1
A Swarm Intelligence Based Routing Protocol for Decentralised
Cognitive Mobile Radio Networks
Andrew Portelli
Dept. of Computer and Communications
Engineering, Faculty of ICT
Abstract
Mobile radio networks are renowned for the innate
high degree of flexibility they provide to the end user.
This is partly attributed to the fact that unlike wireline networks they do not require any existing
infrastructure or central administration. However
flexibility comes at a cost, and the biggest challenge
in these kind of networks is to find an efficient path
between end-to-end communication nodes which
aggravate the network throughput when they become
mobile or behave erratically. This paper presents an
innovative routing algorithm for mobile radio
networks that instils an element of cognition at the
mobile node itself. The packet transmission protocol,
which is based on the ant swarm intelligence meta
heuristic, makes the transmission process highly
efficient, adaptive and scalable with an increasing
number of mobile nodes. It also contributes towards
a reduction in routing packet overheads.
Index Terms
Ad-hoc
Routing
Networks,
Swarm
Intelligence,
1. Introduction
The ever increasing number of wireless
mobile nodes is rendering the management of
radio network infrastructures more complex
with respect to real time interventions aimed at
addressing
haphazard
network
issues.
Notwithstanding the multitude of existing
research projects in this field focusing on
decentralisation, programmable and adaptive
networks with the aim of replacing human
administration are still far from becoming a
reality [4]. Their realisation calls for networks
that are aware of their state or needs, have clear
knowledge of their goals and ways to achieve
them through independent rational decisions
and actions. Current network technologies are
reactive, in that they tend to adapt themselves
by responding to changes in the environment as
a consequence of an occurring problem.
To cater for the forecasted high increase in
wireless mobile radio users in the near future,
these networks should evolve to exhibit
cognitive characteristics where goals are
achieved through autonomous reasoning,
adaptive functionality and self-manageability.
This paper presents a hybrid on-demand
Adrian Muscat
Dept. of Computer and Communications
Engineering, Faculty of ICT
adaptation approach that incorporates a degree
of cognition into each node within the radio
network, and which actively influences the
network when the environment changes. In
purely proactive protocols like Destination
Sequenced Distance Vector Routing [6] nodes
try to maintain at all times routes to all other
nodes. Keeping track of all topology changes
can become a difficult task especially with
increasing number of nodes which are very
mobile. Reactive protocols Dynamic Source
Routing [7] and Ad-hoc On-Demand Distance
Vector routing [8] are in general more scalable.
In these protocols, nodes only gather routing
information on demand. Before nodes transmit
data to a known destination they construct a
path, and only when the path becomes infeasible
they search a new path. This approach helps in
the reduction of the routing overhead. However
networks with reactive routing protocols can
experience significant drops in performance
since these are never prepared for disruptive
events. In the presented hybrid approach, nodes
have a dual role and embrace the added
functionality of a bridge router to forward
packets and network status information to other
mobile nodes. The implemented algorithm helps
the overall decentralised network perceive
current network conditions, and as a result plan,
decide and act on those conditions whilst taking
into account end-to-end goals.
The time varying topology of an ad-hoc
network makes efficient route selection a
formidable task. The problem of simulating
mobile nodes has been under investigation for
over several years and from various presented
approaches aimed at addressing the mobility
problem realistically, there seem to be no
routing algorithm that encompasses all the
characteristics of a true mobile radio network
environment [2].
This paper presents an innovative approach
for an ad-hoc routing algorithm based on swarm
intelligence, a form of artificial intelligence
based on the collective behaviour of
decentralized, self-organized systems. Swarm
intelligence is gaining significant attention in
the development of mobile network simulators
that can truly help address many of the
shortcomings of present network simulation
platforms.
2
Over the past few years a number of swarm
intelligence based algorithms have been studied
[2,3,4] with the aim of addressing routing
problems in support to network optimisation
problems. The presented approaches have been
further studied through a custom developed
VC++ computer model that simulates the
intelligent routing protocol and the radio
network with a number of mobile nodes. The
node mobility is simulated using the Random
Waypoint Algorithm. The routing protocol
makes use of the ant colony algorithm through a
number of agents within the network
environment to determine the best route for
packet transmission over a fixed number of
mobile cognitive nodes.
This paper delves into the background of ant
colony optimization meta-heuristic in section 2,
which is thereafter followed by a detailed
presentation of the routing algorithm in section
3. Section 4 then presents the application of the
algorithm to a mobile ad-hoc network simulated
by a modified random waypoint algorithm.
Results are presented and discussed in Section 5.
Issues, advantages and future work are
presented in the last section.
2. Swarm Intelligence
The behaviour of insects that live in colonies
has been investigated by numerous naturalists
for many years [5]. How every single insect in a
social colony seems to have its own agenda
whilst still respecting the organisation structure
has always been fascinating. Moreover, the
seamless integration of all individual insect
activities to reach an equilibrium state without
any means of supervision makes this natural
behaviour a more interesting subject to explore.
2.1 Ant Colony Optimisation
Ant colony algorithms are a subset of swarm
intelligence. They are very similar to the wellknown and benchmarked travelling salesman
problem [1]. They seek to exploit the ant
movement and cooperative behaviour in their
search for food to solve complex optimisation
problems.
The search process starts with the ants
moving away from their nest and roaming
around in an organised manner in search for
food [2]. Upon reaching an intersection, ants
have to decide which route to take next. The
scenario is depicted by figure 1. The motivation
for using ant behaviour in our studies arises
from the fact that the ants do not need any direct
communication with one another during the
search
process,
therefore
minimising
communication overheads. Instead they
communicate through a spontaneous mechanism
better known as stigmergy.
2
6
D
S
3
4
1
5
Figure 1 Agent migration as a representation
of a Mover Event Graph
The exchange of information realises itself
through the laying of pheromones by each ant
travelling back to the nest after food has been
discovered. The cumulative laying and
concentration of pheromones by different ants
collectively develops into a complex network of
routes connecting the nest to the different food
sources in the most efficient way over a period
of time. The whole process is completely
decentralized.
The dynamics of this search process allows a
high adaptation to changes in mobile ad-hoc
network topology since in these networks the
existence of links are not guaranteed and link
changes occur very often. Due to changes in
pheromone concentration over time, diffusion
effects are taken into consideration in our
simulations.
2.2 Ant Colony Algorithm
The optimisation algorithm can be reduced to
the problem of finding the minimal length
closed tour that visits each node once. We
denote the Eucledian Distance between nodes i
and j as dij, which is given by
[(
dij = xi − x j
)2 + (yi − y j )2 ]1/ 2
For a selected total number of ants
N ants = ∑in=1 Ai (t )
where Ai(t) is the total number of ants at node i
at any given time t. Each ant will act as an agent
and its behaviour can be described by the
following set of characteristics:
1.
The probability of an ant visiting a
node is a function of the visibility, expressed as
ν ij = 1 d
ij
and the amount of trail present on
the connecting edge. (Other parameters can be
added in application of the ant algorithm to
better simulate mobile ad-hoc networks. To
3
keep simplicity in the interpretation of results
our simulations have been limited to these two
paramaters.)
2.
Migration to already visited nodes is
not allowed until all nodes are visited within
once complete tour. This is accomplished
through the association of a tabu list specific to
each ant that saves each of the visited nodes by
time t.
3.
Ants deposit pheromone trails of
concentration τij on each of the visited node
edges E(i,j). Upon the completion of a tour, the
trail concentration is updated as follows [3]:
τ ij (t + n ) = σ ⋅ τ ij (t ) + ∆τ ij
where 1-σ represents the coefficient of
pheromone evaporation between time t and t+n
and ∆τij is the accumulated pheromone
concentration at the node edges and is given by
∆τ ij =
where
N ants
k
∑ ∆τ ij
k =1
∆τ ijk is the amount of pheromone per
unit length left by the kth ant at each edge (i,j)
between time t and t+n. This can be expressed
as follows.


∆τ ijk =  L k
 0
P
if the kth ant goes through E(i, j)
during its tour between time t and t + n
otherwise
where P is a constant representing the total
pheromone level possible and Lk is the tour
length covered by the kth ant. The transition
probability of an agent from one node to another
is therefore defined in [3] as
k
Pij

[τ i, j (t )]α ⋅ [ν ij ]β

α
β
= 
∑
[
τ i , j ( t ) ] .[ν ij ]
 k∈allowed nodes
0

if j ∈ allowed
nodes
otherwise
where α and β are constants representing the
relative importance of trail concentration versus
agent visibility such that if α = 0, the closest
nodes are more likely to be selected. Since
agents are initially randomly distributed over
the nodes, this corresponds to a classical
stochastic greedy algorithm with multiple
starting points. With β = 0, the pheromone
amplification
process
leads
to
rapid
convergence of route discovery. This situation
is referred to as stagnation, and it is the event
during which all agents follow the same route
[4].
3. Packet Route Optimisation in
Mobile Ad-hoc Networks
We have applied the ant colony algorithm in
the implementation of an efficient and scalable
packet routing protocol for a mobile ad-hoc
network environment.
3.1 Random Waypoint Mobility Model
In this model each node is assigned an initial
location (Xinit,Yinit), a destination (Xdest,Ydest), and
a travelling speed V. The initial location and
destination of each node are chosen
independently and uniformly on the region in
which the nodes move. The speed is chosen
uniformly within an interval (Vmin,Vmax),
independently of both the initial location and
destination of each node. After reaching the
destination, a new destination is chosen from
the uniform distribution, and a new speed is
chosen, once again, uniformly on (Vmin, Vmax),
independently of all previous destinations and
speeds. Nodes may pause upon reaching each
destination, or they may immediately begin
travelling to the next destination without
pausing. If they pause, the pause times are
chosen independently of speed and location.
3.2 Route Discovery
For two or more radios to communicate with
each other, they must initially discover a
suitable route for the transmission of packets to
the radio at the receiving end. When radios are
at fixed locations, and the source and the target
nodes are within transmission range of each
other, a simple Address Resolution Protocol
query will determine the route to the target node.
The returned MAC address may then be used
directly to transmit packets to target node. The
scenario gets more complex when radios
become mobile. The status of the different
nodes starts changing without prior notice, and
changes in the transmission route becomes
necessary. New routes will have to be
discovered. Various algorithms have been
studied over the years, but practically none of
them can be commended as much as swarm
intelligent algorithms in the effective
determination of best routes for packet
transmission [3]. Our studies are assessing the
efficacy of the ant colony algorithm for mobile
radio networks.
4. Simulation Model
The behaviour of mobile nodes has been
studied using a modified Random Waypoint
Mobility Model, which not withstanding its
limitations is widely known to give good results.
[3]. The simulation was implemented by custom
4
4.1 Performance Metrics
We have selected the Packet Delivery Ratio
and the Protocol Control Overhead as a metrics
during the simulation in order to evaluate the
performance of the network with and without
the use of the intelligent ant based routing
protocol. Metrics are defined as follows:
Packet Delivery Ratio: The number of packets
sent from the source to the number of received
at the destination;
Packet Hop Count: The number of legs
traversed by a packet between its source and
destination;
Routing Packet Transmission Ratio: The ratio
between the total number of routing packets
transmitted by all nodes for best route discovery
and the number of data packets delivered to the
destination nodes.
5. Numerical Results
Simulation results illustrate how sensitive the
network capacity is to the network topology
configuration denoted by parameters RM and RC.
The behaviour of this network is initially
analysed by randomly placing the start positions
of each of the mobile nodes within the mobility
area and varying the transmission coverage
radius of each radio equally whilst monitoring
the network throughput. It is understood that an
increasing number of nodes may fall within the
transmission zone as the coverage radius is
increased. Figure 2 shows that the capacity can
increase up to 10 times the coverage area as this
approaches the size of the mobility area. This
behaviour is due to the higher probability of
nodes being located near the centre of the
mobility area and thus higher percentage of
nodes selecting the high transmission rates. This
behaviour is represented in figure 3.
1
0.9
0.8
Packet Delivery Ratio
developed discrete event simulation (DES)
using VC++. The discrete event approach was
preferred to the time-stepped approach since in
DES an event is only scheduled when an entity
changes its movement state. In the time stepped
approach, the entity’s state is updated every
time step regardless of whether the entity’s state
has indeed changed or not. This approach turns
out very costly in terms of simulation time and
hardware processing resources.
The results presented in this paper are limited
to a fixed number of ten active nodes which are
uniformly distributed on a two dimensional
square simulation domain of size 1500m ×
1500m. We denote the one dimensional size of
the mobility domain by RM. The mobility of
nodes is characterised by each node moving
from one waypoint to another in a straight line
with a constant velocity which is randomly
selected in the interval of (1,20) m/s each time a
node reaches a destination. A non-zero
minimum node speed has been considered to
compensate for the initialisation problem related
to this mobility model. Each simulation was run
for a period of 900 seconds. Different pause
times have also been considered in the
evaluation of network performance such that
each node stops at each waypoint for a
predefined constant time.
Within our simulation domain, the area
encompassing a node is identified as the
coverage area of radius RC determined by the
maximum transmission range of the radio such
that when a radio at a receiving node is out of
the maximum transmission range of a radio at
the transmitting node, the transmitting radio
transparently makes use of the ant colony
optimisation algorithm to establish the best
route for packet transmission to the destination
radio at the least cost possible. For the sake of
simplicity we have limited our cost metrics only
to shortest packet routing to destination.
Simulations assume an ALOHA collision free
multiple access radio protocol in which each
node is allowed to transmit data packets when
these become available. Each data packet is
expected to reach all the other active nodes
except the same node transmitting it. A constant
bit rate transmission is used in all of the
simulations and the size of each data frame has
a fixed length to maximise the throughput of the
network and also to facilitate the interpretation
of the obtained results. The ant optimisation
algorithm is also assuming a fixed and equal
number of agents from one simulation run to
another.
0.7
Random Velocity
Vavg=20m/s
Vavg=15m/s
Vavg=10m/s
Vavg=5m/s
0.6
0.5
0.4
0.3
0.2
0.1
0
1
2
3
4
5
Ratio of Mobility Area Size Rm to Coverage
Radius Rc
6
Figure 2. An increase in the coverage radius
changes node leads to an increase in network
throughput when nodes are mobile.
Network throughput is increased significantly
when RC ≈ RM. It is also noted that for a fixed
node velocity of 10m/s, the curve approximates
closely the curve for a uniformly distributed
node velocity in the range of (1, 20)m/s when
simulated by Random Waypoint Algorithm.
This confirms the theoretical average node
5
velocity for this model which is approximated
by Vmax/2 in [1].
1250
0.4
1000
Node 5
Node 8
750
0.3
Packet Delivery Ratio
Movement in Y Direction (meters)
1500
Simulation results have shown that network
throughput can be significantly increased with
an optimised routing protocol. Figure 6
compares the packet delivery ratio using best
route optimisation and without the use thereof.
500
250
0
0
250
500
750
1000
1250
Adhoc Net
Trend
0.2
0.1
1500
Movement in X Direction (meters)
Figure 3. Travelling pattern of two nodes
described by Random Waypoint Algorithm
where the probability of a node being located
towards the centre of the mobility area is higher
than at the edges.
Network throughput has also been analysed
with different node pause times for a longer
simulation period. Figure 4 shows that node
pause time has very little affect on the network
throughput and thus simulations can assume that
nodes do not stop when reaching the destination
but continue moving in a new direction at a
different speed.
0
0
5
10
15
20
Node Speed (m/s)
Figure 5. Network Packet Delivery Ratio for
different node speeds.
Results show over a threefold improvement,
with network throughput exceeding 96% for
most of the time. Furthermore node speed has
negligible impact on network throughput. Data
packets reach the listening radios almost
instantaneously once the best transmission route
is available to the transmitting radio.
1
0.9
0.8
Packet Delivery Ratio
(without Route Optimisation)
0.3
0.25
Adhoc Net
0.2
Trend
0.15
Packet Delivery Ratio
0.4
0.35
0.7
0.6
0.5
RW + ACO
0.4
RW
0.3
0.2
0.1
0.1
0
0.05
0
5
10
15
20
Node Speed (m/s)
0
0
300
600
900
Pause Time (seconds)
Figure 4. Network Packet Delivery Ratio for
different node pause times.
The same metric was also analysed against
different node speeds and with the node pause
time fixed to zero. Figure 5 represents the
network throughput for varying node speeds. It
is noted that network performance drops by
approximately 8% as node mobility increases
from nearly stationary to 20m/s. This behaviour
can be explained in terms of the probability of
receiving nodes falling outside the transmission
coverage area of transmission nodes. Higher
drops in the packet delivery ratio are
experienced with stationary transmitting radios
and with all other listening radios mobile at
varying speeds. The leap in network
performance as a result of the integration of the
ant based optimisation algorithm aimed at
enhancing the routing protocol have been
investigated on the latter metric.
Figure 6. Comparison of Network Packet
Delivery Ratio for different node speeds, with
and without the use of ant based optimised
routing protocol.
The impact of node mobility on the end-toend packet transmission delay has been
analysed by monitoring the hop count for a data
packet to transmit itself from its originating
source to the destination node whilst honouring
the discovered best route defined by the ant
algorithm. Simulations carried out in this
analysis are assuming always active nodes with
no link breakdown.
It can be noted that the average path length
reaches a constant value with increasing node
speed. This is due to a decrease in the average
distance between neighbouring nodes with
increasing node mobility. The relatively higher
hop count at low node speeds can be explained
in terms of the initial node locations and the
distance from their neighbours. Higher end-to-
6
end packet delays are demonstrated by less
mobile or stationary nodes. Similar behaviour is
noted by the routing packet transmission ratio
metric against node speed as shown in figure 8.
6
Average Path Length (Hop Count)
5
4
3
2
1
0
0
5
10
15
20
Node Speed (m/s)
Figure 7. Impact of node speed on the average
number of hops for a data packet to reach
destination.
2000000
misbehaviour such as the dropping of routing
packets, misreporting of link status, or the
sniffing of data packets is another key area of
study which we aim to go into at a later stage.
Although this paper highlights some of the
crucial benefits of the ant colony optimisation
algorithm based on efficient route discovery for
packet transmission, more work is envisaged to
further exploit the potential of this algorithm in
wireless network environments, specifically in
scenarios where the network topology is
different and where various mobility models are
active simultaneously. The achieved results
have increased our confidence that the
scalability, adaptability and robustness of the
ant based algorithm can further enhance the
intelligence of the mobile radios without
featuring as a burden on the overall network
performance when compared to other routing
methodologies.
Routing Packet Transmission Ratio
1800000
7. References
1600000
1400000
1200000
1000000
800000
600000
400000
200000
0
0
5
10
15
20
Node Speed (m/s)
Figure 8. Impact of node speed on the
Routing Packing Transmission Ratio
6. Conclusion and Future Work
The performance of an ad-hoc network
employing best route discovery protocols based
on swarm intelligence depends on a number of
factors such as the number of active or passive
radios, node density, the number of agents
employed in route discovery, the number of
successful route discovery packet transmissions,
and the various parameters related to
performance of the ant optimisation algorithm
affecting end-to-end packet transmission delays.
Their influence has been analysed in this paper.
The simulation results presented herein are
only taking into consideration a network
topology with constantly active and behaving
nodes without considering the network load. If
for example on link S-1-3 in figure 1 there is
always a huge amount of traffic, the selection of
the alternative route S-2-3 may result in better
network performance even though the routing
path is longer than S-1-3. Future work may
consider a modified ant colony optimisation
algorithm which monitors network traffic and
flow; vital information that agents can drop at
each active node to further enhance the
knowledge of each radio on the network. Radio
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