Factors Affecting the Performance of Ad Hoc Networks
Dmitri D. Perkins, Herman D. Hughes, and Charles B. Owen
Department of Computer Science and Engineering
Michigan State University
East Lansing, MI 48824-1226
Abstract- Mobile Ad Hoc NETworks (MANETs) are an
emerging class of network architectures that are characterized
by their highly dynamic topology, limited resources (i. e.,
bandwidth and power), and lack of fixed infrastructure. The
primary motivation for such networks is increased flexibility
and mobility. Random node mobility along with various other
factors such as network size and traffic intensity may be very
dynamic, resulting in unpredictable variations in the overall
network performance. This study centers on investigating and
quantifying the effects of various factors and their two-way
interactions on the overall performance of ad hoc networks.
This study will contribute to the modeling and development of
adaptive ad hoc protocols (routing, medium access control,
scheduling and buffer management). Using 2kr factorial
experimental design, we isolate and quantify the effects of five
factors: node speed, pause-time, network size, number of traffic
sources, and type of routing (source versus distributed), that affect
the performance of ad hoc networks. Specifically, this paper
evaluates the impact of these factors on the following
performance metrics: throughput, average routing overhead, and
power consumption.
I. INTRODUCTION AND MOTIVATION
A Mobile Ad Hoc NETworks (MANET) [1] is an selforganizing system of mobile routers (and associated hosts)
connected by wireless links. Ad hoc networks may operate
autonomously, or may be connected to the larger Internet.
The goal of mobile ad hoc networking is to provide a rapidly
deployable means of communication (and computing),
independent of a pre-existing infrastructure (e.g., base
stations). Such networks will utilize a wireless physical layer
consisting of relatively low bandwidth, time-varying links. In
current wireless networks, the wireless mobile node is never
more than one hop from a base station that can route data
across the communication infrastructure. In mobile ad hoc
networks, there are no base stations and because of a limited
transmission range, multiple hops may be required for nodes
to communicate across the ad hoc network. Routing
functionality is incorporated into each host. Thus, MANETs
can be characterized as having a dynamic, multi-hop and,
constantly changing topology. While mobile ad hoc networks
can be used without a fixed infrastructure, their use is also
being considered as part of the vision for a truly ubiquitous
communications environment (e.g., Wireless Internet). The
future success of ad hoc networking will depend on its ability
to support existing and future Internet applications and
protocols. Such a dynamic environment poses tremendous
protocol design challenges at every layer of the network
architecture, ranging from physical layer issues to distributed
medium access control to routing.
Several factors will affect the overall performance of any
protocol operating in an ad hoc network. For example, node
mobility may cause link failures, which will negatively
impact routing and quality-of-service support. Network size,
control overhead, and traffic intensity will have a
considerable impact on network scalability. These factors
along with inherent characteristics of ad hoc networks may
result in unpredictable variations in the overall network
performance.
The primary objective of this study is to evaluate and
quantify the effects of various factors (and their two-way
interactions) that may influence network performance. While
there have been performance evaluations of ad hoc networks
[4, 5], none have actually quantified the effects of the
influential factors. Using a 254 factorial experimental design
[6, 7], we determine the impact of five factors: (1) node
speed, (2) node pause time, (3) network size, (4) number of
traffic sources and (5) routing protocol (source vs.
distributed) on the performance of ad hoc networks. We
examine the impact of these five factors on three performance
metrics: (1) average throughput, (2) average routing
overhead, and (3) power consumption. Quantifying the
effects of these factors will help guide the design choices and
tradeoffs. For example, suppose node mobility is shown to
have a greater impact on average control overhead than any
other factor. This would suggest that designing algorithms
that adapt to node mobility would have the greatest impact on
network performance.
The factorial design model used in this work is based on a
linear regression model and thus, makes the assumption that
the effects of the factors are additive. Further, our model
assumes that experimental errors are independent and
normally distributed.
The remainder of this paper is organized as follows.
Section II describes the simulation environment and
methodology. A discussion of the performance metrics and
experimental factors is presented in Section III. In Section
IV, the simulation results and design analysis are presented,
followed by a summary in Section VI.
II. METHODOLOGY, SIMULATION AND E XPERIMENTAL
DESIGN
Again, the goal of our experiments is to examine and quantify
the affects of various factors and their interactions on the
overall performance of ad hoc networks. To achieve this goal,
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we used a 2kr (k=5, r=4) factorial design methodology [6],
thus conducting 32 separate experiments referred to as design
points. Each experiment was replicated 4 times, resulting in
128 simulation runs. This section provides a brief discussion
of the simulation environment and the factorial experimental
design.
Our simulation study was conducted using the librarybased Global Mobile System Simulator (GloMoSim) [8] for
sequential and parallel simulation of wireless networks. It
was designed using the parallel discrete-event simulation
capability provided by Parsec [9], a C-based parallel
simulation language. GloMoSim was developed based on a
layered approach similar to the OSI seven layer network
architecture. Our model is simulated for 200 seconds of
simulated time.
The radio transmission range of each node is
approximately 250 meters and the channel capacity is 2
Mbits/sec. The free space propagation model [8] was used to
determine if a node is reachable. The free space model
predicts received signal strength when the transmitter and
receiver have a clear, unobstructed line-of-sight path between
them. Received power decays as a function of the T-R
separation distance. The IEEE 802.11 Medium Access
Control Protocol was used as the MAC protocol.
In this study, we used constant bit rate CBR sources that
continuously transmit 1024-byte data packets at a rate of 4
packets per second for the duration of the simulation.
As it is our goal to investigate the impact of routing in ad
hoc networks, we utilize two different routing protocols:
Dynamic Source Routing (DSR) [2] and Ad-hoc On demand
Distance Vector (AODV) [3]. Both routing protocols are ondemand protocols and, thus, do not transmit periodic routing
messages. These protocols differ with regard to the method
by which routes are computed. DSR uses source routing in
order to deliver packets to any destination in a mobile ad hoc
network. Source routing requires that the headers of all data
packets carry an ordered list of nodes through which the
packet must traverse. AODV uses a distributed (e.g., hop-byhop) technique to deliver packets and uses sequence numbers
(e.g., to avoid routing loops) for each route entry.
The random waypoint mobility model [8] is used in our
evaluations. In the random waypoint model, each node is
placed randomly in the simulated area (1600X400m2). After
remaining at the location for a specified pause time, the node
randomly selects another destination from the physical
terrain. The node then moves to the new location at a speed
uniformly chosen between a minimum and maximum speed
(meters/sec). After reaching the destination, the node stays
there for a MOBILITY-WP-PAUSE time period.
III.
PERFORMANCE METRICS AND E XPERIMENTAL
FACTORS
To maintain consistency with factorial design terminology,
we will refer to the variables that affect the outcome of an
experiment as factors and the actual outcomes as
performance responses or metrics. The following
performance metrics are examined in this study [10]:
1.
2.
3.
Throughput: throughput measures the effectiveness of
the network in delivering data packets. That is, how well
does the network deliver packets from the source to the
destination?
Average routing overhead: the average number of
control packets produced per node. Control packets
include route requests, replies and error messages.
Average power consumption: measures the average
power consumptions per node, as energy is a limited
resource in ad hoc networks.
Table 1 shows the levels for each of the factors examined in
this study. Each factor is examined at two different factor
levels. We also examine the effects of the two-way
interactions. That is, we want to determine whether the effect
of one factor is dependent on the level of another. It should
be noted that there are certainly many other factors (i.e.,
transmission range, MAC protocol, link bandwidth, size of
roaming area, etc.) that may have an effect on network
performance. The following factors were chosen as a starting
point for this investigation.
The main effect of a factor is the average change in the
throughput due to changing the factor from its “-” level to its
“+” level, while holding all other factors fixed. This average
is taken over all possible combinations of the other (k-1)
factors. The two-way interaction effect is the difference
between the average throughput when two factors are at the
same level and the average throughput when they are at
opposite levels.
Label
1
2
3
4
5
Table 1
Factors Examined
Factor
Level 1(-)
Speed (m/s)
5
Pause-time (sec)
3
Network size
50
# of sources
10
Routing
Source
Level 2(+)
40
30
80
25
Distributed
IV. SIMULATION RESULTS AND DESIGN ANALYSIS
This section presents the main effects and two-way
interaction for each factor. For brevity and convenience, each
factor is denoted by its label (see Table 1) and each two-way
interaction, say 1 and 3, by (1x3). Before examining the main
effects and their two factor interactions, we first observed the
average performance results of the three performance metrics.
Figures 1-4 show the performance metrics averaged over four
replications at each design. By closely examining the
following graphs and the experimental design matrix, we can
make three key observations:
•
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For control overhead, there are fluctuations at each
design point (one "high" followed by one "lower" value).
Examining the experimental design matrix, we observe
that this pattern follows the level changes of factor 1,
node speed. The fluctuations become more pronounced
for higher design points (i.e., 16-32) suggesting a strong
interaction of node speed, routing and, potentially,
network size.
A. The Effects on Control Overhead
As shown in Figure 4, the effects of factors 1, 4, and 5 result
in a significant increase of control data. Interestingly, this
result suggests that using distributed routing gives rise to an
increase in control overhead. Finally, we see that the amount
of control overhead is negatively influenced by several twoway interactions: (1x4), (1x5), and (4x5). Thus, the previous
observations regarding control overhead are valid.
Average Packet
Overhead
800.00
600.00
400.00
200.00
0.00
0
10
20
Design Points
30
40
30
40
Figure 1. Average control overhead for each experiment.
For power consumption, the design points are
consistently grouped in blocks of four. There are minor
fluctuations (one "high" followed by one "low" values)
within each block, again suggesting that node speed has
significant influence on power consumption. The
consistent groupings of four suggest some interaction
among factors.
Figures 1-3 and the above observations are certainly
beneficial to understanding the main effects of each factor as
well as their two-way interactions, but such observations are
only qualitative. To confirm the results, we must now
quantify the effects. Next, we formally substantiate the
observations discussed above. Figures 4-6 show the 90%
confidence intervals for the expected main effects and twoway interaction on control overhead, throughput and power
consumption, respectively. For control overhead and
throughput, the graphs show that the main effects of factors 1,
3, 4, and 5 are real (significantly different from zero) and thus
impact network performance. The results also show that the
main effect of factor two (pause-time) does not significantly
influence either performance metric. Moreover, the two-way
interactions and the main effects of factors 1 and 2 have no
“real” impact on power consumption. Additional
observations and discussion are presented for each metric in
the following sections.
1000.00
Average Throughput
35000.00
Average Throughput
(bps)
•
For throughput, the second and fourth blocks of eight
design points experience significant fluctuations (one
"high" followed by one "low" values), indicating a factor
interaction. Looking at the design matrix, we observe
that these fluctuations correspond to level changes of
multiple factors, namely factors one (node speed), and
five (routing protocol), and, possibly, factor three
(network size).
1200.00
30000.00
25000.00
20000.00
15000.00
10000.00
5000.00
0.00
0
10
20
Design Point
Figure 2. Average throughput for each experiment.
Power Consumption
60.60
Average
Power Consumption
•
Control Overhead
1400.00
60.50
60.40
60.30
60.20
60.10
60.00
0
10
20
Design Point
Figure 3. Average power consumption for each experiment.
2050
30
40
to be a "better" design choice if efficiency is the primary
concern.
Effects on Control Overhead
200
Effects on Cntrl data
(pkts/router)
150
100
50
0
-50
4x5
3x5
3x4
2x5
2x4
2x3
1x5
1x4
1x3
5
1x2
4
3
2
1
-100
Effect Labels
Figure 4. 90% confidence intervals for the expected main effects and two-way interaction effects on
Control Overhead (packets/router).
Effects on Throughput
Effect on
Throughput (kps)
1000
500
0
-500
-1000
-1500
4x5
3x5
3x4
2x5
2x4
2x3
1x5
1x4
1x3
1x2
5
4
3
2
1
-2000
Effect labels
Figure 5. 90% confidence intervals for the expected main effects and two-way interaction effects on
average throughput in kbps.
Effects of Power Consumption
0.15
Effect on Power
(mWhr)
C. The Effects on Power Consumption
Power consumption is strongly influenced by two factors.
The effect of factor 3, network size, is positive, decreasing
the average power consumption as the network size is
increased. The effect of factor 4, number of sources, is
negative, increasing the average power consumption as the
number of sources increases Intuitively, this is reasonable
since increasing the network size while maintaining a
constant traffic load essentially increases the number of
routers eligible to forward packets, resulting in a reduced
load. On the other hand, increasing the number of traffic
sources simply increases the routing load of each mobile host,
resulting in increased power consumption.
D. Quantifying the Effects: Allocation of Variation
The importance of a factor can be determined by the
proportion of variation in the performance metric that is
explained by the factor. The proportions of variation
explained by each factor and the two-way interactions are
shown in Table 2. The last row, labeled EE, is the proportion
of variation attributed to experimental error.
Notice that main effect of factor 4, the number of sources,
is responsible for 69 percent, 20 percent, and 34 percent of
the variation in power consumption, control over, and
throughput, respectively. Based on these results and analysis,
it appears that factor 4 is the most influential factor, followed
by node speed and network size. The two-way interaction of
node speed and the number of traffic sources (1x4) is also
significant.
0.1
Table 2
Allocation of Variation for each Performance response
Percentage of Variation Explained by
Factor
Power
Control
Factor Consumption Overhead
Throughput
0.05
0
-0.05
4x5
3x5
3x4
2x5
2x4
2x3
1x5
1x4
1x3
1x2
5
4
3
2
1
-0.1
Effect Labels
Figure 6. 90% confidence intervals for main effects and two-way interaction effects on power
consumption in (mWhr).
B. The Effects on Average Throughput
The effects of factors 1 and 4 (and their two-way interactions)
are significantly negative for throughput. The effect of factor
3, network size, is positive, indicating that larger network size
results in improved throughput. Notice, that the type of
routing used has a negligible effect on throughput
performance. Combining this fact with the results (using
distributed routing results in an increase in control overhead)
from the Subsection IV.A suggest that distributed routing is
less efficient than source routing. Thus, source routing tends
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1
2
3
4
5
1x2
1x3
1x4
1x5
2x3
2x4
2x5
3x4
3x5
4x5
EE
0.071
0.071
20.990
68.885
2.332
0.018
0.071
0.282
0.110
0.018
0.004
0.004
6.035
0.110
0.357
0.642
13.373
0.489
2.558
19.857
19.966
0.496
0.284
8.801
10.221
0.115
0.196
0.446
2.596
1.934
14.535
4.133
30.176
0.022
3.994
34.306
0.315
0.021
0.569
16.816
0.172
2.043
0.048
0.147
3.500
0.373
0.258
7.238
V. SUMMARY
This paper has presented a comprehensive analysis of five
factors: node speed, pause-time, network size, number of
traffic sources, and type of routing (source versus
distributed), that affect the routing performance of ad hoc
networks. A factorial experimental design was used to isolate
and quantify the main effects as well and two-way
interactions of these factors on three performance responses:
throughput, average routing overhead, and power
consumption. The main results and observations of our
analysis are as follows:
• For the experimental design used in this study, source
routing was more efficient. That is, it achieved
approximately the same performance as distributed
routing but used less control overhead.
• As the result show, the main effect of factor 4, number of
traffic sources, has the strongest impact on the
performance responses followed closely by node speed
and network size.
• Increasing the network size, while maintaining the traffic
load results in increased throughput, decreased control
overhead and decreased power consumption.
• Increasing traffic load and increasing the number of
traffic sources may not result in the same performance
results. For example, adding more traffic sources will
certainly increase the control overhead, while increasing
the transmission rate at a single source does not
necessarily result in increased control overhead.
It is important to note that, while we were very sensitive to
the selection of factor levels, our results and conclusions
(e.g., estimates of effects and interactions) are based upon the
factor levels used in this design and may vary if different
factor levels are used. To reduce the potential variations when
different factor levels are used, we executed several
simulations and selected the factors most appropriate. That is,
we took special care not to select factors levels too far apart
to provide any meaningful or useful results. Thus, we believe
the results of this study will greatly contribute to the
modeling and design of adaptive protocols for ad hoc
networks.
[2] J. Broch, J, D. B. Johnson, and D. A. Maltz, “The
Dynamic Source Routing protocol for mobile ad hoc
networks”. Internet Draft, draft-ietf-manet-dsr-03.txt,
October 1999. Work in progress.
[3] C. E. Perkins, “Ad Hoc On Demand Distance Vector
Routing”. Internet Draft, draft-ietf-manet-aodv-02.txt,
November 1998. Work in progress.
[4] J. Broch, D. A. Maltz, D. B. Johnson, Y.C. Hu, and J.
Jetcheva. "A performance comparison of multi-hop
wireless ad hoc network routing protocols". Proceedings
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[5] D. D. Perkins and H. Hughes, “A performance
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networks. Proceedings of SPECTS 2000, Vancouver,
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[6] R. Jain, 1991. The Art of Computer System Performance
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[7] A. M. Law and W. David Kelton, 2000. Simulation,
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[8] L. Bajaj, M. Takai, R. Ahuja, K. Tang, R. Bagrodia, and
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[10] M. S. Corson. and J. Macker, “Mobile ad hoc
networking: routing protocol performance issues and
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1999, http://www.ietf.org/rfc/rfc2501.txt.
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