measurements. In [4], the backoff window of each node is linked to its
local congestion state. It has been advocated in [5] that MAC layer support is
beneficial in congestion detection and control algorithms.[1] C. Y. Wan, et.al., “CODA: Congestion Detection and Avoidance in Sensor Networks,” in Proc. ACM SENSYS 2003, November 2003.[2] A. Woo, et.al., “A Transmission Control Scheme for Media Access in Sensor Networks,” in Proc. ACM MOBICOM 2001, pp.221-235, 2001.[3] O. B. Akan and I. F. Akyildiz, “ESRT: Event-to-Sink Reliable Transport for Wireless Sensor Networks,” to appear in IEEE/ACM Trans. Networking, October 2005. [4] I. Aad, et.al., “Differentiation Mechanisms for IEEE 802.11,” in Proc. IEEE INFOCOM 2001, pp. 209-218, April 2001.[5] B. Hull, et.al., “Techniques for Mitigating Congestion in Sensor Networks,” in Proc. ACM SENSYS 2004, November 2004.
Related Work (2)
Cross-layer approaches in congestion detection and control is necessary in WSN
There is a close coupling between local contention and network-wide congestion
The interdependence of congestion and contention are yet to be studied
Goals
In this work, we investigate the interactions between contention resolution and congestion control mechanisms
What are the consequences of independent operations of local contention resolution and end-to-end congestion control mechanisms?
What is the effect of local retransmissions? What are the effects of network parameters such as
buffer sizes of the sensors, number of sources and contention window size?
Can cross layer interaction be performed by preserving the modularity of layered design or are cross-layer designs required?
Evaluation Environment and Performance Metrics ns-2 simulations in a 100x100m2 sensor field One node selected as sink Nodes in an event area send information to the
sink Performance Metrics
Event Reliability (Rev)Number of CollisionsMAC Layer Errors
Buffer OverflowsEnd-to-end LatencyEnergy Efficiency
Number of Sources
Event radius values 20m, 30m, 40m
As reporting rate is increased reliability drops significantly
Increasing number of sources, i.e., event radius, degrades reliability
A common shape is observed for reliability
rrththlowlow
rrththhighhigh
non-congestednon-congestedregionregion
transitiontransitionregionregion
congestedcongestedregionregion
Number of Sources (2)
Close correlation between MAC layer errors and buffer overflows Buffer overflows start to build up as MAC layer errors saturate The maximum value of MAC layer error percentage occurs at rth
low
For higher number of sources, congestion occurs at lower reporting rate
Buffer Size
Buffer size values 5, 50, 100, 250
Change in buffer size has minimal effect on reliability
Buffer Size (2)
Increasing buffer length increases percentage of MAC layer errors Small buffer sizes lead to lower latency If end-to-end latency is important, lower buffer sizes lead to
acceptable reliability Since contention dominates, smaller buffer sizes are actually
beneficial in WSN
MAC Layer Retransmissions
Retransmission limit values 4, 7, 10
Decreasing local reliability affects overall reliability
rthlow occurs at lower
values for decreased Rtxmax
Increasing Rtxmax further have minimal effect on reliability
MAC Layer Retransmissions (2)
Local reliability level affects MAC layer errors In the congested region, end-to-end latency increases significantly Local reliability mechanism has converse effect on end-to-end
latency Latency saturates in congested region and local reliability level
affects the saturation reporting rate value
Contention Window
Average contention window values for source and router nodes
Source nodes are located close
Increasing reporting rate increases contention
Contention occurs mainly in the vicinity of source nodes
Adjusting initial contention window size, CWmin, may affect network performance
Contention Window (2)
Adjusting buffer size and CWmin leads to higher reliability
In non-congested region, lower CWmin size is better
As the reporting rate is increased, increasing CWmin improves reliability by 10%
Adaptive contention window size adjustments lead to efficient results
Reasons for Packet Drops
Distribution of packet drops
In non-congested region, packet drops are due to MAC and routing layers
As reporting rate is increased, MAC layer errors saturate and buffer overflows dominate
Adaptive reliability mechanisms are required considering traffic load
Energy Efficiency
Energy consumption increases with reporting rate in non-congested and transition regions
Energy consumption saturates in the congested region
Number of sources significantly effect energy consumption
Energy Efficiency (2)
Energy consumption is not significantly affected by buffer size or Rtxmax
The effects of these parameters on other performance metrics enable energy-aware, adaptive protocols to be implemented
Conclusions
The interdependence between local contention and network-wide congestion is investigated
Higher event resolution vs. higher contention Increasing number of sources improves event reliability Higher contention degrades network performance since
sources are closely located Small buffer sizes may be beneficial
For low reliability, low latency demanding applications, smaller buffer size leads to more efficient performance
Local reliability vs. End-to-end reliability Higher reporting rate can be supported by local reliability In addition to local reliability, end-to-end congestion and
reliability mechanisms required
Conclusions (2)
Traffic-aware contention window size The knowledge of reporting rate enables initial contention
window size adjustments The effect of buffer size change can be given by contention
window size adjustments Adaptive cross-layer reliability mechanism required
Packet drop distribution changes dynamically Reliability mechanisms need to adopt to sources of drops
Energy efficient adjustments are possible Energy consumption is minimally affected by buffer size and
retransmission limit adjustments Local interactions directly affect overall network
performance
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