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REVIEW OF ROUTING ALGORITHM FOR MANET BASED ON OPTIMIZATION TECHNIQUES ALBERT PAULIN MICHAEL R1, Dr.S.MANIKANDAN2 1Ph.D Research Scholar, Department of Computer Science, Karuppanan Mariappan College, Muthur - 638105. 2Assistant Professor, Department of Computer Science, Karuppanan Mariappan College, Muthur - 638105.
-----------------------------------------------------------------***-----------------------------------------------------------------inspired algorithms is the category of algorithms that Abstract: Mobile Ad-hoc Network is very broadly used in many applications. A MANET is an autonomous collection of mobile users that communicate over relatively bandwidth constrained wireless links. Since the nodes are mobile, the network topology may change rapidly and unpredictably over time. Sensor networks still have problems in conveying data from one sensor node to other node. Routing algorithms are developed for wireless sensor networks. MANETs are mostly used for communication purposes and to transfer data from one device to another device through internet. But both are different in many points. Nodes used in WSNs have very limited memory power and are of very high magnitude i.e. in order of many hundreds, as compared to MANETs. For WSNs communication is not very big issue, but collecting data is more important, while in MANETs communication is the only purpose. Routing efficiency is to management entire network energy. Optimization technique is most important research area in networking. In this proposal we have survey with various routing algorithm for MANET based on optimization algorithm, Like ACO, LOA, BOA, WOA, ISRA. Keywords: Network, MANET, Optimization Technique, Sensor Node, Routing Algorithm.
I.INTRODUCTION Most of the research in MANETS has been focused on Routing issues. Security on the other hand has been given low priority. This paper provides an introduction to Mobile Ad-hoc Networks, Routing related issues and overview of security problems for MANETS, by distinguishing the threats on the basic mechanisms and security mechanisms. It then addresses the possible solution to protect the security mechanism, which involves availability, integrity, authentication and non repudiation. These securities related issues are well addressed if one can provide methods that are pertinent for authentication, key distribution, and intrusion detection and rerouting in case of Byzantine failure in MANETS.
resemble with the performance of nature. By these algorithms various problems can be solved due to their advantages: First, to reach a solution, we don’t have to follow any mathematical approaches. Second, results come very fast and are accurate. Optimization approaches which fall into the category of these algorithms are:
Lion Optimization Algorithm Particle Swarm Intelligence Ant Colony Optimization Whale optimization algorithm Artificial Bee Colony Optimization Artificial Neural Networks Bacterial Foraging Algorithm
Various optimization techniques can be used to find out the best and optimal solution. Nature inspired algorithms are meta heuristics that mimics the nature for solving optimization problems opening a new era in computation.
II. RELATED WORK In [2], Ant Colony Optimization (ACO) algorithm which is inspired by the nature. In MANETs, the routing algorithms are developed easily by this technique. In this technique, the autonomous agents interact with each other and their collective behaviors studied to reach a given solution by finding the best global solution. Algorithm based on energy efficient routing is proposed for MANETs to extend the lifetime of overall system by minimizing energy consumption of nodes.
Various Optimization approaches can be used to find the optimal and best solution. For optimization biologically
The performance of FCM is enhanced by several optimization algorithms, so as to support the FCM from convergence at local minima. In [3], the data clustering is performed by combining FCM and invasive weed optimization algorithm. The investigations on bio-inspired algorithms are unstoppable and certain combinations of algorithm prove better performance. Ant Lion Optimization algorithm describe the hunting mechanism of ant lions in nature. Five main steps hunting prey such as the random walk of ants, building traps, entrapment of ants in traps,
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International Research Journal of Engineering and Technology (IRJET)
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catching preys, and re-building traps are implemented. It can be concluded that the proposed algorithm benefits from high exploitation and convergence rate. ALO can be concluded from the results of multimodal and composite test functions which is due the employed random walk and roulette wheel selection mechanisms [4]. The collective and social behavior of living creatures motivated researchers to undertake the study is Swarm Intelligence. Particle Swarm Optimization (PSO) is initially introduce for simulating human social behaviors [5]. WOA is an optimization algorithm that imitates the whales [6]. By nature, the whales attack a shoal of fish that swim around the whale in the scope of a circle by forming bubbles. The shoal of fish is enclosed by the bubbles and the attack is performed in two phases. They are exploitation and exploration phases. The exploitation phase is meant for enclosing the prey within a circle and the exploration phase searches the fish in a random fashion.In [7], this paper proposes an artificial neural network optimization technique which is used in reactive AODV routing protocol. In this the frequency of the Hello interval between two events is determined to check the network performance. Unnecessary traffic may occur due to updating information at some time interval. So to improve the performance of the network, we have to make the time interval of these messages to be adaptive. Information update at some fixed time interval can cause unnecessary traffic in the wireless network so to make adaptive the time interval of these messages is a technique to improve the performance of the network.
III. PROPOSED OPTIMIZATION ALGORITHMS FOR MANET MANETs are mostly used for communication purposes and to transfer data from one device to another device through internet. But both are different in many points. Nodes used in WSNs have very limited memory power and are of very high magnitude i.e. in order of many hundreds, as compared to MANETs. For WSNs communication is not very big issue, but collecting data is more important, while in MANETs communication is the only purpose.
a random fashion. The behaviour of whales is modelled as follows.
(
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)
⃗⃗⃗⃗ ( )
( )|
⃗⃗⃗⃗⃗⃗⃗
In the above equations, i denotes the current iteration, A^* is the best result attained and the position vector is represented by A. The absolute value is represented by || and dot product (∙) is performed for all elements. CV_1 and CV_2 are the coefficient vectors, which are calculated as follows. Ant Colony Optimization ACO is the Meta heuristic technique which is inspired by foraging behavior of ants. This optimization technique was proposed by Dorgio and Dicario in 1999. In this three main functions are structured:
Ant Solution Construct: In this artificial ants move through adjacent states of problem. Pheromone Update: Once the solution is built completely, pheromone trails are updated. Daemon actions: In this additional pheromone is applied to the best solution.
Particle Swarm Optimization PSO is a population based stochastic optimization technique and was proposed by Kennedy and Eberhart in 1995.This technique is inspired by group behavior of bird flocking and fish swimming. In PSO, each member is represented by particle having velocity and position of each of them. The particle’s best position is evaluated by the highest fitness value . Various steps involved in PSO algorithm are as:
Whale optimization algorithm The whales attack a shoal of fish that swim around the whale in the scope of a circle by forming bubbles. The shoal of fish is enclosed by the bubbles and the attack is performed in two phases. They are exploitation and exploration phases. The exploitation phase is meant for enclosing the prey within a circle and the exploration phase searches the fish in
⃗⃗⃗⃗ ( )
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Initialize the particle in a given search space. Evaluate the performance of each particle. Compare the particle’s fitness value with pbest. If the value of particle is better than pbest then set this value as pbest Update the position and velocity of particles.
Artificial Bee Colony Optimization (ABC) Based on the behaviour of the bees in nature, various swarm intelligence algorithms are available. This algorithm is based
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on the foraging behaviour of honeybee swarm and was proposed by Basturk and Karaboga. These algorithms are classified into two; foraging behaviour and mating behavior. In ABC algorithm there are mainly three groups of bees: Onlookers, Employed, Scouts.
IV. COMPARISON OF OPTIMIZATION ALGORITHMS In this paper different routing protocols, which are developed for wireless sensor networks. an idea for developing a new protocol for sensor networks with the help of other routing protocols, which were basically developed for ad-hoc network. Following table describe the network specification. Table:1 Simulation Parameter Parameters
Specifications
Network Size
300 × 300
Number of Nodes
10/50/100/
Communication Range
50 to 200
Speed
512
Simulation Period
100 PS
Node moving Range
50-100
The above analysis describes the new proposal results compared with previous works. The results is found that the PSO approach prove better than exist approach. Packet Delivery Rate of work is measured. On that analysis PSO has better packet rate start to diminish as the count of nodes increased. The average latency with 13 seconds for node 100 and prove best performance 100 90 80 70 60 50 40 30 20 10 0
WOA ACO ABC PSO
PDR (%)(%)Energy (%) Network Efficient life(%) Time (%) AverageThroughput Latency
Fig: 1 Performance Analysis
V.CONCLUSION Optimization is the basic criteria for finding optimal and best solution from the possible outcomes. There are various optimization techniques available and could be applied accordingly to give the better results. Main focus is on optimization approach which makes the network more reliable, efficient and without any loss of original link during data transmission. These optimization schemes should be used according to the scenario.
Table 2. Performance analysis of the proposed approaches Average Throughput Energy PDR Latency Efficient (%) (%) (%) (%)
Network life Time (%)
WOA
65.5
62.3
72.5
56.3
90.8
ACO
68.4
58.1
76.4
54.0
93.2
ABC
70.2
52.6
79.2
50.3
94.6
PSO
78.6
50.1
82.4
48.4
96.3
REFERENCE [1] David et. Al. Maltz, Yih-Chun Hu and Jorjeta G. Jetcheva, “The Dynamic Source Routing forMobile Ad Hoc Wireless Networks, “ July 2004. [2] X. Zhu, W. Pedrycz, and Z. Li, "Fuzzy clustering with nonlinearly transformed data", Applied Soft Computing, Vol. 61, pp. 364-376, 2017. [3] D. Karaboga and C. Ozturk, “Fuzzy clustering with artificial bee colony algorithm”, Scientific Research and Essays, Vol.5, No.14, pp.1899–1902, 2010 [4] Kayvan Azaryuon, Babak Fakhar, ” A novel Document Clustering Algorithm Based on Ant Colony Optimization algorithm”, vol(7), JMCS, 2013
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[5] Sagar Tiwari et al ,”Algorithm of Swarm Intelligence using Data Clustering”, Vol4($), 2013, IJCSIT. [6] S. Mirjalili , A. Lewis , The whale optimization algorithm, Adv. Eng. Softw. 95 (2016) 51–67. [7] Vishwakarma, D, and , Shah, S.K “Development and Simulation of Artificial Neural Network Based Decision on Parametric Values for Performance Optimization of Reactive Routing Protocol for MANET” International Conference onComputational Intelligence and Communication Networks (CICN), 2010 [8]
Li, Z., & Wu, Y. (2017). Smooth mobility and link reliability-based optimized link state routing scheme for MANETs. IEEE Communications Letters, 21(7), 1529-1532.
[9] K.L.Shunmuganathan., & J.Visumathi., “ An Effective IDS for MANET using Forward Feature Selection and Classification algorithem”, Procedja Engineering Vol 38, pp:2816- 2823, 2012. [10] Bing Zeng Yen Dong., “An Improved harmony Search Based Energy – Efficient Routing Algorithm for Wireless Sensor Networks”, Applied Soft Computing, Nov 25, 2015.
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