Route optimization in Mobile Adhoc Network (MANET) routing protocols using cuckoo search algorithm.

1. Jour of Adv Research in Dynamical & Control Systems, 12-Special Issue, August 2017
Dynamic Search Technique for Optimizing
MANET Routing in LPNS: An Approach of
Cuckoo Search Algorithm
S. Chandia, Research Scholar, Department of Computer Science, Government Arts College, Coimbatore, India.
E-mail:chandiacit@gmail.com
Dr.M. Devapriya, Assistant Professor, Department of Computer Science, Government Arts College, Coimbatore, India.
E-mail:devapriya_gac@rediffmail.com
Abstract--- In MANETs, the network topology changes are caused mainly due to the sudden demise of the nodes
without prior notice to the neighbour nodes. Performance degradation caused due to these topology changes are
tackled by developing Loyalty Pair Neighbors selection based Adaptive Re-transmission Reduction Routing in
MANET (LPNS) protocol which enhances the loyal neighbour node selection for constructing the stable paths and
minimizing retransmissions. In this work, a Meta heuristic optimization technique Cuckoo Search (CS) is considered
for the optimization of manet routing. In the proposed ELPNS_Cuckoo protocol, the neighbour selection in Loyalty
Pair Neighbors selection (LPNS) is enhanced using the Cuckoo Search optimization algorithm. It achieves improved
performance in terms of packet delivery ratio, throughput and delay.
Keywords--- MANET, Routing Protocol, LPNS, Cuckoo Search.
I. Introduction
A mobile ad hoc network (MANET) is a collection of wireless nodes that is self-configured to form a network
without the aid of any established infrastructure. The nodes are mobile and their movement is random. Each host in
this network must have the ability to work as a router. The host mobility changes the quality of wireless link signals
because of the changes in the propagation path loss, shadowing effect, multipath fading, and interference [1]. This
leads to the link failure and breaks all the routes that use this link. MANET needs an efficient adaptive routing
protocol because of its highly dynamic topology [2]. Ad hoc networks have to face several challenges, such as
dynamic topology, real-time communication, resource constraint, bandwidth management and packet broadcast
overhead. These issues complicate the network to design the routing protocols [3]. There have been many routing
protocols developed for MANET over the past few years. The primary routing protocols for MANET consume
considerable amount of battery power present in the nodes. Thus, routing in MANET is very much energy-
constrained [4].
In our previous work, the LPNS based Adaptive Re-transmission Reduction Routing has been developed for
tackling the effects of the network topology changes and reducing the control overhead, delay and energy
consumption. Network path delay and path retransmission information are considered in that approach. In this
proposed paper, the LPNS is enhanced withCS(ELPNS_Cuckoo) which leads to achieve efficient routing with
better topology control. The remainder of this article is organized as follows: Section 2 describes the related research
works briefly. Section 3 explains the proposed methodology while section 4 provides the evaluation results of the
proposed methodology. Section 5 makes a conclusion about this research work.
II. Related Works
The broadcasting in MANET causes large routing overhead which leads to redundant retransmissions. Thus to
improve the routing performance, broadcasting should be optimized in route discovery phase.
Minimizing the Maximum used Power Routing (MMPR) [5] takes into account the power consumption and
remaining power. MMPR selects the path that consumes minimum power for data transmission. In addition, MMPR
also considers the remaining power under the selection of the desired path to prolong the network lifetime. However,
MMPR does not consider the transmission amount of the selected path, so that the path may break during the data
transmission. Energy-aware routing schemes have been employed to prolong the lifetime of energy constrained
mobile nodes in ad-hoc networks. The routes have been mainly identified by considering the energy spent to
transmit packets from source nodes to destination nodes, or the RE of nodes.
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2. Jour of Adv Research in Dynamical & Control Systems, 12-Special Issue, August 2017
In [6], a method is proposed that has been advocated to improve routing efficiency to select the most stable path
so as to reduce the latency and the overhead. Clearly, the probabilities of path duration and path availability strongly
depend on the mobility pattern of the nodes which in turn depends on the movement of a node with respect to others
in the network. The work presented in [7] analyzes synchronous as well as asynchronous heuristics for discovering
nodes with prolonged topological stability. These nodes appear more appropriate to be elected as cluster heads, since
the frequency of cluster head re-election and re-clustering can be decreased. The heuristics described rely on 2-hop
topological information and avoid any use of geographical data. The node location information has recently found
use in solving many existing problems in MANETs. The directly communicable nodes of any node (i.e., the
neighbors) and, ideally, the location of the other nodes should be available in advance to the node. The beacon-less
routing protocol (BLR) [8] is a position based routing protocol which uses the geographical location information to
minimize routing overhead. BLR does not require nodes to periodically exchange beacon packets which minimizes
the usage of battery power and interferences for the regular data transmission.
In, the implementation of protocol HMQ Ant (Hybrid Multipath QOS Ant) is done with ACO (Ant Colony
Optimization) based hybrid ad hoc routing strategy for a hierarchical MANET architecture. The designed protocol
gives optimum solution for adaptive and dynamically changing networks. Though the literature has many research
works, most of them do not consider the QOS metrics while some suffers from high energy consumption. Hence this
motivates the proposed research concept.
III. Proposed Methodology
In the proposed approach, the LPNS protocol is employed along with Cuckoo Search algorithm.
3.1 Loyalty Pair Neighbors Selection
As described in our previous work, the LPNS scheme which includes the Adaptive re-transmission reduction
routing provides minimized end-to-end delay and increased packet delivery ratio. In this approach, the inmate loyal
neighbor list is generated and the current queue size and remaining battery power are shared between the neighbor
nodes. The power values are computed to obtain the remaining power of each node based on which the nodes are
categorized into high and low power nodes. Among these categorized nodes, the high power nodes with large queue
size are selected as the loyalty pair to form the neighbor set. Then the node mobility, direction and angle to revise
loyalty pair nodes are computed while the retransmission delay is also calculated for sorting the transmission node
set. Additionally the re-transmission feasibility is estimated using edge facet and transmission-edging ratio. Finally
based on the computed values, the loyalty pair set is updated within the sorted routing table. Finally data packets are
transmitted using the route discovered through the loyalty pair nodes.
3.2 Loyal Pair Nieghbor Selection with Enhanced Cuckoo Search Algorithm(ELPNS_Cuckoo)
First, the CS algorithm is described briefly. The CS algorithm is a population based stochastic global search
algorithm inspired by the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of host
bird. Yang & Deb [9] combined the cuckoo breeding behavior [10] with the Levy flights [11] and proposed cuckoo
search algorithm. In the algorithm, each egg in a nest represents a solution (loyalty pair neighbors), and a cuckoo
egg represents a new solution, the aim is to employ the new and potentially better solutions to replace not-so-good
solutions in the nests until the optimal solution is found. During the search process, there are three idealized rules
that should be emphasized: (1) each cuckoo lays one egg at a time, and dumps it in a randomly chosen set; (2) the
best nests which has high quality of eggs will carry over to the next generations; (3) the number of available host
nests is fixed, and the egg laid by a cuckoo is discovered by the host bird with a probability 𝑝𝑝𝑎𝑎 ∈ [0,1], in this case,
the host bird can either throw the egg away or abandon the nest to build a new nest (new solution) in a new location.
Based on the above-mentioned three rules, the CS can be implemented simply as follows:
Begin
Objective Function :f(x),x=(x1, x2,...xd);
Generate initial population of n host nests;
While (t<MaxGeneration) or (Stop Criterion)
Get a cuckoo(i) and replace its solution by Levy Flights
Evaluate its fitness Fi
Choose a nest j among n randomly
If (Fi>Fj) Replace j by new solution;
End if
Keep the best solution;
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3. Jour of Adv Research in Dynamical & Control Systems, 12-Special Issue, August 2017
Rank the solution and find the current best;
Pass the current best solution to next generation
End while
Loyalty Pair Neighbors are selected based on the node mobility, direction and angle and power consumption. For
the optimal selection of loyalty pair neighbours, the ELPNS_Cuckoo is employed. Here the fitness function Fi is
calculated based on hop count, queue length and the energy level. The node with less hopcount, high energy and
more queue length is considered as the best node.
IV. Simulation Analysis
In this section, the performance of the proposed ELPNS_Cuckoo protocol is evaluated and compared with the
LPNS protocol. The evaluations are made using NS-2 simulator. The performance comparisons are made in terms of
delay, residual energy, jitter, dropping ratio and throughput. The simulation settings are shown in Table 1.
Table 1: Simulation Environment
Simulation Parameter Value
Simulator NS-2 34
Topology size 500 m X 500 m
Number of Nodes 50,60,70,80,90,100
Mobility Random way point
Transmission range 250 m
Bandwidth 2 Mbps
Interface queue length 50
Traffic type CBR
Number of CBR Traffic 2,4,6,8,10
Packet Size 512 bytes
Node speed 1,2,3,4,5
Fig. 1: Number of Nodes Vs Throughput
Throughput represents the total number of messages delivered successfully per unit time. From the Fig(1), we
can clearly conclude that as the node increases,the throughput of ELPNS_Cuckoo is more than the LPNS.
Fig. 2: Number of Nodes Vs Jitter
25000
30000
35000
40000
45000
50000
55000
50 60 70 80 90
Throughput
Nodes
Throughput_LPNS
Throughput_ELPNS_cuckoo
0.07
0.09
0.11
0.13
0.15
0.17
0.19
50 60 70 80 90
Jitter
Nodes
Jitter_ELPNS_cuckoo Jitter_LPNS
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4. Jour of Adv Research in Dynamical & Control Systems, 12-Special Issue, August 2017
Jitter is nothing but the variation in latency of the packets flow between two ends. The simulation result in Fig(2)
clearly shows that the jitter value of ELPNS_Cuckoo is low when compared to LPNS.
Fig. 3: Number of Nodes Vs Residual_Energy
From the above simulation result we can clearly conclude that the residual energy of ELPNS_Cuckoo is high
when compared to LPNS.
Fig. 4: Number of Nodes Vs Dropping Ratio
From the Fig(4), we can observe that the dropping ratio is much reduced in proposed ELPNS_Cuckoo than
LPNS.
Fig. 5: Number of Nodes Vs Delay
The graph in the fig(5) shows the variation in delay. LPNS consistently shows highest delay than
ELPNS_Cuckoo.
99.4
99.5
99.6
99.7
99.8
99.9
100
50 60 70 80 90
Average_Residual_Energy
Nodes
Res_Energy_ELPNS_cuckoo
Res_Energy_LPNS
0
5
10
15
20
25
50 60 70 80 90 100
Dropping_Ratio
Nodes
Dropping_Ratio_ELPNS_Cuckoo
Dropping_Ratio_LPNS
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
60 70 80 90 100
Delay(s)
Nodes
Delay_ELPNS_Cuckoo
Delay_LPNS
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5. Jour of Adv Research in Dynamical & Control Systems, 12-Special Issue, August 2017
V. Conclusion and Future Work
In proposed ELPNS_Cuckoo, LPNS based Adaptive Re-transmission Reduction Routing is enhanced by
optimizing the neighbor node selection using cuckoo search. Here, the network path delay and path retransmission
information are only considered and the energy minimization or power enhancement strategies are not considered.
Though efficient performance has been achieved, it can be further improved by moving the unused nodes to sleep
mode for achieving routing with minimum power consumption and better topology control.
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