This document proposes a modified ant colony optimization (ACO) routing protocol for mobile ad hoc networks (MANETs). The key points are: 1) The protocol is based on swarm intelligence principles and uses mobile software agents like ants to intelligently route packets from node to node. 2) It modifies the standard ACO algorithm to make it power-balanced and achieve faster packet delivery rates by making the pheromone decay dependent on nodes' battery levels. 3) The routing process involves forward and backward ants establishing and maintaining routes between source and destination via probabilistic path selection based on accumulated pheromone levels.

1. Modified Ant Colony Optimization (ACO)
Based Routing Protocol for MANET
Saptarshi Banerjee
Assistant Software Engineer
TCS
West Bengal, India
banerjee.saptarshi44@gmail.com
Arnab Majumdar
Dept. of M.M.E.
N.I.T. Durgapur
West Bengal, India
xie.7802@gmail.com
Himadri Nath Saha
Dept. of C.S.E
IEM Kolkata
West Bengal, India
him_shree_2004@yahoo.com
Ratul Dey
Dept. of C.S.E
IEM Kolkata
West Bengal, India
ratul170292@gmail.com
Abstract--A mobile ad-hoc network (MANET) is a collection of
mobile nodes which communicate over radio. These kinds of
networks are very flexible, thus they do not require any
existing infrastructure or central administration. Therefore,
mobile ad-hoc networks are suitable for temporary
communication links. The biggest challenge in this kind of
networks is to find a path between the communication end
points, which is aggravated through the node mobility. In this
paper we present a new on-demand power-balanced routing
algorithm for mobile, multi-hop ad-hoc networks. The
protocol is based on swarm intelligence and especially on the
ant colony based meta heuristic. These approaches try to map
the solution capability of swarms to mathematical and
engineering problems. The proposed routing protocol is
highly adaptive, efficient and scalable. The main goal in the
design of the protocol is to reduce the overhead for routing.
Our simulation results show that the proposed routing
protocol is significantly different from existingprotocols.
Network is known as ad-hoc because each node is ready to
forward data for other nodes. Hence the determination of
nodes, which will be used to forward the data is calculated
dynamically based on the network connectivity. This is
completely a different concept when compared to older
network technologies where some designated nodes,
usually with custom hardware and variously known as
routers, switches, hubs, and firewalls, perform the task of
forwarding the data. Minimal configuration and quick
deployment make ad hoc networks relevant to handle
emergency situations such as natural or human-induced
disasters, military conflicts.
A Mobile Ad-hoc Network (MANET) is a WANET
where the nodes do not follow any particular geometry and
route-paths change dynamically shown in fig.1. Also
MANET has no centralized base station and hence is a very
attractive option for the telecommunication industries to
exploit. Another feature of MANET is that they generally
self organize themselves and have the power of adaptation.
This actually helps the MANET to construct and
deconstruct on the way without needing any system
administration. These uncommon features are responsible
for making MANET a very tantalizing option for scenarios
which includes brisk network deployment such as search
and rescue operations. The process of forwarding packets
from source node to destination node is called routing.
There are three types of routing in MANET (i) Proactive,
(ii) Reactive and (iii) Hybrid. In Proactive Routing
Protocol, route is pre-decided i.e. table-driven. In Reactive
Routing Protocol, route is created on-demand basis. Hybrid
Routing Protocol uses both the above mentioned routing
approaches.
Keywords--MANET; Routing; Ant Colony Optimization;
power-balanced; intelligence routing
I. INTRODUCTION
A Wireless Ad Hoc Network (WANET) is a
decentralized type of wireless network. The network is ad
hoc because it does not rely on a pre-existing infrastructure,
such as routers in wired networks or access points in
managed (infrastructure) wireless networks. Instead
each node participates in routing by forwarding data for
other nodes, so to determine which nodes forward data is
made dynamically on the basis of network connectivity. In
addition to the classic routing, ad hoc networks can
use flooding for forwarding data.
Naturally the present days’ routing algorithms are not
adequate to tackle the growing complexity of such
networks. The centrally designed algorithms have severe
problems on scalability, whereas static algorithms have to
An ad hoc network typically refers to any set of networks
where all devices have equal status on a network and are
free to associate with any other ad hoc network device in
face trouble in order to keep them up-to-date withlink range. Ad hoc network often refers to a
operation of IEEE 802.11 wirelessnetworks.
mode of
network changes; along with other several distributed and
dynamic algorithms have to combat problems on their
oscillation and their stability parameter [1]. ACO based
routing protocol supplies a promising and challenging
alternative approach towards these approaches. The Ant
A wireless ad-hoc network is depicted as
“Independent Basic Service Set”, which is a
IBSS –
computer
network where the communication links are wireless. The
978-1-4799-6908-1/15/$31.00 ©2015 IEEE

2. Colony Optimization protocol [2] controls entire network
management by the utilization of mobile software agents in
various ways. These working agent nodes including both
classes of Proactive as well as Reactive are autonomous
entities for system. Agent nodes possess capability to
cooperate and move packets intelligently from one node to
the other one within the communication network. To make
the algorithm power-balanced and in order to achieve
steeper convergence of packet-delivery rate ACO algorithm
has to be modified.
intelligence which utilises the network management and
the agents involved are autonomous entities which are
proactive and reactive [10,11]. These entities have the
power to identify location update. In order to achieve
power-balance of the algorithm as well as in to attain
steeper convergence for packet-delivery rate, ACO
protocol needed to be modified.
III. ANT COLONY OPTIMIZATION
The ant colony optimization algorithm (ACO) is a
probabilistic technique for solving computational problems
which can be reduced to finding good paths through
graphs. In MANET routing is a cumbersome problem as
network characteristics such as traffic load and network
topology may differ problematically and in a time varying
nature. The multi-agent nature of ACO algorithms is very
well equated with the distributed nature of network routing.
This algorithm is a member of the ant colony algorithms
family, in swarm intelligence methods, and it constitutes
some meta-heuristic optimizations which are based on the
behavior of ants seeking a path between their colony and a
source of food. The first ants select paths randomly. They
deposit pheromone to mark trails. New ants are likely to
follow this trail and reinforce it if they find food. Over the
time, pheromone diffuses. Thus, longer paths have less
pheromone concentration than shorter paths, after some
time. Eventually, all the ants start following the shortest
path due to its highest pheromoneconcentration.
B
A
AB
Fig. 1 (Mobile Ad-hoc Network)
The paper is organized as follows: Section II of this paper
basically gives the brief information about some previous
works related to this research work. Section III describes
the key concept of Ant Colony Optimization protocol.
Section IV illustrates the proposed idea of modified Ant
Colony Optimization. Section V explains the algorithm of
ACO based MANET Routing combining the idea of ACO
and OLSR protocol. Section VI elaborates the network
description of Ant Colony Optimisation protocol in details.
Section VII provides the mathematical evaluation of the
algorithm along with certain proposed modifications.
Concluding remarks along with the future works are given
in Section VIII.
A. ANT ALGORITHM OVERVIEW
Let G = (V, E) be a connected graph with n = |V|nodes.
ACO can help find the shortest path between 2 nodes vS
and vD (path length being no. of nodes from source to
destination)
II. RELATED WORK
Each edge e (x, y) є E, has an associated variable
(artificial pheromone) which is modified by the ants when
they visit the node.
In [3] authors described routing solution which is modelled
by ant systems. The routing protocol uses a new metric to
find the route with higher transmission rate, less latency
and better stability. In [4] QoS of routing algorithm
depends on ant colony meta-heuristic Load balanced
routing aims to move traffic from the areas that are above
the optimal load to less loaded areas, so that the entire
network achieves better performance. If the traffic is not
distributed evenly, then some areas in a network are under
heavy load while some are lightly loaded or idle. In [5]
dynamic load aware routing (DLAR) protocol routing load
of a route has been considered as the primary route
selection metric.
An ant k, from node x, visits node y, with a probabilitypk
xy
given by,
…(1)
where, is the “attractiveness” of this state
A new protocol [6, 7, 8] briefly describes the
transformation of models of collective intelligence of ants
into the useful optimization and control algorithms.
Another protocol describes [9] on the concept of swarm
change, allowed y is the set of neighbours of node x, and
≥ 0 and ≥ 1 are parameters to control the
influence of and respectively.
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3. B. PHEROMONE UPDATE when the charge is full, i.e., r = 100%, let ρ be a constant
(randomly chosen small value). As the routing progresses,
and the charge decays, or more specifically when r < (1 -ρ),
we use this relationWhen all the ants have completed their solution, the trails
are updated by the equation
= r * …(5)
…(2) instead of the previous relation.This will make the
algorithm power-balanced and achieve steeper convergence
of packet-delivery rates. To implement our proposed
modification, we use an Update Probability model
function. It takes in the battery charge as the input and
changes the probability model used in the algorithm [12].
where, ρ is the pheromone-evaporation coefficient, and is
the amount of pheromone deposited by the kth
ant
V. ACO-BASED MANET ROUTING
….(3)
Where, Q is a constant, and Lk is cost of kth
ant’s tour.
ACO is very suitable to MANETs, especially because of
the dynamic topology. The routing occurs in 3phases:
Route discovery phase – New route from a source to
destination is established.
Route maintenance phase – Improvement of initialroutes
during communication, to converge to the optimumroute.
Route failure handling – actions performed upon failure
to establish a route or departure of nodes fromthe network.
C. FLOW CHART
ROUTE DISCOVERY PHASE
A forward-ant (FANT) – a small packet with a unique
sequence number – is broadcasted from the source node to
all neighbouring nodes. The FANT establishes the
pheromone track to the source node. A node receiving a
FANT for the first time creates a record in its routing table,
which is a triple (destination address, next hop, pheromone
value).The pheromone value is computed based on the
number of hops the FANT needed to reach this node. It
then relays the FANT to its neighbouring nodes. When the
FANT reaches the destination node, its information is
extracted, and it is destroyed. Subsequently, the destination
node creates a backward-ant (BANT) and relays it back to
its neighbours. The BANT has the same role as the FANT
establishing a pheromone track to the destination node.
When the BANT reaches the source node, it is destroyed
and the route is established [13].
Fig. 2 Flow Chart
ROUTE MAINTENANCE PHASE
The data packets themselves are used to maintain the path;
no special packets are needed. When a node vi relays a
data-packet toward the destination node vD to the next hop
vj , it increases the pheromone value of the entry (vD, vj, τ)
by an amount Δτ , i.e., the path is strengthened by the data-
packets [14]. In contrast, the node vj increases the
pheromone value of the entry (vS, vi, τ) by an amount Δτ
The regular diffusion of pheromone is done by the decay
equation stated earlier.
IV. PROPOSED MODIFIED ACO
Instead of having a constant pheromone decay coefficient,
in the decay relation
…(4)= (1 - ρ)
we propose to use the residual battery charge of a mobile
hop as a parameter to guide the decay. Let ‘r’ be the
residual battery charge of a mobile hop. We propose that,
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4. ROUTE FAILURE HANDLING
Routing failures caused by node mobility are common in
MANETs. ACO recognizes route-failure through missing
acknowledgements. If a node gets a ROUTE_ERROR for a
certain link, it first deactivates the link, by setting
pheromone value to 0; then it searches for alternative paths
in its routing table; if it finds one, it routes through that
path; else it informs its neighbours hoping that they can
relay the packet. Either the packet can be transported to the
destination node, or the backtracking continues up to the
source node. If the packets cannot reach the destination
node, then a new Route Discovery Phase has to be initiated.
Also, DUPLICATE_ERROR can be checked through
unique sequence numbering of the packets[15].
A. PROCESSING FANTs AND BANTs
FANTs
1. If it has reached its destination, then
a. Determine the previous node and set it as neighbor
chosen to simplify processing of this ant atrouter.
b.
2.
Record the node ID and entrance time onto astack.
Else if the ant was generated at this node,
a. If it has reached its hop, or age limit then
delete it.
b.
3.
Else proceed to selecting links.
Finally, send it back to the router.
BANTs
First, we update the destination node entry of the
current ant in the routing table
If the (b-)ant was generated at this node (i.e., it has
come back to its source), we send it to Ant Sink.
Else, we set its source as the Ant Nest, and send it to
the router.
VI. NETWORK DESCRIPTION
1.
The description of the Network is shown in figure 3.
The Network consists of 6 main components
responsible for handling the routingoperations.
router–
2.
3.
SELECTION OF LINKS
Calculate the goodness for feasible links (heuristic
correction factor) along with their goodness
probabilities.
If exploration Probability > p, then next hop node
1.
2.
is selected in random uniform way
(p = 0 in the original version, and a random
number in Gianni’s version)
Select the out link in a probabilistic proportional
way according to the computed goodness
estimates goodness Probability. The selection is
made among the nodes not visited yet. If all the
nodes have been already visited the one with the
highest goodness estimate is deterministically
selected.
Record the node ID and entrance time onto a
stack.
3.
Fig.3 Simulated Network
4.dGen – the data Generator, which generate and send data
to be transmitted to the router and aGen.
aGen – the Ant generator, which generates the forward ant
(FANT) messages taking the input from the Datagenerator.
aSink – the Ant Sink, responsible for processing/
destroying the backward ant (BANT).
aNest – the main component - the Ant Colony, which
processes the ants and implements the ACO.
dSink – the data Sink.
B. PHEROMONE MODELING & HEURISTIC
CORRECTION FACTOR
The main task in implementing and simulating the ACO
algorithm is the modeling of the ‘pheromone’ required for
the calculation of the probability. We have used the link-
delay to reach the node as the factor that implements
pheromone concentration [16].
More the link-delay, slower the traffic, more the time taken
by ants to tread this path, and therefore, it can be assumed
4

5. intuitively: lower is the pheromone concentration
Pheromone concentration ∝ - Link Delay [17].
The Heuristic Correction Factor does essentially 2 things.
4. From current node to destination get Node (last entry)
we create a new vector for ourprocessing.
5. First entry of this node is current node, hence we can
save it.
6. We keep on iterating on the nodes and destination node
is always updated while in case of sub paths only good sub
paths are updated.
1. It sets the total number of feasible links from this
node – the nodes which have either not visited
before, or all nodes, if all have been visited before.
Calculates the normalized link delay for each
neighbor into the hFactor[] array.
2.
7. Time to different nodes is difference of entrance times
both nodes.
Other statistical calculations (like reinforcements and
squashing function) are utility functionality and not
mandatory for ACO Implementation[19].
C. CALCULATION OF PROBABILITY
1. The probability of selection of links is computed
as
max { goodness Probability (neighbor #i) }
Here the resultant path finds the optimized but also suitable
path for the sending continues data set from source to
distention.
2. ‘goodness probability’ (neighbor #i) = normalized
( ‘Pgoodness’ (neighbor #i) )
Pgoodness (neighbor #i) = Probability to reach3.
destination node via neighbor #i +
Weight * Link-delay for neighbor #i
Queue
VII. SIMULATION RESULTS
We have used OMNET++ 4.5 for Simulation. Simulation
environment is as shown in figure 4.
Where, destination node is where we intend to
send this ant message to & Queue weight is the
number of ant messages left to be sent.
Probability to reach one node from another, for all
combinations, is maintained in the routing table,
Link-delay of neighbors are maintained in the
neighbors[] array (set by hFactor[]),and goodness
Probability is maintained in the goodness
Probability[] array for each neighbor.
4.
5. Dynamic Programming Approach:
Probability of ant to reach destination node = max
{Probability of selecting neighbor #i + Probability
of ant to reach destination node via neighbor #i}
for all neighbors.
D. RESCALING PROBABILITIES
Fig. 4 Simulation Environment
Rescaling probabilities penalizes low probabilities and
enhances higher probabilities. It is implemented simply by
powering the probabilities selectively with the ‘rescaling
power factor’ α and normalizing them[18].
This simulation results shows in how many path node 1
(n1) can communicate node 43 (n43). The various paths
followed here are:
Path 1: Here the path followed is n1, n4, n5, n10, n13, n18, n19,
n26, n32, n34, n40, n 43
E. UPDATING ROUTINGTABLE
To calculate the trip time from current node to the sub-
destination, we apply the following algorithm:
1. Stack is implemented using a vector for simplification of
sub-path updating.
2. Since we use pushback, hence first node visited is at
begin() and last node visited is towards the end of the
vector.
3. We determine the position of current node in vector
status.
Path 2: Here the path followed is n1, n4, n7, n6, n9, n11, n14,
n22, n21, n25, n27, n30, n36, n35, n39, n42, n43
Path3: Here the path followed is n1, n4, n7, n12, n15, n16, n23,
n24, n28, n31, n33, n37, n38, n42, n43
………………….
Path n: Here the path followed is n1, n 4, n 7 ,n 6 ,n 9 , n 11, n 12
n 15 n 14 n 22 n 21 n 25 n 27 n 30 n 36 n 35 n 39 n 42 n 43
5

6. Out of all the paths possible for n1 to communicate to n43,
Path 1 is observed as the most optimized path. But an error
is observed between n5 and n10 as n10 is unable to accept
any packets. Hence the most optimized path will be Path 3
which involves traversing through minimum number of
nodes after Path 1.
Histogram showing the Packet Delivery rate for Modified
ACO
Histogram showing the packet delivery rate forACO
Fig. 7 Histogram showing the packet delivery rate for Modified ACO
The above stated histogram shows the change of the packet
delivery rate of modified ACO Routing Protocol with the
change in time.
Convergence graph for Packet Delivery rate for
Modified ACOFig. 5 Histogram Packet delivery rate forACO
The above histogram shows the change of the packet
delivery rate of ACO Routing Protocol with the change in
time.
Convergence graph for Packet Delivery rate forACO
Fig. 8 Convergence graph for Packet-delivery rate for ModifiedACO
The above stated graphical plotting shows the change of
the packet delivery rate of modified ACO Routing Protocol
with the decay of time.
Fig. 6 Graph of Packet delivery rate forACO
VIII. CONCLUSION
The above graphical plotting shows the change of packet
delivery rate of Ant Colony Optimization protocol withthe
time decay.
The proposed protocol achieves steeper convergence of
packet delivery rates, as expected, since the ants find the
routes faster than original with battery charge decay. It has
been observed that the packet delivery ratio deceases; this
can be due to either faster convergence and rescaling
probabilities reduce the duration of reach ability of each
node, or increased processing overhead. Hence, we
conclude that our simulation achieves the desired and
expected results. The packet delivery ratio can further be
improved by controlling the influence of the batter-residual
charge through the following modification:
Again while we are considering the modified Ant Colony
Optimization routing protocol, the delivery rate of packets
changes rather increases with the time, which can be shown
by both the histogram as well as graphical plotting. This
result will make the power-balanced algorithm and will
achieve a higher steeper convergence of packet-delivery
rates.
6

7. We modify the decay equation further to incorporate an [9] T. White and B. Pagurek, “Toward multi-swarm problem solving in
networks,” in Proc. 3rd Int. Conf. Multi-Agent Systems, July 1998, pp.
333–340.
influence control factor 0 << < 1, so
equation becomes
that the modified
[10] J. Mandal and H. N. Saha, “Modified Ant Colony Based Routing
Algorithm in MANET,” International Journal of Computer &
Organization trends (IJCOT),vol..3, no.10, pp.473-477, November 2013.
= r<
…(6)
The Ant-based Routing Algorithm has 2 versions – we [11] H. N. Saha, K. Hazra, I. Mondal, M. Chakraborty and S. Sarkar, “A
Review on Intelligence Secure Routing Protocols in the Mobile Ad hoc
Networks,” International Journal of Advanced Multidisciplinary
Research(IJAMR),vol..1,no. 3, pp.01–13, December2014.
implemented the normal one; there is a newer version for
Flying Ants in which Ants don’t wait in the Message
Queue. This can be implemented with minor modifications
from the existing implementation. We have also kept scope
for extending the implementation to the Beehive Algorithm [12] Mesut G¨unes, Udo Sorges, Imed Bouazizi, “ARA – The Ant-Colony
Based Routing Algorithm for MANETs” International Workshop on Ad
Hoc Networking (IWAHN 2002), Vancouver, British Columbia, Canada,
August 18-21, 2002.
(variant of ACO) – closely resembling
implementation.
the ACO
[13] Adamu Murtala Zungeru, Li-Minn Ang and Kah Phooi Seng,
“Classical and swarm intelligence based routing protocols for wireless
sensor networks: A survey and comparison,” Journal of Network and
Computer Applications, Elsevier Vol.35, No 5, September 2012, pp.
1508–1536
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