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