MPR selection to the OLSR quality of service in MANET using minmax algorithm

Alamsyah, Alamsyah; Purnama, I Ketut Eddy; Setijadi, Eko; Purnomo, Mauridhi Hery

doi:10.11591/ijece.v9i1.pp417-425

Cited by 12 publications

(7 citation statements)

References 28 publications

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“…The Simulations were conducted using NS 2.35 [20,21], under different mobility and density scenarios, where we varied node velocity and node density. Nodes in the simulation move according to Random Waypoint Mobility model each node is then assigned a particular trajectory, and the "two ray ground propagation model" is used [22]. We select the number of network nodes to be 60 in an area of 870*870 square meters while changing the speed.…”

Section: Methodsmentioning

confidence: 99%

LE-OLSR protocol performance evaluation in various energy conditionsof mobile Ad-hoc and sensor wireless networks

Belkheir¹,

Rouissat

Mokaddem

et al. 2020

IJEECS

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<p>A mobile Ad hoc and sensor wireless network commonly includes various mobile devices and sensors with limited resources. In such network, energy constraint and link stability are the most critical parameters to take into consideration when deploying routing mechanisms, to assure reliable communication between nodes.These requirements are due to the network scalability and the absence of a fixed infrastructure, which may cause a rapid depletion of the nodes batteries and leads onto network link breakages. LE-OLSR (Lifetime Enhancement - Optimized Link State Protocol) is a variant of the existing OLSR. This latter is widely used as routing protocol for mobile ad-hoc and wireless sensor networks. LE-OLSR implements a new load balancing algorithm; it takes into account energy constraints of a node when selecting routers, responsible for forwarding data packets over the entire network. The aim of our work is to study and simulate the LE-OLSR protocol behavior in realistic mobile environments, including scenarios with various residual energy constraints schemes. The performed simulations had shown that LE-OLSR performances decrease compared to the case of ideal mobile network. Nevertheless, LE-OLSR had shown persistence and fair results in the realistic environment.The obtained results show that LE-OLSR protocol is an adequate and a prime solution for dense mobile ad hoc networks.</p>

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Section: Methodsmentioning

confidence: 99%

LE-OLSR protocol performance evaluation in various energy conditionsof mobile Ad-hoc and sensor wireless networks

Belkheir¹,

Rouissat

Mokaddem

et al. 2020

IJEECS

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show abstract

“…In order to improve both quality and security in selection of MPRs the researchers used several techniques. There are those who treated just the energetic side of the nodes [3]- [7] others treated the effect of mobility [8]- [12]. To integrate more parameters both in the calculation of MPRs and in order to improve the functioning of mobile ad hoc network (MANET) networks precisely of the OLSR protocol, the researchers used techniques (genetic algorithms and neural networks) making it possible to process and combine several criteria at the same time, but, they still remain less efficient because they only integrate some parameters and not all.…”

Section: Introductionmentioning

confidence: 99%

New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM

Barki

Guennoun²,

Addaim³

2023

IJ-AI

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In order to improve the selection of multi-point relays (MPRs) by a node node performing the computation (NPC) in the optimized link state routing (OLSR) protocol and therefore to guarantee more security for the routing in the mobile ad hoc network (MANET), we propose new approach that could distinguish between the strong and weak MPRs in the list of MPRs already selected using the standard algorithm described in RFC3626 document. This approach is based on self organizing map (SOM) artificial neural network that processes the collected data and then only selects the strong MPRs using a set of criteria allowing a reliable retransmission and a strong link and therefore better network performances. The obtained results, from the simulations that have been carried out using a customized network simulator 3 (NS3) network simulator, show an improvement in terms of throughput, packets delivery ratio (PDR) and the security of the network compared to the standard approach.

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“…The OLSR protocol significantly classifies the qualitative and quantitative properties, where the qualitative properties include "distributed operation, loop freedom, unidirectional link support, security, and demand-based operation". The OLSR is the trivial protocol, which provides the link-state information in the MANET [11,12]. In this manuscript, the soft computing model named ANN is used to improve the security of the MANET.…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

2022

IJIES

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Mobile Ad hoc Network (MANET) includes many wireless mobile nodes, and higher energy consumption is the major concern in MANET. Compared to other protocols, the Optimized Link State Routing Protocol (OLSR) is a superior pro-active protocol, which works on limited configuration attribute variables in the MANET. In a dynamic environment, default variables of the attribute are not suitable as per the IETF draft. Therefore, the Artificial Neural Network (ANN) is integrated with the OLSR protocol in this manuscript. In addition, the OPNET modeler is used to evaluate the performance of ANN based OLSR protocol, which is simulated by utilizing the MATLAB software environment. The performance measures such as Routing Overhead Ratio (ROR), End to End Delay (EED), Energy Consumption (EC), and Packet Delivery Ratio (PDR) are employed for analysing the protocol's performance. The simulation result revealed that the ANN based OLSR protocol effectively increases the network lifespan and reduces EC of the MANET network. Compared to the Ant Colony Optimization (ACO) based Modified Ad-hoc On Demand Distance Vector (MAODV) and Prevention of Selfish Node using Hash Function (PSNHF) based AODV protocols, the proposed ANN based OLSR protocol achieved higher PDR of 99.92%, 99.90%, and 99.89% on the nodes of 80, 90 and 100.

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MPR selection to the OLSR quality of service in MANET using minmax algorithm

Cited by 12 publications

References 28 publications

LE-OLSR protocol performance evaluation in various energy conditionsof mobile Ad-hoc and sensor wireless networks

LE-OLSR protocol performance evaluation in various energy conditionsof mobile Ad-hoc and sensor wireless networks

New approach for selecting multi-point relays in the optimized link state routing protocol using self-organizing map artificial neural network: OLSR-SOM

Artificial Neural Network based Optimized Link State Routing Protocol in MANET

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