On the performance of modified flooding algorithms in an infrastructure-less timevarying radio short messaging network
Clayton Tabone
University of Malta
ctab004@um.edu.mt
Abstract
In this paper the possibility of building a
free radio peer-peer messaging system based on
an infrastructure-less time-varying network is
investigated. This task is accomplished by
testing modified flooding algorithms over a
simulated time-varying radio network. A
physical radio model is developed to simulate
an Aloha-based medium access protocol and the
time-varying network is modeled with a
community based mobility model. The
performance of the modified flooding
algorithms are studied on this time-varying
network model and the algorithms are
compared in terms of number of messages
delay, end-to-end delay, and scalability.
1. Introduction
Routing in multi-hop networks has been the
subject of much research and controversy
during the last decade. More than fifty
algorithms have been proposed, most of which
are twists and variants of a smaller set of
original algorithms. Despite this effort several
issues, for example maintaining a route and
ensuring a certain level of QoS, still lack a
satisfactory solution, and the research in ad hoc
networks has now taken a different route.
Another area that has lacked attention is the use
of suitable mobility models. However
infrastructure-less radio data networks can be
very useful in providing low-cost slow-speed
connectivity, given that a minimum number of
such devices are deployed over a given area.
Such an application is a real-time short
messaging system, that can be easily added to a
mobile device enabled with a suitable simple
single channel radio transceiver.
This service is an alternative to the SMS
service without requiring the infrastructure
offered by mobile network providers, thus
making the transmission of these messages
totally free of charge and with no central point
of failure. In September 2007 a Swedish
company started working on a similar project
whereby it is selling modified handsets which
Adrian Muscat
University of Malta
adrian.muscat@um.edu.mt
enable free mobile telephony via an
infrastructure-less network. Such a system has
not been well studied and in this paper the
performance of a messaging system over an
infrastructure less network is investigated with a
computer simulation.
2. The Physical Layer
The radio propagation model chosen for the
physical layer is the free-space path loss model.
The transmit power of each node is set to a
constant 100mW. The frequency of the wireless
link being modeled is set to 2.5GHz in this
paper. The system modeled makes use of a
single channel, thus only simplex mode
transmissions may occur since all nodes
transmit and receive on the same channel. The
minimum detectable signal of each receiver is
assumed to be equal to -70dBm, which defines
the range of these devices, 301.77m. The nodes
are assumed to have an infinite queue for
buffering messages. The nodes also have a list
which maintains the most recent history for
each node. The stored data includes the past
transmissions and receptions. Elements from
these lists are removed periodically during the
simulation, thus their size is also limited. The
Data Link Layer in the simulator performs
multiple access control (MAC) over the
transmission medium. The model assumes the
pure Aloha MAC protocol. The radio capture
effect is taken into consideration with a capture
ratio of 5dB. The network layer performs the
routing functions. The protocols of the network
layer have the function of routing any received
messages to some other node.
3. The routing protocol
The basic flooding algorithm states that any
receiving node will retransmit the received
message to all its neighbouring nodes if the
Time To Live (TTL) counter of the received
message is greater than zero, and the receiving
node is not the destination node. Each new
message being created must be assigned an
initial fixed TTL counter. Each time the
message is transmitted by some node, that node
is responsible for decrementing this counter.
When this counter reaches zero, the message is
discarded. In this paper a node will not transmit
multiples of the same packet. This is done by
keeping a history list. In order to reduce
collisions a received packet is relayed after a
random amount of time.
The geographical-based protocol uses
position data from the transmitting and
destination node to direct the transmission
towards the destination. The scope of this study
is to determine whether position information
improves the efficiency of the flooding
protocol.
The receiving nodes will have the following
data available; their current position, the
position of the destination node, the position of
the last node. The receiving node calculates two
distances:
A = The distance between the receiving node and
the destination node
B = The distance between the last node and the
destination node
If A is smaller than B the message will be
transmitted by the receiving node. This
condition creates a limitation on the direction
where the message is flooded. Received
messages will only be routed if the receiving
node is closer to the destination node than to the
last node.
gives quite a realistic mobility pattern for
people moving from one town to another (e.g. to
go to work). In each group location a
community is formed. The Community based
Mobility Model, developed by Musolesi and
Mascolo [2] was chosen for this application.
The model is based on the assumption that
normally mobile nodes are carried by people
and thus their mobility behaviour may be based
on the social networks which each person
belongs to.
Fig.1 shows a snapshot of the node
configuration in one of the simulations where
the Community Based Mobility model is being
used. The black lines depict the path that the
node has been moving through. This figure
shows clearly that the nodes move from one
community to another. This process forms
virtual “streets” which connect each community
with all the others.
Development of Computer Simulation
Due to the simple air interface of the
problem and also due to the fact that the
problem lies mainly in the routing and mobility
parts a self-developed discrete event-based
simulator was used to study the problem, rather
than an off the shelf simulator like OPNET or
ns-2. Full details of this network simulator is
given in [3].
4. The Mobility Model
The choice of mobility model can have a
significant effect on the performance of an ad
hoc network protocol. Thus it is important to
choose a proper mobility model. If the mobility
model does not model closely the mobility
behaviour of the nodes in the real system, it is
possible that the results obtained from the
simulation do not illustrate the true performance
of the system [1].
The simulation scenario in this project is that
of people moving around inside different
premises or towns (depending on simulated area
size) forming separate groups or communities.
The majority of these group members remain in
the same group for a long period of time (e.g.
8hrs). Then, these people move to another group
in another geographical location either by walk
or by using some form of transport. They spend
another long period of time in that group and
then they choose another group and move to
another location. This process repeats itself and
Figure 1 – Components of the graphical user
interface
Both textual information and graphical
information was outputted during the validation
and verification stages of the network simulator.
A GUI that displays the position and activity of
each node was therefore developed. Textual
Information was outputted on the console. The
GUI proved to be very useful when testing
networks made up of a large number of nodes
and also when testing the mobility model. The
GUI included features such as node
identification, traces, transmission circles, a
single stepping function, and a progress bar.
Fig.1 shows a snapshot of the GUI.
the packet delivery rate under varying traffic
conditions.
The nodes were positioned in the form of a
grid and the neighbour count for this system is
very close to 8, calculated as in [5]. Table 1
gives the parameters for this test setup.
Table 1 – Network protocol validation over
an idle network
Number of nodes
Simulation area shape
Simulation
area
dimensions
Link delay
Duration of simulation
Duration
for
the
transmission
of
one
message
TTL
Offered Traffic (node 0
only)
5. Model Validation
The Aloha MAC protocol layer was
validated with the theoretical model given in [4]
and in static conditions. Fig.2 shows the
simulation and theoretical results when the
capture effect is switched off, while Fig.3 gives
the throughput versus traffic offered with the
capture threshold set at 5dB. Both results are as
expected.
Practical and theoretical throughput for pure Aloha
Practical
Theoretical
16
Throughput (%)
1,900 m × 1,900 m
1×10-9 s = 1 ns
43200 s = 12 hours
0.1 s
15 hops
0.01 packets/s – 100
packets/s
Fig.4 & Fig.5 show the results. As expected,
the performance of both protocols decreases as
the traffic rate is increased. The smallest
message delay for low channel utilisation was
around 5s. This delay can be attributed to the
back off duration that each node waits before
routing the message to its adjacent nodes. This
delay was introduced in order to reduce the
number of collisions.
20
18
100
Square
14
12
10
Average Delay Vs. Interarrival time
8
6
120
4
Flooding
2
Geographical
100
0
1
2
3
4
Average Message Delay (s)
0
5
Channel Utilisation (attempts per packet time)
Figure 2 – Throughput of pure Aloha
protocol
80
60
40
20
0
Throughput for Aloha with capture ratio equal to 5dB
0
2
4
6
8
10
12
Interarrival time (s)
35
Throughput (%)
30
25
Figure 4 – Average message delay for both
protocols over an idle network
20
15
10
5
0
0
2
4
6
8
10
Channel Utilisation
Figure 3 – Throughput of Aloha protocol
with a capture effect of 5dB
The behaviour of the network protocols was
verified by studying the transmission delay and
12
As a rule of thumb, the maximum back off
duration was selected to be an order of
magnitude larger than the message duration and
the smallest back off duration was selected to be
equal to the message duration, i.e., 0.1s. Hence
the mean back off duration is 0.55s. In the gridstyle scenario being considered and under ideal
conditions an average delay of 9×0.55s = 4.95s
should be expected.
Message Delay Vs. Network Traffic for various source packet rates
1400
1200
Message Delay (s)
This delay increases for higher values of
network traffic. This happens because collisions
occur more frequently, thus the messages that
were previously passing through the shortest
path under low traffic conditions, now have a
larger probability of collision. This will increase
the possibility of a sub-optimal route being
used. As a result of this, the message is
delivered over a longer path with a larger
number of hops. Thus the average message
arrival delay increases.
1000
800
600
400
200
0
1280
640
15
320
213
160
Delivery rate Vs. Interarrival time
30
80
45
67
57
Interarrival time for network traffic (s)
50
120
0-200
200-400
400-600
600-800
800-1000
60
40
1000-1200
Interarrival
time for
source
traffic (min)
1200-1400
Percentage delivery rate (%)
100
Figure 6 – Average message delay for
flooding protocol over a busy network
80
60
40
20
Flooding
Geographical
0
0
2
4
6
8
10
12
Interarrival time (s)
Figure 5 – Delivery rate for both protocols
over an idle network
The delivery rate, is largely dependent on
the amount of network traffic, whilst the amount
of source traffic is not a strong factor towards
the resulting delivery rate under these traffic
conditions. See Fig.7. The same network setup
is used for the validation of the geographicalbased protocol and similar results are observed.
Delivery Rate Vs. Network Traffic for various source packet rates
Fig.5 shows the percentage delivery rate for
the system. The lower delivery rates achieved
for higher traffic conditions are attributed to the
increase in the number of collisions.
The same network was tested for the case
when nodes 0-98 were transmitting, i.e. a busy
network and the flooding protocol was used.
Fig.6 shows the message delay for the busy
network.
90
80
Delivery rate (%)
Fig.4 & Fig.5 show that the performance of
both protocols is practically identical in terms of
the delivery rate and the transmission delay for
an idle network with node 0 as the only
transmitting node.
100
70
60
50
40
30
20
10
0
1280
640
320
213
60
160
45
80
67
Interarrival time for network traffic (s)
0-10
10-20
20-30
30-40
40-50
50-60
30
57
50
60-70
15
40
70-80
80-90
Interarrival
time for
source
traffic (min)
90-100
Figure 7 – Delivery rate for flooding
protocol over a busy network
6. Results
For this application the results of interest are
the delay for a message and the success rate in
delivering messages. These two parameters will
determine the feasibility of the system.
The performance of the flooding protocol
when routing peer-to-peer traffic over a mobile
network was studied.
In this case a low packet rate was used. The
reason for this is the additional network traffic
generated for every new packet generated.
Fig.8 shows the average message delay for
various traffic intensities. The highest average
message delay is five seconds at the highest
traffic tested. In this case the average message
delay is lower when compared to the result
obtained in the protocol validation phase. The
reason behind this is the fact that the average
number of hops is now lower than in the
previous test cases, since the distance between
source and destination varies. In some cases,
this distance might even be as short as a single
hop.
The performance of the two protocols are
very close to each other. However, the results
show that under certain conditions it is
preferable to use one protocol over the other.
Fig.8 shows the delay for the two protocols.
The two protocols behave very similarly in
terms of message transmission delay. This once
again confirms the statement that for this
particular test setup, the network size is the
main factor affecting the message delay. Thus,
the message delay should not be considered as a
prime factor for choosing one protocol over the
other.
Average delay Vs. Interarrival time
The reason for achieving such low delivery
rates can also be attributed to the fact that the
network is mobile. As previously stated, each
node keeps a history of past messages. This
feature was added to try and limit the amount of
packets generated by the flooding protocol.
However, this might pose a serious limitation on
the performance of the network under mobility
conditions. A node can retransmit a message in
a particular location. This same node then
moves to another location. The node receives
the message again in its new location. This node
might be the last and only hop which is in range
to some cluster of nodes in another community.
The node might not retransmit the message
since it is in its network history. Thus the
message will not be passed to the cluster of
nodes to which the destination node belongs.
The geographical-based flooding protocol
was studied over the same peer-to-peer network.
The average delay of the geographical-based
protocol is comparable to that observed for the
flooding protocol, i.e. 5 seconds. This shows
that the delay is mainly limited by the network
size, which limits the number of hops for each
transmission path and the back-off time, Fig.8.
The plot for the delivery rate also follows
the same shape as the plot for the flooding
protocol. However, the largest delivery rate seen
is slightly above seventy per cent for this
network setup, Fig.9.
8
Flooding
7
Average Message Delay (s)
The delivery rate of the flooding protocol in
this test is reasonable for very low traffic rates
where each node generates a new message
approximately every 8 hours. However the
amount of packets generated by the flooding
protocol for each new message is large, Fig.9.
Geographical
6
5
4
3
2
1
0
0
20
40
60
80
100
120
Thousands
Interarrival time (s)
Figure 8 - Average delay for both protocols
over a mobile network
Fig.9 shows the delivery rates obtained for
both protocols. This plot shows clearly that the
geographical-based protocol behaves slightly
better when the interarrival time is shorter than
12000s or 3hrs, 20mins. However both
protocols have a delivery rate of less than 60%
for this level of traffic. This shows that the
system is not appropriate when each user sends
more than 1 message every 3hrs, 20mins. The
geographical-based protocol gives slightly
higher performance for this higher traffic rates
since it has less overhead. Despite this, the
performance is still unsatisfactory.
The flooding protocol gives around 15%
higher delivery rates than the geographicalbased protocol for an interarrival time higher
than 20000s or 5hrs, 30mins. This higher
performance is due to the fact that the flooding
protocol does not discard the redundant paths as
the geographical-based protocol does.
Delivery rate Vs. Interarrival time
100
Percentage delivery rate (%)
90
80
70
60
50
40
30
20
Flooding
10
Geographical
0
0
20
40
60
80
100
120
140
Thousands
Interarriv al time (s)
Figure 9 - Delivery rate for both protocols
over a mobile network
It is highly improbable that all users send
more than an average of one message per 5hrs,
30mins. Thus, under these conditions the
flooding protocol should be chosen over the
geographical-based protocol. This means that
the
additional
resources
required
in
implementing the geographical-based protocol
does not offer the expected advantages for this
network, hence it is not feasible.
The delay caused by the queuing time is
highly dependant on the value of the back-off
duration. It is worth noting that the value of the
back-off duration must be chosen wisely. The
choice of the back-off is a trade off between the
responsiveness and the delivery ratio of the
system. If further simulations are conducted
with various values for the back-off duration, an
optimal value for the back-off duration can be
obtained for a particular set of network
conditions. Several routing protocols implement
some form of feedback mechanism together
with an adaptive algorithm in order to obtain the
best instantaneous value for the back-off
duration.
References
[1]
T. Camp, J. Boleng, and V. Davies, “A Survey
of Mobility Models for Ad Hoc Network
Research,” Wireless Communication & Mobile
Computing (WCMC): Special issue on Mobile
Ad Hoc Networking: Research, Trends and
Applications, vol. 2, 2002, pp. 483-502.
[2]
Mirco Musolesi and Cecilia Mascolo, “A
community based mobility model for ad hoc
network research,” Proceedings of the 2nd
international workshop on Multi-hop ad hoc
networks: from theory to reality, Florence,
Italy:
ACM,
2006,
pp.
31-38;
http://portal.acm.org/citation.cfm?id=1132983.
1132990.
[3]
“The
Network
Simulator
http://www.isi.edu/nsnam/ns/.
[4]
Yuping Zhao, “Basic MAC Protocols”;
http://signal.hut.fi/geta/courses/lecture_mat/yup
ing/Lecture%203%20%20MAC%20basics%20-%20ALOHA.pdf.
[5]
S. Kurkowski, T. Camp, and M. Colagrosso,
“MANET
Simulation
Studies:
The
Incredibles,” ACM's Mobile Computing and
Communications Review, vol. 9, Oct. 2005,
pp. 50-61.
7. Conclusions
The simulation environment which was built
in this project provides a good platform to test
new protocols in the future. It was designed in a
modular style to permit easy integration with
new protocols.
Unfortunately, the flooding protocol with
acknowledgement which was implemented as
part of the project could not be validated due to
lack of time. Hence the results for this protocol
were not included in the dissertation. This
protocol offers the acknowledgement feature
over the standard flooding protocol. It would be
a good idea to test this protocol. Position-based
protocols seem to scale well for this system.
Other protocols which use a position-based
scheme can be implemented in the simulator
and compared to the simple geographical-based
protocol which was implemented as part of this
project.
The large message delay incurred is partly
due to the re-transmit feature in the design of
the relaying protocol. If this is reduced as can be
done in practice then the message delay I
significantly reduced.
-
ns-2”;