QoS Improvement in MANET Using Particle Swarm Optimization Algorithm

Trivedi, Munesh Chandra; Sharma, Anupam

doi:10.1007/978-981-10-0755-2_20

Cited by 11 publications

(4 citation statements)

References 12 publications

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“…PSO method is used for enhancing the lifespan. This method provides better function and minimizes energy utilization [Trivedi and Sharma, 2016]. Link lifetime prediction approach using PSO technique for recover the route in the network.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Efficient Clustering and Improve Quality of Service in Manet

Reddy¹,

Mungara²

2021

INDJCSE

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A Mobile Ad hoc Network (MANET) is a self-governing system in that numerous nodes are connected with each other, applying multi-hop wireless links. MANET has overturned society owing to their infrastructure-less decentralized modes of transaction. As a result, researchers have concentrated on discovering better directions to operate the probability of MANETs completely. The current initiation of machine learning methods has prepared it to concern artificial intelligence to increase the best approaches for this function. In this work, Machine Learning-based Efficient Clustering and Improve Quality of Service in MANET (MLEC) is proposed. In this work, we improve the clustering technique that applied Quality of Service (QoS) parameters to choose Cluster Heads (CHs). The cluster formation and CH selection is accessible via a multi-objective fitness function applying Particle Swarm Optimization (PSO) algorithm. This approach gives three inputs: node mobility, energy utilization rate, and bandwidth. The algorithm establishes a higher rate of achievement in forecasting output values also could correctly recognize better CH that should offer the well-organized routing. In addition, it reduces the intra-cluster distance among nodes and their relevant CHs. The proposed work is experimented with broadly in the network simulator and compared with the other baseline protocols.

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Section: Related Workmentioning

confidence: 99%

Machine Learning-Based Efficient Clustering and Improve Quality of Service in Manet

Reddy¹,

Mungara²

2021

INDJCSE

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“…). During the movement, each particle updates its position and velocity according to its own experience and that of its neighbors [13]. The best previous position of the particle is recorded as the personal best , and the best position obtained by the population thus far is called…”

Section: B Ch Selection Using Mpso Algorithmmentioning

confidence: 99%

Efficient Cluster Head Selection Using Modified Particle Swarm Optimization over MANET

Kumar¹

2019

IJRASET

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A Mobile Ad-Hoc NETwork (MANET) is a wireless network, collection of mobile nodes that are dynamically and arbitrarily positioned in such a way that the contact among each other. These nodes do not make a fixed configuration since the nodes can go in any direction. These MANET nodes can converse along with other nodes in the radio range. In the existing system, Genetic Algorithm (GA) and Ad hoc On-Demand Distance Vector (AODV) routing approaches are introduced for energy efficiency. However these methods have issue with best Cluster Head (CH) node selection in larger network. Thus the overall performance is reduced significantly. To avoid the above mentioned drawbacks, in the proposed system, Modified Particle Swarm Optimization (MPSO) and Dynamic Source Routing (DSR) protocol is proposed. The proposed method contains three phases such as system model, CH selection using MPSO and routing. In the system model, nodes are connected to send and receive the packets. Energy model and mobility model are constructed to give efficient packet transmission over MANET. In second phase, the MPSO algorithm is focused to select the best CH node using fitness function. It is used to calculate the energy utilization through distance among nodes. In the third phase, the DSR protocol is used to produce efficient routing path over MANET. Thus the simulation results conclude that the proposed MPSO+DSR algorithm is better than the existing GA and AODV in terms of lower energy consumption, higher throughput, lower end to end delay, higher packet delivery ratio and higher network lifetime Keywords: MANET, Genetic Algorithm (GA), Cluster Head (CH), Modified Particle Swarm Optimization (MPSO), Dynamic Source Routing (DSR) and routing path I. INTRODUCTION MANETs is an infrastructure-less, active system encloses a set of wireless movable nodes that communicate along with each other without the use of any centralized authority. Every node in a MANET is without charge to move freely in any route, and hence modify its links to other nodes often [1] [2]. In a MANET, every node has its own radio broadcast range. Two nodes can have communiqué with every other openly only if they are in the similar radio communication series. If two hubs are not in the similar radio transmission range, they can still transmit but using the intermediate nodes. Every time the nodes in the MANET are concerned in transmission, battery energy of those nodes will get reduced. The issue of power utilization in MANETs can be identified in various levels. Recently, several researchers aimed on the optimization of power efficiency of mobile nodes, from diverse of viewpoint. The goal of power responsive routing method is to decrease power consumption in packets broadcast among a source and a destination, to prevent packets routing via nodes with low residual energy. It is used to optimize flooding of routing data on the given system and to neglect interfering and intermediate conflicts. Power resourceful routing algorithms achieved via creating proper routing approaches that...

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“…The extra processing and communication overhead is added by the scheme in order to form and sustain the hierarchical structure. An algorithm in [12] is based on PSO which improves QoS metrics such as end to end delay, NRL and PDR of AODV protocol. Energy aware multiple paths routing scheme in [13] is proposed in view of PSO which ascertain the best route to decrease the directing overhead which upgrades the reliability of the system in terms of transmission cost, energy and traffic ratio.…”

Section: Related Workmentioning

confidence: 99%

Security Improvement of AODV Routing Protocol through IPSO-IDRS Mechanism for Ad-hoc Networks

Dixit*¹,

Singhai²

2019

IJITEE

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Ad-hoc network is vulnerable to different types of attacks because of its vigorously changing topology, limited storage capacity, absence of centralized infrastructure etc. Adhoc On Demand Distance Vector (AODV) is established protocol for routing for this type of networks. AODV is exposed to black hole attack due to the inadequacy of security consideration. The malicious nodes drop all information packets rather than sending it to the neighboring node. In this research paper, Anomaly based intrusion detection system (IDS) known as Particle swarm optimization (PSO) is purposefully utilized to detect the misbehavior activities in the network. In AODV routing protocol, swarm agents are used for identifying misbehavior activities of nodes based on dropping behavior of data packets. An intrusion response system (IRS) is activated after the detection of an intrusion. It is necessary to take action against black hole attack or reduce the effect of the damage caused by the attack. IDS and a response system are integrated for detection and removal of the source of an attack respectively. The guard nodes are placed in the network with the aim to oppose black hole attackers and in this way IR is initiated. The malevolent nodes detected by PSO are bypassed and new routing paths are established using guard nodes. This research has been carried out for analyzing the influence of malicious nodes and guard nodes on varying network size vice versa. The simulation study of proposed technique integrated particle swarm optimization intrusion detection response system (IPSO-IDRS) explains how it is better in terms of the performance metric like throughput and PDR.

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QoS Improvement in MANET Using Particle Swarm Optimization Algorithm

Cited by 11 publications

References 12 publications

Machine Learning-Based Efficient Clustering and Improve Quality of Service in Manet

Machine Learning-Based Efficient Clustering and Improve Quality of Service in Manet

Efficient Cluster Head Selection Using Modified Particle Swarm Optimization over MANET

Security Improvement of AODV Routing Protocol through IPSO-IDRS Mechanism for Ad-hoc Networks

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