Impact of mobility models on Optimized Link State Routing Protocol in MANET

Laqtib, Safaa; Yassini, Khalid El; Houmer, Meriem; Ouadghiri, Moulay Driss El; Hasnaoui, Moulay Lahcen

doi:10.1109/wincom.2016.7777199

Cited by 10 publications

(6 citation statements)

References 4 publications

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“…In this distribution mobility model, the nodes in the network are randomly distributed. First, each node will select a target location with a random speed then the node will continue to move to another location target with another random speed [24], [25]. The simulation is carried out in two scenarios, the first scenario is the addition of variations in the speed of node movement and the second scenario is the addition of the network area.…”

Section: Resultsmentioning

confidence: 99%

Energy consumption analysis of DSR reactive routing protocol on mobile ad-hoc network

Nurfitriana¹,

Ginting²,

Nugroho³

2023

J.INFOTEL

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Mobile ad-hoc network is a connection between mobile devices that uses wireless media. Mobile devices on the network hereinafter referred to a nodes. This network does not have an administrative center so each node on the network in addition to functioning as a sender and receiver of data information also functions as a router that will look for route information from the sender to the receiver. The topology of an ad-hoc network is always changing because the nodes move dynamically. The topology changes resulted in the repetition of route information searches. The process of finding route information requires a routing protocol. The routing protocol-enabled nodes must maintain the energy usage in the route-finding mechanism. Choosing the right routing protocol can be a solution to make energy use by nodes more efficient, especially in ad-hoc networks. In this study, a routing protocol in the reactive category is used, namely DSR (Dynamic Source Routing). This study aims to determine the performance of energy consumption, remaining energy, and PDR with scenarios of increasing node movement speed and network area. Based on the research results, it is known that the DSR routing protocol can handle changes in the speed of node movement and network area related to energy consumption and remaining energy. This is evidenced by the results of research showing that with faster node movements and wider areas, less energy is required. Meanwhile, regarding the success of packet delivery, the DSR routing protocol cannot handle changes in the speed of node movement and network area. This is evidenced by the results of the packet delivery ratio measurement which shows that with faster node movements and wider areas, many packets are not successfully received.

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Section: Resultsmentioning

confidence: 99%

Energy consumption analysis of DSR reactive routing protocol on mobile ad-hoc network

Nurfitriana¹,

Ginting²,

Nugroho³

2023

J.INFOTEL

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“…These nodes will set their speed (S t ) and direction (D t ) for each specific time interval (t). The speed and direction of a node are calculated as follows [28]:…”

Section: Gauss-markov Mobility Modelmentioning

confidence: 99%

“…The performance of mobility models in mobile ad hoc networks (MANETs) has also been investigated [28,[36][37][38]. For instance, Abdullah et al [37] evaluated the RWP model on various routing protocols: ad hoc on-demand distance vector (AODV), optimized link state routing (OLSR), and Geographic Routing Protocol (GRP).…”

Section: Related Workmentioning

confidence: 99%

“…He showed that choosing a suitable speed and pausing time for the RWP mobility model could lead to better performance than MDL. In another performance study, Laqtib et al [28] investigated the performance of the OLSR under four mobility models: RWP, RD, RW, and GM. The study concluded that the RWP model gives optimal performance results with respect to delay, packet retransmission, and throughput, regardless of the number of mobile nodes in the network.…”

Section: Related Workmentioning

confidence: 99%

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RSSGM: Recurrent Self-Similar Gauss–Markov Mobility Model

et al. 2020

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Understanding node mobility is critical for the proper simulation of mobile devices in a wireless network. However, current mobility models often do not reflect the realistic movements of users within their environments. They also do not provide the freedom to adjust their degrees of randomness or adequately mimic human movements by injecting possible crossing points and adding recurrent patterns. In this paper, we propose the recurrent self-similar Gauss–Markov mobility (RSSGM) model, a novel mobility model that is suitable for applications in which nodes exhibit recurrent visits to selected locations with semi-similar routes. Examples of such applications include daily human routines, airplane and public transportation routes, and intra-campus student walks. First, we present the proposed algorithm and its assumptions, and then we study its behavior in different scenarios. The study’s results show that different and more realistic mobility traces can be achieved without the need for complex computational models or existing GPS records. Our model can flexibly adjust its behavior to fit any application by carefully tuning and choosing the right values for its parameters.

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“…Because each packet must be passed through intermediate nodes quickly, that packet has to travel from the source to the destination. Malicious routing attacks may target the routing detection or maintenance process by failing to follow the specifications of the routing protocol [2,3]. This increases the possibility of attacks such as eavesdropping, spoofing, denial of service, and impersonation.…”

Section: Introductionmentioning

confidence: 99%

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

Laqtib

Yassini

Hasnaoui

2020

IJECE

Self Cite

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Machine learning techniques are being widely used to develop an intrusion detection system (IDS) for detecting and classifying cyber attacks at the network-level and the host-level in a timely and automatic manner. However, Traditional Intrusion Detection Systems (IDS), based on traditional machine learning methods, lacks reliability and accuracy. Instead of the traditional machine learning used in previous researches, we think deep learning has the potential to perform better in extracting features of massive data considering the massive cyber traffic in real life. Generally Mobile Ad Hoc Networks have given the low physical security for mobile devices, because of the properties such as node mobility, lack of centralized management and limited bandwidth. To tackle these security issues, traditional cryptography schemes can-not completely safeguard MANETs in terms of novel threats and vulnerabilities, thus by applying Deep learning methods techniques in IDS are capable of adapting the dynamic environments of MANETs and enables the system to make decisions on intrusion while continuing to learn about their mobile environment. An IDS in MANET is a sensoring mechanism that monitors nodes and network activities in order to detect malicious actions and malicious attempt performed by Intruders. Recently, multiple deep learning approaches have been proposed to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of three models, Inceprtion architecture convolutional neural network Inception-CNN, Bidirectional long short-term memory (BLSTM) and deep belief network (DBN) on the deep learning-based intrusion detection systems, using the NSL-KDD dataset containing information about intrusion and regular network connections, the goal is to provide basic guidance on the choice of deep learning methods in MANET.

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Impact of mobility models on Optimized Link State Routing Protocol in MANET

Cited by 10 publications

References 4 publications

Energy consumption analysis of DSR reactive routing protocol on mobile ad-hoc network

Energy consumption analysis of DSR reactive routing protocol on mobile ad-hoc network

RSSGM: Recurrent Self-Similar Gauss–Markov Mobility Model

A technical review and comparative analysis of machine learning techniques for intrusion detection systems in MANET

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