Wireless communication networks and swarm intelligence

Al-Mousawi, Ali Jameel

doi:10.1007/s11276-021-02545-x

Cited by 7 publications

(5 citation statements)

References 54 publications

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“…In this paper, we introduce a form of swarm intelligence [29][30][31][32][33][34][35][36][37][38][39], the DBFO algorithm, to solve the multiradio, multichannel allocation problem in wireless monitoring networks [40].…”

Section: Design Of Hardware and Softwarementioning

confidence: 99%

Channel Allocation Algorithm Based on Swarm Intelligence for a Wireless Monitoring Network

et al. 2023

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In wireless networks, multiple monitoring nodes are used to collect users’ transmission data in real time, which can be used for fault diagnosis and analytical feedback of the wireless network. Due to the limited number of monitoring nodes, key issues include how to optimize and schedule the channel resources of each node to cover more users, obtain more network data, and maximize the quality of network monitoring. In this paper, a channel allocation algorithm based on swarm intelligence—“discrete bacterial foraging optimization”—is proposed based on the classic bacterial foraging optimization algorithm. The position of each dimension in the iterative process is discretized to binary 0 or 1 to encode and express the channel allocation problem of wireless monitoring networks, and the channel allocation scheme is optimized by location updates guided by bacterial foraging. Many simulation and practical experiments have proved the effectiveness of the algorithm, and it also has low complexity and provable convergence. Compared with similar algorithms, this algorithm improves monitoring quality by 1.428% while boosting speed by up to 32.602%. The algorithm has lower complexity, higher performance, and can converge to the optimal solution at a faster rate.

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Section: Design Of Hardware and Softwarementioning

confidence: 99%

Channel Allocation Algorithm Based on Swarm Intelligence for a Wireless Monitoring Network

et al. 2023

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“…(1), x k , y k represents the coordinates of the k -th point, and x c and y c are the vertical and horizontal coordinates of the central pixel respectively. It can be seen from formula (1) that the coordinates of the image sampling points in the mobile network are not necessarily integers, (1) x k , y k = x c + R cos 2 k∕R cos P, y c − R sin 2 k∕R sin P and the coordinates of the pixel points on the digital image must be integers, otherwise the pixel value of the sampling point cannot be found, this affects the security of image data in a mobile network [16,17]. Bilinear interpolation algorithm is used to calculate the gray value of coordinate complement integer points.…”

Section: Image Target Key Feature Extraction Based On Rotation Invari...mentioning

confidence: 99%

“…At present, my country has entered the information age, and the form of "Internet + " has become an upsurge in people's work and life, and the distance between people has gradually narrowed with the change of communication methods [1,2]. Especially in recent years, 5G technology has been continuously developed and gradually applied in various fields [3].…”

Section: Introductionmentioning

confidence: 99%

Design of Quick Search Method for Key Feature Images in Mobile Networks

Zheng

Woźniak

2022

Mobile Netw Appl

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In order to promote the efficiency of image retrieval in mobile network and realize the fast query of key images, this paper designs a quick search method of key feature images in mobile networks. The key features of retrieved images are extracted by rotation invariant local binary method. According to the extracted key features of the image, the query target image is processed by coarse quantization then the distance of the key features of the image is calculated and retrieved. Finally, non-exhaustive search method is used to achieve quick search of key feature images in mobile network. Experimental results show that this method can effectively extract specific images, The desired image can be quickly searched by reserved key features, and the F-score value of quick search is higher than 0.9.

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“…SAs have become an important method for solving optimization problems because of their excellent self-organization, self-adaptation, and self-learning characteristics. It has been adopted in various domains [ 11 , 12 ], such as image segmentation [ 13 ], wireless networks [ 14 ], unmanned aerial vehicles [ 15 ], target tracking [ 16 ], neural network [ 17 ], MRI classification [ 18 ], feature selection [ 19 ], and engineering problems [ 20 ], and vehicle design [ 21 , 22 ].…”

Section: Introductionmentioning

confidence: 99%

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

et al. 2022

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Whale optimization algorithm (WOA) tends to fall into the local optimum and fails to converge quickly in solving complex problems. To address the shortcomings, an improved WOA (QGBWOA) is proposed in this work. First, quasi-opposition-based learning is introduced to enhance the ability of WOA to search for optimal solutions. Second, a Gaussian barebone mechanism is embedded to promote diversity and expand the scope of the solution space in WOA. To verify the advantages of QGBWOA, comparison experiments between QGBWOA and its comparison peers were carried out on CEC 2014 with dimensions 10, 30, 50, and 100 and on CEC 2020 test with dimension 30. Furthermore, the performance results were tested using Wilcoxon signed-rank (WS), Friedman test, and post hoc statistical tests for statistical analysis. Convergence accuracy and speed are remarkably improved, as shown by experimental results. Finally, feature selection and multi-threshold image segmentation applications are demonstrated to validate the ability of QGBWOA to solve complex real-world problems. QGBWOA proves its superiority over compared algorithms in feature selection and multi-threshold image segmentation by performing several evaluation metrics. Supplementary Information The online version contains supplementary material available at 10.1007/s42235-022-00297-8.

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Wireless communication networks and swarm intelligence

Cited by 7 publications

References 54 publications

Channel Allocation Algorithm Based on Swarm Intelligence for a Wireless Monitoring Network

Channel Allocation Algorithm Based on Swarm Intelligence for a Wireless Monitoring Network

Design of Quick Search Method for Key Feature Images in Mobile Networks

Boosting Whale Optimizer with Quasi-Oppositional Learning and Gaussian Barebone for Feature Selection and COVID-19 Image Segmentation

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