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SDN-IoT: SDN-based efficient clustering scheme for IoT using improved sailfish optimization algorithm

PEERJ COMPUTER SCIENCE 2023年 9卷 e1424页

作者： Mohammadi, Ramin Akleylek, Sedat Ghaffari, Ali Ondokuz Mayis Univ Dept Computat Sci Samsun Turkiye Ondokuz Mayis Univ Samsun Dept Comp Engn Samsun Turkiye Univ Tartu Tartu Estonia Ondokuz Mayis Univ Cyber Secur & Informat Technol Reseach & Dev Ctr Samsun Turkiye Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Istinye Univ Fac Engn & Nat Sci Dept Comp Engn Istanbul Turkiye

The Internet of Things (IoT) includes billions of different devices and various applications that generate a huge amount of data. Due to inherent resource limitations, reliable and robust data transmission for a huge number of heterogenous devices is one of the most critical issues for IoT. Therefore, cluster-based data transmission is appropriate for IoT applications as it promotes network lifetime and scalability. On the other hand, Software Defined Network (SDN) architecture improves flexibility and makes the IoT respond appropriately to the heterogeneity. This article proposes an SDN-based efficient clustering scheme for IoT using the Improved sailfish optimization (ISFO) algorithm. In the proposed model, clustering of IoT devices is performed using the ISFO model and the model is installed on the SDN controller to manage the Cluster Head (CH) nodes of IoT devices. The performance evaluation of the proposed model was performed based on two scenarios with 150 and 300 nodes. The results show that for 150 nodes ISFO model in comparison with LEACH, LEACH-E reduced energy consumption by about 21.42% and 17.28%. For 300 ISFO nodes compared to LEACH, LEACH-E reduced energy consumption by about 37.84% and 27.23%.

关键词： SDN Clustering sailfish optimization algorithm

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The sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2019年 80卷 20-34页

作者： Shadravan, S. Naji, H. R. Bardsiri, V. K. Islamic Azad Univ Kerman Branch Dept Comp Engn Kerman Iran Grad Univ Adv Technol Dept Comp Engn Kerman Iran

Nature-inspired optimization algorithms, especially swarm based algorithms (SAs), solve many scientific and engineering problems due to their flexibility and simplicity. These algorithms are applicable for optimization problems without structural modifications. This work presents a novel nature-inspired metaheuristic optimization algorithm, called sailfish Optimizer (SFO), which is inspired by a group of hunting sailfish. This method consists of two tips of populations, sailfish population for intensification of the search around the best so far and sardines population for diversification of the search space. The SFO algorithm is evaluated on 20 well-known unimodal and multimodal mathematical functions to test different characteristics of the algorithm. In addition, SFO is compared with the six state-of-art metaheuristic algorithms in low and high dimensions. It also indicates competitive results for improvement of exploration and exploitation phases, avoidance of local optima, and high speed convergence especially on large-scale global optimization. The SFO algorithm outperforms the best algorithms in the literature on the majority of the test functions and it shows the statistically significant difference among other algorithms. Moreover, the SFO algorithm shows significantly great results for non-convex, non-separable and scalable test functions. Eventually, the promising results on five real world optimization problems indicate that the SFO is applicable for problem solving with constrained and unknown search spaces.

关键词： Swarm optimization Metaheuristic algorithm sailfish optimization algorithm Constrained engineering optimization

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Prediction of demand for staple food and feed grain by a novel hybrid fractional discrete multivariate grey model

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APPLIED MATHEMATICAL MODELLING 2024年第PartB期125卷 85-107页

作者： Zhang, Xiaolong Rao, Congjun Xiao, Xinping Hu, Fuyan Goh, Mark Wuhan Univ Technol Sch Sci Wuhan 430070 Peoples R China Natl Univ Singapore NUS Business Sch Singapore 119613 Singapore Natl Univ Singapore Logist Inst Asia Pacific Singapore 119613 Singapore

Increasing geopolitical conflicts, extreme weather and other emergencies are continuously affecting global agriculture and food systems, threatening national and regional food security. Therefore, it is particularly important to accurately predict national or regional grain demand. In this paper, a novel fractional discrete dynamic multivariate grey model is proposed to predict grain demand. Based on the discrete modeling idea, the model considers the impacts of the change trend of the related factor sequences, adds the linear correction term and the random disturbance term, and applies the fractional order accumulation strategy, which improves the accuracy and robustness. An algorithm based on sailfish optimizer is introduced to optimize the fractional order accumulation parameter of the model. Taking the Yangtze River Economic Belt as an example, the fitting and prediction results of the novel and the existing three models are compared. The novel model can better fit and predict the demand for staple and feed grain, which is superior compared to other models. The predictions show that demand for staple food and urban feed grain in the Yangtze River Economic Belt will increase to varying degrees, while rural feed grain demand will remain stable overall. This paper will help the government to better grasp the changes in the structure and quantity of staple food and feed grain demand in the Yangtze River Economic Belt and formulate efforts to ensure food security.

关键词： Demand for staple food and feed grain Yangtze River Economic Belt Fractional discrete dynamic multivariate grey model sailfish optimization algorithm

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A New Feature Level-Based Multi-Biometric Identification System to Enable Secure and Reliable Communication Systems for Next-Generation Smart Cities

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SECURITY AND PRIVACY 2025年第1期8卷

作者： Mohammed, Bayan Omar Univ Sulaimani Coll Sci Dept Comp Sci Sulaymaniyah Iraq

The concept of smart cities, which aims to improve urban lifestyles through innovative technologies and policies, has gained significant momentum over the last few years. As we transition into the next-generation smart city era, it is crucial to review some of the fundamental technologies driving this evolution. Among these, security-related challenges necessitate the implementation of effective authentication systems. This paper introduces a novel feature-based multi-biometric identification system designed to provide secure and reliable communication frameworks for next-generation smart cities. The use of multi-modal biometric identification for twins has long been a fascinating and valuable area of research. Because of its high reliability and acceptance, this approach significantly advances the domain of biometric identification for twins. The unique characteristics of twins are determined by variations in features extracted during the multi-modal biometric process. However, many of these extracted features are superfluous, expanding the search space and complicating the generalization process. Therefore, the primary challenge lies in isolating the most salient features capable of accurately identifying twins through multi-modal biometrics. To address this challenge, this research proposes an improved twin multi-modal biometric identification framework. The proposed method employs a fusion of Dis-Eigen features with the sailfish optimization algorithm to enhance both accuracy and efficiency. In this study, unique biometric features of twins were extracted and simplified using the Dis-Eigen process, after which the sailfish algorithm was applied to optimize model parameters and improve identification accuracy. As a result, the intra-class similarity error decreased, while the inter-class similarity error increased, improving twin identification. Consequently, several classifier applications achieved an identification rate exceeding 93%.

关键词： Dis-Eigen feature level-based multi-biometric identification system next generation smart cities sailfish optimization algorithm secure and reliable

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SOA Based BB84 Protocol for Enhancing Quantum Key Distribution in Cloud Environment

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WIRELESS PERSONAL COMMUNICATIONS 2023年第3期130卷 1759-1793页

作者： Sehgal, Shravan Kumar Gupta, Rashmi Guru Gobind Singh Indraprastha Univ GGSIPU Delhi 110078 India NSUT Dept Elect & Commun Engn AIACTR New Delhi 110031 India Netaji Subhas Univ Technol Dept Elect & Commun Engn Delhi 110078 India

Quantum Key Distribution (QKD) systems are thought to be the best method for securing data in cloud storage and boosting security and privacy. Due to the increasing use of cloud services, ensuring the confidentiality of stored data in cloud storage, data exchange, and key sharing used to encrypt data has become a major concern in recent years. The error key may occur during key generation. Through this error key, Eve can easily know the knowledge of the shared key. Enhanced error correction algorithms are utilized to discover and eliminate mistake bits while transmission, ensuring that both keys are equal and producing their shared error-free secret key. Hence, this study improves a BB84 protocol by improving its bit size at the compatibility level using the sailfish optimization algorithm (SOA), and together with the transmitter, as well as the receiver, create a raw key in the next state. QKD is developed from improved BB84 protocol and encrypts data using a hybrid AES-RC4 encryption algorithm. The improved BB84 protocol generates the quantum key distribution, which encrypts data using a hybrid encryption algorithm. Here, error correction is done through the multi-objective function which is optimized using the sailfish optimization technique, resulting in outcomes through adding either estimate mistake or a best key combination. After encryption, if the data is uploaded to the cloud, only the authorized user can decode the data. Moreover, in a Python environment, the proposed method is implemented, and the proposed model's accuracy rate is 97 per cent, with a 3 per cent error rate and 59 s for key generation time. As a result, the proposed SOA-based QKD swift key generation system outperforms existing methods.

关键词： QKD BB84 IBB84 sailfish optimization algorithm Hybrid AES-RC4 encryption algorithm

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HPSO-WOA-SFO: A Novel Hybrid Swarm Intelligence Approach for Enhancing Discrete Road Path Planning 9th

HPSO-WOA-SFO: A Novel Hybrid Swarm Intelligence Approach for...

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9th International Conference on Intelligent IoT as a Service

作者： Wu, You Hu, Xi Zhu, Guosheng Hubei Univ Sch Cyber Sci & Technol Wuhan 430062 Peoples R China Jianghan Univ Sch Artificial Intelligence Wuhan 430056 Peoples R China

ISBN: (纸本)9783031705069;9783031705076

The Hybrid Particle Swarm optimization-Whale optimization algorithm-sailfish Optimizer (HPSO-WOA-SFO) is proposed for solving multi-obstacle discrete road path planning. This paper proposes to utilize the advantage of the two-population update iteration of the sail-fish algorithm to integrate the PSO and WOA into the SFO to enhance its exploitation ability and exploration ability, respectively. Meanwhile, the two communication mechanisms between the two populations of the SFO are studied in depth, and their algorithmic advantages and application scenarios are analyzed. Comparative experiments with four representative path planning algorithms and ablative experiments involving HPSO-WOA-SFO are conducted. The results demonstrate that, on average, HPSO-WOA-SFO outperforms the comparative algorithms by 21.40% in terms of global optimal convergence accuracy and is 10.71% faster in terms of convergence speed. Moreover, the proposed algorithm rapidly escapes local optima and enhances global optimality by 17.47% when trapped in local optima during the optimization process.

关键词： Evolutionary algorithm Discrete Space optimization Problem sailfish optimization algorithm UAV Path Planning

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Study on the optimization of the classification method of rolling bearing fault type and damage degree based on SFO-VMD

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MEASUREMENT SCIENCE AND TECHNOLOGY 2023年第12期34卷 125047-125047页

作者： Jing, Laixing Bian, Jun He, Xiangdong Liu, Yanqiu Dalian Polytech Univ Sch Mech Engn & Automat Dalian 116034 Peoples R China Zhongyuan Inst Sci & Technol Electromech Engn Sch Zhengzhou 450007 Peoples R China

Currently, rolling bearings operate in harsh environments, resulting in acquired signals with a low signal-to-noise ratio. In light of this, this paper proposes an improved variational modal decomposition(VMD) combined with refine composite multi-scale fuzzy entropy (RCMFE) and linear support vector machine (LSVM) for fault diagnosis. Firstly, the sailfish optimization (SFO) algorithm is employed to optimize the important parameter combinations in the VMD algorithm, using the envelope entropy as its objective function. The analysis includes both simulated and real measured signals with varying signal-to-noise ratios. The results demonstrate that, compared to traditional manual parameter setting and empirical modal decomposition methods, this approach effectively addresses the parameter setting issue of VMD in the signal decomposition process. Additionally, it successfully eliminates noise to extract the fault characteristic signal hidden within the original signal. Secondly, the RCMFE algorithm is introduced to overcome the problem of commonly used dimensioned and dimensionless indicators being influenced by load and speed when used as characteristic indicators. By analyzing the influence of load and speed on the RCMFE value, the results demonstrate its strong stability as a feature indicator, unaffected by these factors. For the intelligent classification of failure type and damage degree, LSVM is chosen as the classification method. Analysis results indicate that the distribution characteristics of RCMFE values align better with LSVM compared to the common radial basis function support vector machine, resulting in a significant improvement in diagnosis accuracy.

关键词： rolling bearings variational modal decomposition sailfish optimization algorithm refine composite multi-scale fuzzy entropy fault diagnosis

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Energy-Efficient Cluster-based Routing Protocol in Internet of Things Using Swarm Intelligence

Energy-Efficient Cluster-based Routing Protocol in Internet ...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Sankar, Sennan Ramasubbareddy, Somula Chen, Fang Gandomi, Amir H. Sona Coll Technol Dept Comp Sci & Engn Salem India VNRVJIET Dept Informat Technol Hyderabad India Univ Technol Sydney Fac Engn & Informat Technol Ultimo Australia

ISBN: (纸本)9781728125473

Energy conservation is a difficult challenge, because the Internet of Things (IoT) connects limited resource devices. Clustering is an efficient method for energy saving in network nodes. The existing clustering algorithms have problems with the short lifespan of a network, an unbalanced load among the network nodes and increased end-to-end delay s. This paper proposes a new Cluster Head (CH) selection and cluster formation algorithm to overcome these issues. The process has two phases. First, the CH is selected using a Swarm Intelligence algorithm called sailfish optimization algorithm (SOA). Second, the cluster is formed by the Euclidean distance. The simulation is conducted using the NS2 simulator. The efficacy of the SOA is compared to Improved Ant Bee Colony optimization-based Clustering (IABCOCT), Enhanced Particle Swarm optimization Technique (EPSOCT) and Hierarchical Clustering-based CH Election (HCCHE). The final results of the simulation show that the proposed SOA improves network life and decreases node-to-sink delays.

关键词： Internet of Things Wireless Sensor Networks Swarm Intelligence sailfish optimization algorithm

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