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Research on the Routing Method of Swarm Network Based on moth search Link Distance and Residual Energy Weights

IEEE ACCESS 2023年 11卷 84852-84859页

作者： Chen, Guangjiao Chen, Guifen Wu, Guowei Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun 130022 Peoples R China

Routing method is a key technology for swarm communication. In order to reduce the node energy consumption in large-scale swarm communication, a routing method based on moth search link distance and residual energy is proposed. First, the swarm network is divided into sub-cluster networks by filtering of cluster head nodes. The node with more residual energy is used as cluster head to forward data signals from other nodes. The link between cluster head and edge nodes is established by relaying the signals from the nodes. The routing function of the link is established by calculating the distance and residual energy weight of the nodes. During cluster head broadcast communication, a modified moth-based search algorithm is applied to solve the combined weights of the routes. The optimal solution of the function constitutes the best route within the subcluster. Simulation results show that the proposed method meets the communication requirements, while the performance of the proposed method is higher than other mainstream algorithms by comparing the magnitude of three metrics: network coverage, residual energy and BER. The proposed routing method reduces the energy consumption of multi-node cluster network communication. The method achieves routing for large-scale cluster communication.

关键词： Swarm network routing method link length node residual energy moth search algorithm

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GMMSO: game model-combined improved moth search optimization approach for reconfigurable asymmetric multi-processor system-on-chip architecture

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ENGINEERING OPTIMIZATION 2023年第4期55卷 685-702页

作者： Mariyappan, Isaivani Veluchamy, Malathi Anna Univ Dept Elect & Elect Engn Reg Campus Madurai Madurai Tamil Nadu India

Heterogeneous Multi-Processor System-on-Chip architectures are prevalent in modern embedded system applications that target high-performance needs. This work comprises two parts to optimize the multiprocessor system structure concerning system speed, power, and area requirements. The first part includes the game model concept, which is remarkably advantageous in generating strategic decisions on incoming signals and minimizes traffic delays coming from the input portion of the system. Secondly, an improved moth search optimization is proposed to explore the design space and optimize the system space and power consumption. This research is implemented in the Field Programmable Gate Array (FPGA) Xilinx Zynq and Virtex platforms and evaluated by ExPRESS benchmarks. The Game Model-combined improved moth search Optimization (GMMSO) technique showed performance improvements of 69.34% in delay measurement, 46% in data rate and 36.34% in static power consumption compared with the traditional HMILP (Heuristic Mixed Integer Linear Programming) methods.

关键词： Very large-scale integration multi-processor system-on-chip optimization game model moth search algorithm field programmable gate array

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Brain tumor diagnosis based on discrete wavelet transform, gray-level co-occurrence matrix, and optimal deep belief network

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2020年第11期96卷 867-879页

作者： Xu, Li Gao, Qi Yousefi, Nasser Baotou Med Coll Dept Comp Sci & Technol Baotou 014040 Inner Mongolia Peoples R China Islamic Azad Univ Sci & Res Branch Tehran Iran

Brain tumors are a group of cancers that originate from different cells of the central nervous system or cancers of other tissues in the brain. Excessive cell growth in the brain is called a tumor. Tumor cells need food and blood to survive. Growth and proliferation of tumor cells in the cranial space, cause strain inside the brain and thus disrupt vital human structures. Therefore, diagnosis in the early stages of brain tumors is crucial. This study introduces a new optimized method for early diagnosis of the brain tumor. The method has five main parts of noise reduction, tumor segmentation, morphology, feature extraction based on wavelet and gray-level co-occurrence matrix, and classification based on an optimized deep belief network. For optimizing the classifier network, an enhanced version of the moth search algorithm is utilized. Simulation results are applied to three different datasets, FLAIR, T1, and T2, and the accuracy results of the presented method are compared with two other metaheuristics, particle swarm optimization and Bat algorithms. The final results showed that the presented technique has good achievements toward the compared methods.

关键词： Brain tumor image segmentation morphology discrete wavelet transform gray-level co-occurrence matrix deep belief network improved moth search algorithm

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Enhancing multi-tenancy security in the cloud computing using hybrid ECC-based data encryption approach

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IET COMMUNICATIONS 2020年第18期14卷 3212-3222页

作者： Kumar, Pawan Kumar Bhatt, Ashutosh Inst Technol Dept Comp Sci & Engn Gopeshwar Uttarakhand India Birla Inst Appl Sci Dept Comp Sci & Engn Bhimtal Uttarakhand India

In this study, elliptic curve cryptography (ECC) is elected for tenant authentication, data encryption, and data decryption due to its minimum key size. The proposed ECC-based authentication approach allows the authorised person to access private data;it protects different related attacks effectively. To develop more secure data encryption, the authors have combined a nature-inspired optimisation, such as a moth search algorithm (MSA) with ECC, to select the correct and optimal value of the elliptic curve. The proposed encryption and decryption approach combines DNA encoding with the ECC encryption algorithm. The mechanism of DNA encoded ECC provided multi-level security with less computational power. The security analysis of the proposed method has been provided to prove its effectiveness against certain attacks, such as denial-of-service attack, impersonation attack, reply attack, plaintext attack and chosen-ciphertext attack. The experimental result is evaluated based on encryption time, decryption time, throughput and key size of the security model. The average execution time of the proposed encryption and decryption is only 83.153 and 86.076 s, respectively. From the evaluation, it is clearly determined that the proposed technique provides two-layer security with minimum key size and less storage space.

关键词： cloud computing public key cryptography message authentication authorisation search problems cloud computing hybrid ECC-based data encryption approach elliptic curve cryptography tenant authentication data decryption minimum key size ECC-based authentication approach private data secure data encryption nature-inspired optimisation moth search algorithm correct value optimal value decryption approach DNA encoding ECC encryption algorithm multilevel security security analysis denial-of-service attack impersonation attack reply attack plaintext attack chosen-ciphertext attack encryption time decryption time two-layer security multitenancy security enhancement

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Incremental learning for text categorization using rough set boundary based optimized Support Vector Neural Network

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DATA TECHNOLOGIES AND APPLICATIONS 2020年第5期54卷 585-601页

作者： Venkata Sailaja, N. Padmasree, L. Mangathayaru, N. VNR Vignana Jyothi Inst Engn & Technol Hyderabad Telangana India

Purpose Text mining has been used for various knowledge discovery based applications, and thus, a lot of research has been contributed towards it. Latest trending research in the text mining is adopting the incremental learning data, as it is economical while dealing with large volume of information. Design/methodology/approach The primary intention of this research is to design and develop a technique for incremental text categorization using optimized Support Vector Neural Network (SVNN). The proposed technique involves four major steps, such as pre-processing, feature selection, classification and feature extraction. Initially, the data is pre-processed based on stop word removal and stemming. Then, the feature extraction is done by extracting semantic word-based features and Term Frequency and Inverse Document Frequency (TF-IDF). From the extracted features, the important features are selected using Bhattacharya distance measure and the features are subjected as the input to the proposed classifier. The proposed classifier performs incremental learning using SVNN, wherein the weights are bounded in a limit using rough set theory. Moreover, for the optimal selection of weights in SVNN, moth search (MS) algorithm is used. Thus, the proposed classifier, named Rough set MS-SVNN, performs the text categorization for the incremental data, given as the input. Findings For the experimentation, the 20 News group dataset, and the Reuters dataset are used. Simulation results indicate that the proposed Rough set based MS-SVNN has achieved 0.7743, 0.7774 and 0.7745 for the precision, recall andF-measure, respectively. Originality/value In this paper, an online incremental learner is developed for the text categorization. The text categorization is done by developing the Rough set MS-SVNN classifier, which classifies the incoming texts based on the boundary condition evaluated by the Rough set theory, and the optimal weights from the MS. The proposed online text categorizatio

关键词： Incremental learning Text mining Support Vector Neural Network Rough set theory moth search algorithm

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A hybrid attack detection strategy for cybersecurity using moth elephant herding optimisation-based stacked autoencoder

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IET CIRCUITS DEVICES & SYSTEMS 2021年第3期15卷 224-236页

作者： Alotaibi, Abdullah Shawan Shaqra Univ Dept Comp Sci Shaqra Saudi Arabia

Cybersecurity is a major concern in the network resources of the Internet of Things (IoT) environment, as the attacks on the IoT devices degrade the performance of the computational operations. Various attack detection methods are adopted in the research area to prevent an illegal user from accessing the resources. To resolve the vulnerabilities in the computing devices, an effective attack detection method, named, moth Elephant Herding Optimisation (MEHO)-based stacked autoencoder approach is proposed in this research. Initially, the input data is passed into the pre-processing stage, where the data is cleaned by removing the noise and artefacts . The pre-processed data is further subjected to the feature selection stage, where the Class-Wise Information Gain technique (CIG) is used to select the essential features. The class-aware features, like traffic-based features, content-based features and basic features are selected efficiently. Finally, the attack detection is performed using the stacked autoencoder classifier, which is trained using the proposed MEHO algorithm. The MEHO algorithm is developed by integrating the moth search (MS) algorithm and the Elephant Herding Optimisation (EHO). The performance is evaluated using the metrics, like accuracy, False Acceptance Rate (FAR) and detection rate, acquired with the values of 0.9286, 0.0636 and 0.9258 respectively.

关键词： false acceptance rate content-based features computer crime feature selection IoT devices optimisation noise removal class-wise information gain search problems cybersecurity class-aware features telecommunication traffic moth elephant herding optimisation-based stacked autoencoder detection rate moth search algorithm FAR stacked autoencoder classifier neural nets learning (artificial intelligence) traffic-based features Internet of Things hybrid attack detection CIG network resources computing devices MEHO computer network security pattern classification data cleaning

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Improving convergence in swarm algorithms by controlling range of random movement

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NATURAL COMPUTING 2021年第3期20卷 513-560页

作者： Chaudhary, Reshu Banati, Hema Univ Delhi Dept Comp Sci New Delhi India Univ Delhi Dyal Singh Coll New Delhi India

Swarm intelligence algorithms are stochastic algorithms, i.e. they perform some random movement. This random movement imparts the algorithms with exploration capabilities and allows them to escape local optima. Exploration at the start of execution helps with thorough inspection of the search/solution space. However, as the algorithm progresses, the focus should ideally shift from exploration to exploitation. This shift would help the algorithm to enhance existing solutions and improve its convergence capabilities. Hence if the range of random movement is not kept in check, it may limit an algorithm's convergence capabilities and overall efficiency. To ensure that the convergence of an algorithm is not compromised, an improved search technique to reduce range of uniform random movement was recently proposed for bat algorithm. Uniform distribution and levy distribution are the most commonly used random distributions in swarm algorithms. In this paper, the applicability of the improved search technique over different swarm algorithms employing uniform and levy distributions, as well as Cauchy distribution has been studied. The selected algorithms are firefly algorithm, cuckoo search algorithm, moth search algorithm, whale optimization algorithm, earthworm optimization algorithm and elephant herding optimization algorithm. The resultant variants of each of these algorithms show improvement upon inclusion of the improved search technique. Hence results establish that the improved search technique has positive influence over swarm algorithms employing different random distributions.

关键词： Exploitation Improved search moth search algorithm Cuckoo search algorithm Firefly algorithm Whale optimization algorithm Earthworm optimization algorithm Elephant herding optimization algorithm

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Triple-Band Single-Layer Rectenna for Outdoor RF Energy Harvesting Applications

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SENSORS 2021年第10期21卷 3460页

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Koulouridis, Stavros Rocca, Paolo Georgiadis, Apostolos Tentzeris, Manos M. Goudos, Sotirios K. Aristotle Univ Thessaloniki ELEDIA Res Ctr Sch Phys ELEDIA AUTH Thessaloniki 54124 Greece Univ Patras Elect & Comp Engn Dept Patras 26504 Greece Univ Trento ELEDIA UniTN DISI I-38123 Trento Italy Heriot Watt Univ Sch Engn & Phys Sci Edinburgh EH14 4AS Midlothian Scotland Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA

A triple-band single-layer rectenna for outdoor RF energy applications is introduced in this paper. The proposed rectenna operates in the frequency bands of LoRa, GSM-1800, and UMTS-2100 networks. To obtain a triple-band operation, a modified E-shaped patch antenna is used. The receiving module (antenna) of the rectenna system is optimized in terms of its reflection coefficient to match the RF-to-DC rectifier. The final geometry of the proposed antenna is derived by the application of the moth search algorithm and a commercial electromagnetic solver. The impedance matching network of the proposed system is obtained based on a three-step process, including the minimization of the reflection coefficient versus frequency, as well as the minimization of the reflection coefficient variations and the maximization of the DC output voltage versus RF input power. The proposed RF-to-DC rectifier is designed based on the Greinacher topology. The designed rectenna is fabricated on a single layer of FR-4 substrate. Measured results show that our proposed rectenna can harvest RF energy from outdoor (ambient and dedicated) sources with an efficiency of greater than 52%.

关键词： RF energy harvesting rectenna moth search algorithm antenna optimization RF-to-DC rectifier triple-band operation Greinacher voltage doubler impedance matching

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