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Efficient key distribution for secure and energy-optimized communication in wireless sensor network using bioinspired algorithms

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ALEXANDRIA ENGINEERING JOURNAL 2024年 92卷 63-73页

作者： Khadidos, Adil O. Alhebaishi, Nawaf Khadidos, Alaa O. Altwijri, Mohammed Fayoumi, Ayman G. Ragab, Mahmoud King Abdulaziz Univ Fac Comp & Informat Technol Informat Technol Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Informat Syst Dept Jeddah 21589 Saudi Arabia King Abdulaziz Univ Ctr Res Excellence Artificial Intelligence & Data Jeddah Saudi Arabia King Abdulaziz Univ Fac Comp & Informat Technol Dept Comp Sci Jeddah 21589 Saudi Arabia Al Azhar Univ Fac Sci Dept Math Cairo 11884 Egypt

Wireless Sensor Networks (WSNs) play a major part in numerous applications such as smart agriculture, healthcare, and environmental monitoring. Safeguarding protected communication in this network is dominant. Securing data transmission in WSNs needs a strong key distribution device to defend against malicious attacks as well as illegal access. Traditional techniques like pre-shared or centralized key management are often unreasonable owing to resource limitations, particularly in large-scale sensor systems. To overcome this challenge, a lightweight key distribution technique is employed for safeguarding the security and privacy of data transmission streamlining processes decreasing computational overhead as well as energy consumption. By optimizing and simplifying key distribution devices, we propose to improve the complete efficacy and trustworthiness of WSNs that aid safe communication while preserving valuable energy resources. Therefore, this article designs an Efficient Key Distribution for Secure and Energy-Optimized Communication using bioinspired algorithms (EKDSOCBA) for WSN. The purpose of the EKD-SOCBA technique is to accomplish security and energy efficiency in WSNs. Initially, the EKD-SOCBA technique applies a golden jackal optimization (GJO) based clustering approach to cluster the nodes and select cluster heads (CHs). Also, a lightweight Dynamic Step-wise Tiny Encryption Algorithm (DS -TEA) is applied to secure data transmission in the network. Finally, a lightweight key management phase is employed to protect the encryption key and decrease energy utilization and overhead costs. To exhibit the enhanced act of the EKD-SOCBA model, a comprehensive set of imitations was involved. Extensive results stated enhanced presentation of EKD-SOCBA methodology over other models on WSN.

关键词： Wireless sensor networks Key management Lightweight energy Security Energy efficiency bioinspired algorithms

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A Concise Review on Applications of bioinspired algorithms in Electrical Power System

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2024年

作者： Moorthy, C. Balakrishna Sivamani, S. Univ Technol & Appl Sci Coll Engn & Technol Salalah Oman

The integration of bioinspired algorithms into electrical power systems has gained significant attention in recent years due to their potential to address complex optimization and control problems. This paper presents a concise review on the applications of bioinspired algorithms in various aspects of electrical power systems, including power generation, transmission, distribution, and utilization. The review starts with an overview on electrical power system and then discusses the fundamental concepts of bioinspired algorithms. Next, the paper explores the application of bioinspired algorithms in power generation. It examines their use in optimizing the operation and scheduling of power plants, maximizing renewable energy integration, and improving the efficiency of power generation processes. Moving on to power transmission and distribution, the review discusses how bioinspired algorithms can be applied to optimize the routing and scheduling of power flows, enhance fault detection and diagnosis, and improve the reliability and security of the grid infrastructure. Furthermore, the utilization of bioinspired algorithms in power systems is explored, focusing on load forecasting, demand response, energy management, and power quality enhancement. Finally, the paper concludes with a summary of the main findings and future research directions. It emphasizes the need for further exploration of bioinspired algorithms in electrical power systems, including the development of hybrid algorithms and their integration with emerging technologies such as machine learning and big data analytics.

关键词： power generation transmission distribution utilization bioinspired algorithms

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Evaluation and optimization of red pine needle-reinforced roller-compacted concrete by bioinspired algorithms

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COMPOSITES COMMUNICATIONS 2024年 51卷

作者： Yildizel, Sadik Alper Uzun, Mehmet Armagan, Kemal Ozbakkaloglu, Togay Karamanoglu Mehmetbey Univ Engn Fac Dept Civil Engn TR-70200 Karaman Turkiye Texas State Univ Ingram Sch Engn San Marcos TX 78666 USA

This paper discusses the improvement of roller compacted concrete by the addition of red pine needle (PN) fibers optimized by bioinspired techniques. The purpose was to determine a perfect set of mechanical characteristics, such as flexural, compressive, and splitting tensile strengths, by determining proper correlations between the water-cement ratio, superplasticizer, and fiber volume. Based on the data obtained with the help of experiments, it was shown that optimizing the PN fibers added to RCC with nature-inspired algorithms (particle swarm optimization, genetic algorithms, simulated annealing and artificial neural networks) yields positive results in terms of improving a number of characteristics, including flexural strength, fracture toughness, and durability. In addition, adopting PN fibers has certain environmental benefits. All methods under analysis showed good results, with the artificial neural network (ANN) being superior in terms of predicting the parameters of RCC. The discussed research confirms the effectiveness of such optimization methods for determining the best proportions for RCC using PN fibers. At the same time, future studies could further develop sustainable and mechanically strong RCC formulations.

关键词： RCC Optimization bioinspired algorithms Pine needle fiber

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Evaluation of bioinspired algorithms for image optimization

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JOURNAL OF ELECTRONIC IMAGING 2022年第4期31卷 041206-041206页

作者： Sharma, Neha Chakraborty, Chinmay Chandigarh Univ Mohali India Birla Inst Technol Jharkhand India

Steganography is a technique for concealing sensitive information behind a specific media source, such as an image, audio, or video file, in such a way that the concealed data are invisible to everyone. Many algorithms have been developed to optimize this process for better output. We aim to identify the different optimization algorithms used in image steganography after embedding the data to improve the resilience, visibility, and payload carrying capacity. Additionally, we highlight several bioinspired algorithms, including particle swarm optimization, ant colony optimization, firefly optimization, and artificial bee colony optimization, and evaluate through performance measures such as peak signal-to-noise ratio (PSNR) and mean square error (MSE). The performance metrics generated from the collected data indicate that the firefly method produced a higher PSNR and a lower MSE, namely 72.42 dB and 0.13, respectively. The methods are evaluated in terms of their ability for data embedding, robustness, and imperceptibility.

关键词： peak signal-to-noise ratio mean square error particle swarm optimization ant colony optimization firefly algorithm artificial bee colony image optimization image steganography bit error rate bioinspired algorithms computationally intelligent algorithms

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A Comparative Analysis of bioinspired algorithms for Solving the Problem of Optimization of Circulant and Hypercirculant Networks 15

A Comparative Analysis of Bioinspired Algorithms for Solving...

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15th International Asian School-Seminar on Optimization Problems of Complex Systems (OPCS)

作者： Monakhov, Oleg Monakhova, Emilia Inst Computat Math & Math Geophys SB RAS Novosibirsk Russia

ISBN: (纸本)9781728129860

The solution of the optimization problem of constructing regular networks (graphs) that are optimal over the average diameter is investigated. Two classes of parametrically described regular networks are investigated - circulant and hypercirculant networks. An approach using bioinspired algorithms for the automatic synthesis of parametric descriptions of optimal circulant and hypercirculant networks has been developed. A comparative analysis of five different bioinspired algorithms (genetic algorithm, differential evolution, particle swarm optimization, algorithm of artificial bee colony and firefly algorithm) was carried out using them to solve this optimization problem. For the found optimal networks structural characteristics such as diameter, average diameter, bandwidth, reliability were obtained, and these networks were compared according to these characteristics.

关键词： circulant graph hypercirculant network optimization bioinspired algorithms average diameter reliability

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From ephemeral computing to deep bioinspired algorithms: New trends and applications

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2018年 88卷 735-746页

作者： Camacho, David Lara-Cabrera, Raul Merelo-Guervos, J. J. Castillo, Pedro A. Cotta, Carlos Fernandez-Leiva, Antonio J. Fernandez de Vega, Francisco Chavez, Francisco Univ Autonoma Madrid Comp Sci Dept Madrid Spain Univ Granada Comp Architecture & Technol Dept Granada Spain Univ Malaga Malaga Spain Univ Extremadura Comp & Commun Technol Dept Badajoz Spain Univ Extremadura Dept Engn Comp Sci & Telemat Syst Badajoz Spain

Ephemeral computing is a term that describes computing systems whose nodes or their connectivity have an ephemeral, heterogeneous and possibly also unpredictable nature, These properties will affect the functioning of distributed versions of computer algorithms. Such algorithms, which are usually straightforward extensions of sequential algorithms, will have to be redesigned and, in many cases, rethought from the ground up, to be able to use all ephemerally available resources. Porting algorithms to an inherently ephemeral, unreliable and massively heterogeneous computing substrate is thus one of the main challenges in the ephemeral computing field. algorithms adapted so that they can be consciously running on this kind of environments require specific properties in terms of flexibility, plasticity and robustness. bioinspired algorithms are particularly well suited to this endeavor, thanks to their decentralized functioning, intrinsic parallelism, resilience, adaptiveness, and amenability for being endowed with algorithmic components dealing with both the massive complexity of the computational substrate and that of the problem being tackled. Arranging these components and functionalities in a collection of algorithmic strata results in deep architectures, whereby different layers of optimization are organized into loosely-coupled hierarchies that not only are able to use ephemeral computing environments, but also profit from them by making adaptivity and diversity maintenance features of the algorithm. Moreover, the synergies that arise when these massively heterogeneous computing resources are made available to deep versions of bioinspired algorithms may enable hard real-world problems and applications to be successfully faced in the Big Data context (including but not limited to social data analysis) as well as problems in the areas of computational creativity or computer gaming. (C) 2018 Published by Elsevier B.V.

关键词： Ephemeral computing bioinspired algorithms Complex systems Deep bioinspired architectures

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A Comparative Analysis of bioinspired algorithms for Solving the Problem of Optimization of Circulant and Hypercirculant Networks

A Comparative Analysis of Bioinspired Algorithms for Solving...

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International Asian School-Seminar Optimization Problems of Complex Systems

作者： Oleg Monakhov Emilia Monakhova Institute of Computational Mathematics and Mathematical Geophysics SB RAS

ISBN: (纸本)9781728129877

关键词： Circulant graph Hypercirculant Network optimization bioinspired algorithms Average diameter Reliability circulation mean diameter network optimization Network

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bioinspired algorithms and complex systems

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JOURNAL OF COMPUTATIONAL SCIENCE 2017年第Nov.期23卷 192-194页

作者： Cotta, Carlos Schaefer, Robert Univ Malaga Malaga Spain AGH Univ Sci & Technol Krakow Poland

bioinspired algorithms are search, optimization, and learning techniques whose functioning is based on some metaphor of a biological process. Prominent examples include evolutionary algorithms and swarm intelligence methods. The practical application of these techniques to real-world problems typically involves orchestrating the interplay among different algorithmic components in order to attain synergistic search capabilities. This is a common theme in complex systems, in which the whole is more than the sum of the parts due to the complex interaction patterns among system components, giving rise to emergent properties not anticipated at the base level. Indeed, such systems are prevalent in many contexts, both natural (biological systems, ecosystems, etc.) and artificial (social networks, finance markets, etc.). Analyzing and understanding such systems is not only of the foremost interest, but also constitutes in general a formidable task requiring powerful tools. Metaheuristics in general and bioinspired algorithms in particular can greatly contribute to this end. Furthermore, their intrinsic complex nature makes them prone to be also subject of analysis using a complex-system perspective. (C) 2017 Published by Elsevier B.V.

关键词： Complex systems bioinspired algorithms Metaheuristics

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bioinspired artificial intelligence based android malware detection and classification for cybersecurity applications

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ALEXANDRIA ENGINEERING JOURNAL 2024年 100卷 142-152页

作者： Alotaibi, Shoayee Dlaim Alabduallah, Bayan Said, Yahia Hassine, Siwar Ben Haj Alzubaidi, Abdulaziz A. Alamri, Maha Al Zanin, Samah Majdoubi, Jihen Univ Hail Coll Comp Sci & Engn Dept Artificial Intelligence & Data Sci Hail Saudi Arabia Princess Nourah Bint Abdulrahman Univ Coll Comp & Informat Sci Dept Informat Syst POB 84428 Riyadh 11671 Saudi Arabia Northern Border Univ Coll Engn Dept Elect Engn Ar Ar 91431 Saudi Arabia King Khalid Univ Coll Sci & Art Mahayil Dept Comp Sci Abha Saudi Arabia Umm Al Qura Univ Coll Engn & Comp Al qunfudah Dept Comp Sci Mecca Saudi Arabia Al Baha Univ Fac Comp & Informat Dept Syst & Networks Bahah Saudi Arabia Prince Sattam bin Abdulaziz Univ Appl Coll Dept Comp Sci Kharj Saudi Arabia Majmaah Univ Coll Sci & Humanities Alghat Dept Comp Sci Al Majmaah 11952 Saudi Arabia

With the fast growth of mobile phone usage, malicious threats against Android mobile devices are enhanced. The Android system utilizes a wide range of sensitive apps like banking apps;thus, it develops the aim of malware that uses the vulnerability of safety measures. Identifying Android malware in smartphones is a vital target for the cyber community to eliminate menacing malware instances. Drawing stimulus from the adaptability and efficacy of biological systems, these methods emulate nature's problem-solving systems for identifying malicious software. By integrating principles, namely, swarm intelligence (SI), neural networks (NN), and genetic algorithms (GA), these bioinspired systems reveal exceptional efficiency in identifying both known and developing Android malware attacks. This bioinspired system provides a capable avenue for robust Android malware detection from an ever-shifting threat landscape. This article designs a bioinspired Artificial Intelligence-based Android Malware Detection and Classification (BAI-AMDC) technique for Cybersecurity Applications. The BAIAMDC technique exploits the concept of bioinspired algorithms with a DL approach for the classification and detection of Android malware. In the BAI-AMDC technique, an improved cockroach swarm optimization algorithm-based feature selection (ICSOA-FS) technique can be applied to choose optimum features. The BAIAMDC technique employs a bidirectional gated recurrent unit (BiGRU) model for Android malware detection. An arithmetic optimization algorithm (AOA) can be utilized to enhance the detection performance of the BAIAMDC technique. The experimental validation of the BAI-AMDC system can be performed on the CICAndMal2017 database with 10,000 instances. The simulation values highlighted the productive ability of the BAIAMDC system on the Android malware recognition process.

关键词： bioinspired algorithms Metaheuristics Artificial intelligence Android malware Feature selection

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A bioinspired Emergent Control for Smart Grids

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IEEE ACCESS 2023年 11卷 7503-7520页

作者： Garcia Medina, Marcel Simeon Aguilar, Jose Rodriguez-Moreno, Maria D. Univ Nacl Expt Tachira Lab Prototipos San Cristobal 5001 Venezuela Univ Alcala Escuela Politecn Super ISG Alcala De Henares 28805 Spain Univ Los Andes CEMISID Merida 5101 Venezuela Univ EAFIT GIDITIC Medellin Colombia IMDEA Networks Inst Leganes 28918 Madrid Spain Intelligent Autonomous Syst Grp IAS TNO NL-2595 The Hague Netherlands

Satisfying energy demand has become a global problem that is on the rise due to population growth, infrastructure deterioration, a decline in fossil fuel sources, and high costs for investment, among others. Smart grids, in addition to those challenges that they have at the level of energy generation, have other management challenges derived from the great diversity of components that make them up, such as energy storage systems (batteries, capacitors, etc.), the different types of consumers (controllable, non-controllable loads) and prosumers (electric vehicles, self-sustaining buildings, micro-grid, etc.), among others. Consequently, a distributed control problem is presented, mainly oriented to the coordination of its components. A possible solution is to achieve the participation of each component when conditions are more favorable, such as prioritizing production with renewable energy sources, or taking advantage of prosumers so that they can meet local demand, among other things. Therefore, new strategies with a distributed approach such as bio-inspired emergent controls are necessary. The objective of this work is the specification of an emergent control approach to coordinate a smart grid. This approach allows the coordination of the energy supply in various operating scenarios. The results obtained demonstrate a perfect synchronization between the different smart grid components (agents), prioritizing renewable energy sources, regardless of the operational context (for example, in cases of failures, unsuitable environmental conditions, etc.).

关键词： Smart grids Task analysis Renewable energy sources Decentralized control Biological system modeling Bio-inspired computing Mathematical models Emergent control smart grid bioinspired algorithms distributed artificial intelligence

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