Firefly Algorithm in Telecommunications

Adaniya, Mario H. A. C.; Carvalho, Luiz F.; Zarpelão, Bruno Bogaz; Sampaio, Lucas Dias Hiera; Abrão, Taufik; Jeszensky, Paul Jean Etienne; Proença, Mário Lemes

doi:10.1016/b978-0-12-801538-4.00003-3

Cited by 3 publications

(2 citation statements)

References 32 publications

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“…Several methods are proposed for the formation of user pairs such as round robin [28]. As these approaches tend to require significantly higher computational loads when the number of users increases, new computationally lower schemes have been proposed in literature to determine the user pairs such as the heuristic models which are inspired by artificial intelligence approaches [29][30][31][32][33][34] which include drosophila optimization algorithm [35], particle swarm optimization algorithm [36][37][38], firefly optimization algorithm [39], dolphin echolocation algorithm [40], genetic algorithm [20,41] and ant-colony optimization algorithm [19,42]. These models have the ability to solve problems in varying fields such as, but not limited to, transportation, signal processing, image processing and biomedical engineering.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The negative point of this method takes a long time for converging and has a low probability of peak detection [36]. Firefly has also been employed in telecommunications, as studied in [39] for instance. Although firefly algorithm has been implemented for clustering sensor nodes or users in wireless sensor networks [47], the user grouping issue in NOMA has not been addressed.…”

Section: Firefly Optimization Modelmentioning

confidence: 99%

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Ant-colony and nature-inspired heuristic models for NOMA systems: a review

Liyn

Ghani²,

Roslim³

et al. 2020

TELKOMNIKA

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The increasing computational complexity in scheduling the large number of users for non-orthogonal multiple access (NOMA) system and future cellular networks lead to the need for scheduling models with relatively lower computational complexity such as heuristic models. The main objective of this paper is to conduct a concise study on ant-colony optimization (ACO) methods and potential nature-inspired heuristic models for NOMA implementation in future high-speed networks. The issues, challenges and future work of ACO and other related heuristic models in NOMA are concisely reviewed. The throughput result of the proposed ACO method is observed to be close to the maximum theoretical value and stands 44% higher than that of the existing method. This result demonstrates the effectiveness of ACO implementation for NOMA user scheduling and grouping.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Firefly Optimization Modelmentioning

confidence: 99%

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

Liyn

Ghani²,

Roslim³

et al. 2020

TELKOMNIKA

View full text Add to dashboard Cite

show abstract

Location-based Tourism Recommender System using Swarm Intelligence

et al. 2024

2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG)

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Modified Firefly Optimization Algorithm-Based IDS for Nature-Inspired Cybersecurity

et al. 2023

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The new paradigm of nature-inspired cybersecurity can establish a robust defense by utilizing well-established nature-inspired computing algorithms to analyze networks and act quickly. The existing research focuses primarily on the efficient selection of features for quick and optimized detection rates using firefly and other nature-inspired optimization techniques. However, selecting the most appropriate features may be specific to the network, and a different set of features may work better than the selected one. Therefore, there is a need for a generalized pre-processing step based on the standard network monitoring parameters for the early detection of suspicious nodes before applying feature-based or any other type of monitoring. This paper proposes a modified version of the firefly optimization algorithm to effectively monitor the network by introducing a novel health function for the early detection of suspicious nodes. We implement event management schemes based on the proposed algorithm and optimize the observation priority list based on a genetic evolution algorithm for real-time events in the network. The obtained simulation results demonstrate the effectiveness of the proposed algorithm under various attack scenarios. In addition, the results indicate that the proposed method reduces approximately 60–80% of the number of suspicious nodes while increasing the turnaround time by only approximately 1–2%. The proposed method also focuses specifically on accurate network health monitoring to protect the network proactively.

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Firefly Algorithm in Telecommunications

Cited by 3 publications

References 32 publications

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

Ant-colony and nature-inspired heuristic models for NOMA systems: a review

Location-based Tourism Recommender System using Swarm Intelligence

Modified Firefly Optimization Algorithm-Based IDS for Nature-Inspired Cybersecurity

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