On the Interdependence of
Congestion and Contention in
Wireless Sensor Networks
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
Electrical & Electronics Engineering Department
Middle East Technical University
06531, Ankara, Turkey
akan@eee.metu.edu.tr
Outline
 Wireless Sensor Networks (WSN)
 Congestion and contention in WSN
 Related Work
 Goals
 Evaluation Environment
 Results
 Conclusions
Wireless Sensor Networks
Internet,
Satellite,
UAV
Sink
Sink
 Several thousand
nodes
 Distance of tens of
feet
 Densities as high as
20 nodes/m2
Task
Manager
I.F.Akyildiz, W.Su, Y. Sankarasubramaniam, E. Cayirci,
“Wireless Sensor Networks: A Survey”, Computer Networks Journal, March 2002.
I.F.Akyildiz, M.C. Vuran, O. B. Akan, W. Su,
“Wireless Sensor Networks: A Survey REVISITED” Computer Networks Journal, 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 network congestion
and local contention 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 Collisions
MAC Layer Errors
Buffer
Overflows
End-to-end Latency
Energy Efficiency
Number of Sources
 Event radius values
non-congested
region
rthlow
transition
region
rthhigh
congested
region
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
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 rthlow
 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