Enhancing Security of MME Handover via Fractional Programming and Firefly Algorithm

Vien, Quoc-Tuan; Lê, Tuấn Anh; Yang, Xin‐She; Duong, Trung Q.

doi:10.1109/tcomm.2019.2920353

Cited by 10 publications

(2 citation statements)

References 43 publications

(98 reference statements)

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“…These particularities have made SI to be a promising solution for many problems in various research areas, and therefore have been applied to solve an increasing number of problems in NGN. For example, an ACO algorithm is proposed to optimize the mobility trajectory for a mobile sink in home automation networks [25], the PSO method is employed to optimize antenna placement in distributed MIMO systems in [26], and the Firefly algorithm (FA) is utilized to find the key update and residence management for an enhanced handover security [27]. The work in [28] employed the Harris hawks optimizer (HHO) to overcome the non-convex difficulties caused by the channel model in unmanned aerial vehicle (UAV) assisted visible light communications.…”

Section: B Swarm Intelligence For Next-generation Wireless Networkmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

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Due to the proliferation of smart devices and emerging applications, many next-generation technologies have been paid for the development of wireless networks. Even though commercial 5G has just been widely deployed in some countries, there have been initial efforts from academia and industrial communities for 6G systems. In such a network, a very large number of devices and applications are emerged, along with heterogeneity of technologies, architectures, mobile data, etc., and optimizing such a network is of utmost importance. Besides convex optimization and game theory, swarm intelligence (SI) has recently appeared as a promising optimization tool for wireless networks. As a new subdivision of artificial intelligence, SI is inspired by the collective behaviors of societies of biological species. In SI, simple agents with limited capabilities would achieve intelligent strategies for high-dimensional and challenging problems, so it has recently found many applications in nextgeneration wireless networks (NGN). However, researchers may not be completely aware of the full potential of SI techniques. In this work, our primary focus will be the integration of these two domains: NGN and SI. Firstly, we provide an overview of SI techniques from fundamental concepts to well-known optimizers. Secondly, we review the applications of SI to settle emerging issues in NGN, including spectrum management and resource allocation, wireless caching and edge computing, network security, and several other miscellaneous issues. Finally, we highlight open challenges and issues in the literature, and introduce some interesting directions for future research.

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Section: B Swarm Intelligence For Next-generation Wireless Networkmentioning

confidence: 99%

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Pham¹,

Nguyen²,

Mirjalili³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…To solve the multi-UAV task allocation problem, an improved genetic algorithm (GA) was proposed in [16]. An adaptive firefly algorithm (FA) algorithm was proposed to enhance data security in [17]. By using the golden section (GS) algorithm, Cuevas et al optimized the evolutionary computation in [18].…”

Section: Introductionmentioning

confidence: 99%

Power Allocation for 5G Mobile Multiuser Cooperative Networks

Yin

2021

Journal of Advanced Transportation

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With the fifth generation (5G) communication technology, the mobile multiuser networks have developed rapidly. In this paper, the performance analysis of mobile multiuser networks which utilize decode-and-forward (DF) relaying is considered. We derive novel outage probability (OP) expressions. To improve the OP performance, we study the power allocation optimization problem. To solve the optimization problem, we propose an intelligent power allocation optimization algorithm based on grey wolf optimization (GWO). We compare the proposed GWO approach with three existing algorithms. The experimental results reveal that the proposed GWO algorithm can achieve a smaller OP, thus improving system efficiency. Also, compared with other channel models, the OP values of the 2-Rayleigh model are increased by 81.2% and 66.6%, respectively.

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Transmit Beamforming Designs in Wireless Communications Using the Firefly Algorithm

Le,

Yang

2024

Springer Tracts in Nature-Inspired Computing

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Enhancing Security of MME Handover via Fractional Programming and Firefly Algorithm

Cited by 10 publications

References 43 publications

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Swarm Intelligence for Next-Generation Wireless Networks: Recent Advances and Applications

Power Allocation for 5G Mobile Multiuser Cooperative Networks

Transmit Beamforming Designs in Wireless Communications Using the Firefly Algorithm

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