QoS multicast routing for wireless mesh network based on a modified binary bat algorithm

Meraihi, Yassine; Acheli, Dalila; Ramdane-Chérif, Amar

doi:10.1007/s00521-017-3252-9

Cited by 17 publications

(5 citation statements)

References 37 publications

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“…Step 7. Compute the final output matrix D t+1 , to obtain the orthogonal projection with the next iteration at time 't + 1 as follows in Equation ( 10), D t+1 = D P n j + P n j − D P n j (10) where P n j is the standard packet delivery ratio of current upstream neighbor 'n j ' and that can be computed as follows in Equation (11).…”

Section: R =mentioning

confidence: 99%

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PDSCM: Packet Delivery Assured Secure Channel Selection for Multicast Routing in Wireless Mesh Networks

Daniel,

Durga

et al. 2023

Technologies

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The academic and research communities are showing significant interest in the modern and highly promising technology of wireless mesh networks (WMNs) due to their low-cost deployment, self-configuration, self-organization, robustness, scalability, and reliable service coverage. Multicasting is a broadcast technique in which the communication is started by an individual user and is shared by one or multiple groups of destinations concurrently as one-to-many allotments. The multicasting protocols are focused on building accurate paths with proper channel optimization techniques. The forwarder nodes of the multicast protocol may behave with certain malicious characteristics, such as dropping packets, and delayed transmissions that cause heavy packet loss in the network. This leads to a reduced packet delivery ratio and throughput of the network. Hence, the forwarder node validation is critical for building a secure network. This research paper presents a secure forwarder selection between a sender and the batch of receivers by utilizing the node’s communication behavior. The parameters of the malicious nodes are analyzed using orthogonal projection and statistical methods to distinguish malicious node behaviors from normal node behaviors based on node actions. The protocol then validates the malicious behaviors and subsequently eliminates them from the forwarder selection process using secure path finding strategies, which lead to dynamic and scalable multicast mesh networks for communication.

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Section: R =mentioning

confidence: 99%

“…Most of the existing multicast routing protocols [9][10][11] are focused on improving the quality-of-service requirements to achieve high throughput, minimize delay, minimize congestion, minimize the cost, etc. On the other hand, addressing security issues in a multicast environment is also important.…”

Section: Introductionmentioning

confidence: 99%

PDSCM: Packet Delivery Assured Secure Channel Selection for Multicast Routing in Wireless Mesh Networks

Daniel,

Durga

et al. 2023

Technologies

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“…By introducing inertia weight into the velocity update equation (Meraihi et al. 2019 ), binary bat algorithm (BA) is improved, and then a appropriate value is selected through chaotic map, uniform distribution and Gaussian distribution. The algorithm meets the constraints of multiple QoS such as bandwidth, delay and packet loss rate, and obtains a low-cost multi-cast tree.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

“…( 2017 ) Memetic Algorithm Broadcast Scheduling in SDN-Based Vehicular Networks Low Low High Meraihi et al. ( 2019 ) Binary BA QoS Multi-cast Routing Medium High High Shen et al. ( 2014 ) Discrete PSO Multi-cast Routing Medium Medium High Panwar et al.…”

Section: Binary Metaheuristic Algorithms In Applicationsmentioning

confidence: 99%

A survey on binary metaheuristic algorithms and their engineering applications

Pan

Snåšel

et al. 2022

Artif Intell Rev

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This article presents a comprehensively state-of-the-art investigation of the engineering applications utilized by binary metaheuristic algorithms. Surveyed work is categorized based on application scenarios and solution encoding, and describes these algorithms in detail to help researchers choose appropriate methods to solve related applications. It is seen that transfer function is the main binary coding of metaheuristic algorithms, which usually adopts Sigmoid function. Among the contributions presented, there were different implementations and applications of metaheuristic algorithms, or the study of engineering applications by different objective functions such as the single- and multi-objective problems of feature selection, scheduling, layout and engineering structure optimization. The article identifies current troubles and challenges by the conducted review, and discusses that novel binary algorithm, transfer function, benchmark function, time-consuming problem and application integration are need to be resolved in future.

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“…Therefore, each satellite has 0-2 interorbit links. The expression of the link length can be divided into two cases where the orbital phase factor is equal to 0, that is, the interorbit link is parallel to the equator, and the orbital phase factor is not equal to 0, that is, the relative equator is inclined [15,16], as shown in the following formula:…”

Section: ) Interorbital Intersatellite Linkmentioning

confidence: 99%

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

Liu

Sun

Zhang

2021

Wireless Communications and Mobile Computing

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Satellite communication has become an important research trend in the field of communication technology. Low-orbit satellites have always been the focus of extensive attention by scholars due to their wide coverage, strong flexibility, and freedom from geographical constraints. This article introduces some technologies about low-orbit satellites and introduces a routing algorithm DDPG based on machine learning for simulation experiments. The performance of this algorithm is compared with the performance of three commonly used low-orbit satellite routing algorithms, and a conclusion is drawn. The routing algorithm based on machine learning has the smallest average delay, and the average value is 126 ms under different weights. Its packet loss rate is the smallest, with an average of 2.9%. Its throughput is the largest, with an average of 201.7 Mbps; its load distribution index is the smallest, with an average of 0.54. In summary, the performance of routing algorithms based on machine learning is better than general algorithms.

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QoS multicast routing for wireless mesh network based on a modified binary bat algorithm

Cited by 17 publications

References 37 publications

PDSCM: Packet Delivery Assured Secure Channel Selection for Multicast Routing in Wireless Mesh Networks

PDSCM: Packet Delivery Assured Secure Channel Selection for Multicast Routing in Wireless Mesh Networks

A survey on binary metaheuristic algorithms and their engineering applications

Load Balancing Routing Algorithm of Low‐Orbit Communication Satellite Network Traffic Based on Machine Learning

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