Sustainable Computing-Informatics & Systems最新文献_第8页

Artificial intelligence-powered visual internet of things in smart cities: A comprehensive review 智慧城市中由人工智能驱动的视觉物联网：全面回顾

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-05-29 DOI: 10.1016/j.suscom.2024.101004

Omar El Ghati , Othmane Alaoui-Fdili , Othman Chahbouni , Nawal Alioua , Walid Bouarifi

{"title":"Artificial intelligence-powered visual internet of things in smart cities: A comprehensive review","authors":"Omar El Ghati , Othmane Alaoui-Fdili , Othman Chahbouni , Nawal Alioua , Walid Bouarifi","doi":"10.1016/j.suscom.2024.101004","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.101004","url":null,"abstract":"<div><p>The field of smart cities has seen significant advancements in recent years to improve citizens' quality of life. Technologies such as the Internet of Things (IoT) and Edge Computing (EC), along with Artificial Intelligence (AI), are being utilized to achieve this goal. This study focuses on a specific branch of IoT known as Visual IoT, which uses digital cameras as sensors and relies on visual data. Advances in AI have enabled researchers to integrate AI models into camera-based edge devices, increasing the use of AI-powered Visual IoT systems in smart cities. However, since the energy consumption in battery-powered systems is naturally a concern, being deployed outdoors for visual data gathering with the integration of AI-based processing raises a significant challenge. This paper examines AI-powered Visual IoT systems in smart cities with a special emphasis on energy efficiency. Our goal is not only to evaluate how AI is used in Visual IoT systems in the context of smart cities but also to evaluate the level of consideration given to the energy efficiency aspect in the reviewed studies. Furthermore, we explore all of the methods used to address it. Through our work, readers will gain insights into the current landscape of Visual IoT in smart cities and an understanding of how much importance is placed on energy consumption in AI-integrated solutions.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 101004"},"PeriodicalIF":4.5,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141290937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering 使用自适应熵秃鹰搜索优化和基于密度的自适应软聚类的 WSN 最佳节能路由选择

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-05-22 DOI: 10.1016/j.suscom.2024.101003

Maravarman Manoharan , Babu Subramani , Pitchai Ramu

{"title":"An optimal energy efficient routing in WSN using adaptive entropy bald eagle search optimization and density based adaptive soft clustering","authors":"Maravarman Manoharan , Babu Subramani , Pitchai Ramu","doi":"10.1016/j.suscom.2024.101003","DOIUrl":"10.1016/j.suscom.2024.101003","url":null,"abstract":"<div><p>Wireless Sensor Network (WSN) uses soft computing techniques to reduce task time consuming and unsolvable energy consumption problems. This study used soft-computing-based methods to demonstrate the best data transfer in WSN. Nodes in a network are initially clustered using density-based Adaptive Soft (DAS) clustering. Afterward, the cluster head (CH) is selected using a modified beetle swarm optimization technique. Distance, energy, trust, and throughput are all considered when deciding on the ideal CH. The node with the highest entropy for data transmission is then determined by calculating each node’s entropy weight values based on these factors. The CH carries out the data aggregation after the data collection from the sensor nodes. Finally, entropy value based bald eagle search (EBES) optimization with an adaptive entropy value is used to perform the finest energy efficient routing, a strategy for the best possible data transmission. The proposed approach attains improved performance than the compared existing approaches in terms of delay (6.5 ms), throughput (320.1 kbps), energy (1.92<span><math><mi>j</mi></math></span>), and packet delivery ratio (218.7%), the work provided is contrasted to the various current methods. The performance of the proposed approach is compared to existing approaches to prove its effectiveness, and it has been proven to perform better than the existing routing approaches.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 101003"},"PeriodicalIF":4.5,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141137196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transactive energy management system for smart grids using Multi-Agent Modeling and Blockchain 使用多代理建模和区块链的智能电网交易能源管理系统

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-05-13 DOI: 10.1016/j.suscom.2024.101001

Maganti Syamala , Uma Gowri , D. Vijendra Babu , A. Sahaya Anselin Nisha , Mohammed Altaf Ahmed , Elangovan Muniyandy

{"title":"Transactive energy management system for smart grids using Multi-Agent Modeling and Blockchain","authors":"Maganti Syamala , Uma Gowri , D. Vijendra Babu , A. Sahaya Anselin Nisha , Mohammed Altaf Ahmed , Elangovan Muniyandy","doi":"10.1016/j.suscom.2024.101001","DOIUrl":"10.1016/j.suscom.2024.101001","url":null,"abstract":"<div><p>Technological approaches for effective energy regulation are required due to incorporating contemporary electrical systems with sustainable power resources. Our article suggests a Transactive Energy Managing System (T.E.M.S.) using Blockchain-based technologies and Multiple-Agent Modelling (M.A.M.) to improve the long-term viability and dependability of energy-efficient grids. The framework utilizes a decentralized methodology enabled by self-governing agents. These units include services, sellers, and customers in the electricity system. Such entities engage in vibrant interactions, trading energy according to current economic circumstances, choices, and facts. Blockchain-based technology promotes a more robust and decentralized power industry by eliminating the demand for centralized mediators and improving information security. The suggested T.E.M.S. aims to tackle issues such as demand-response administration, incorporation of sustainable energy resources, and grid reliability. The efficiency of the mechanism in maximizing power use, lowering load spikes, and fostering an improved power environment is proved via modelling and evaluation. The research advances intelligent grid technology by providing an extensive, decentralized approach that aligns with the changing power industry. In this era of connected layouts, integrating Blockchain-based technologies with Multiple-Agent Modelling offers a solid basis for creating flexible and adaptable power control technologies.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 101001"},"PeriodicalIF":4.5,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141039752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Low power content addressable memory designing and implementation using voltage swing self adjustable match line technique 利用电压摆动自调节匹配线技术设计和实现低功耗内容可寻址存储器

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-05-11 DOI: 10.1016/j.suscom.2024.101002

Saidulu Inamanamelluri, D. Dhanasekaran, Radhika Bhaskar

{"title":"Low power content addressable memory designing and implementation using voltage swing self adjustable match line technique","authors":"Saidulu Inamanamelluri, D. Dhanasekaran, Radhika Bhaskar","doi":"10.1016/j.suscom.2024.101002","DOIUrl":"10.1016/j.suscom.2024.101002","url":null,"abstract":"<div><p>One of the essential components of computer systems is memory. A primary hindrance in this regard is the memory speed. Content Addressable Memory (CAM) speeds up transformations and table lookups in network routers and data processing systems for hardware search engines. Parallel seeks using the CAM (Content Addressable Memory) model are often used to enhance memory performance. This paper uses the voltage swing self-adjustable match line (VSSA-ML) technique to describe low-power content addressable memory design and implementation. This project decreases Match Line (ML) power loss by reducing load capacitance and ML voltage swing. A simple ML voltage detector is proposed instead of the complex, fully different detector that allows ML voltage swings near zero. This paper presents 6 T 8×8 CAM arrays using VSSA-ML Technique using Tanner tools 45-nm technology. On the other hand, this design enhances robustness in processing variations by self-adjusting voltage swings. Implementation analysis states that the described mode 6 T 8×8 CAM design utilized fewer MOSFETs than the 8 T 8×8 CAM array.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 101002"},"PeriodicalIF":4.5,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141025745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Coordinating electric vehicle charging with multiagent deep Q-networks for smart grid load balancing 利用多代理深度 Q 网络协调电动汽车充电，实现智能电网负载平衡

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-05-03 DOI: 10.1016/j.suscom.2024.100993

Lakshmana Phaneendra Maguluri , A. Umasankar , D. Vijendra Babu , A. Sahaya Anselin Nisha , M. Ramkumar Prabhu , Shouket Ahmad Tilwani

{"title":"Coordinating electric vehicle charging with multiagent deep Q-networks for smart grid load balancing","authors":"Lakshmana Phaneendra Maguluri , A. Umasankar , D. Vijendra Babu , A. Sahaya Anselin Nisha , M. Ramkumar Prabhu , Shouket Ahmad Tilwani","doi":"10.1016/j.suscom.2024.100993","DOIUrl":"10.1016/j.suscom.2024.100993","url":null,"abstract":"<div><p>Integrating EVs (Electric Vehicles) with the electrical system presents essential load distribution difficulties because EV recharging structures are unpredictable and variable. The article presents an innovative technique employing multiple-agent deeper Q-Networking (MADQN) to coordinate electric automobiles and improve the electricity system balance of load. The suggested MADQN simulation rapidly optimizes battery charge plans by utilizing the capabilities of multiple agent networks as well as deeper reinforced learning. The framework adjusts to current network situations utilizing cooperative decision-making between substances, considering variables like a need for power, accessibility to green energy sources, and protection of the arrangement. Beneficial load distribution is made possible when reducing expenses and ecological damage because of the system's capacity to gather data from and modify intricate, changing circumstances. The findings from the modelling indicate how well the suggested MADQN method works to enhance network efficiency, lower peak usage, and use more sustainable power resources. These factors help build a more robust, adaptable, intelligent grid environment.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 100993"},"PeriodicalIF":4.5,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141027185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of Things 面向物联网高效任务卸载的三层 D2D 边缘云计算架构的高级深度强化学习算法

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-05-01 DOI: 10.1016/j.suscom.2024.100992

Komeil Moghaddasi , Shakiba Rajabi , Farhad Soleimanian Gharehchopogh , Ali Ghaffari

{"title":"An advanced deep reinforcement learning algorithm for three-layer D2D-edge-cloud computing architecture for efficient task offloading in the Internet of Things","authors":"Komeil Moghaddasi , Shakiba Rajabi , Farhad Soleimanian Gharehchopogh , Ali Ghaffari","doi":"10.1016/j.suscom.2024.100992","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100992","url":null,"abstract":"<div><p>The Internet of Things (IoTs) has transformed the digital landscape by interconnecting billions of devices worldwide, paving the way for smart cities, homes, and industries. With the exponential growth of IoT devices and the vast amount of data they generate, concerns have arisen regarding efficient task-offloading strategies. Traditional cloud and edge computing methods, paired with basic Machine Learning (ML) algorithms, face several challenges in this regard. In this paper, we propose a novel approach to task offloading in a Device-to-Device (D2D)-Edge-Cloud computing using the Rainbow Deep Q-Network (DQN), an advanced Deep Reinforcement Learning (DRL) algorithm. This algorithm utilizes advanced neural networks to optimize task offloading in the three-tier framework. It balances the trade-offs among D2D, Device-to-Edge (D2E), and Device/Edge-to-Cloud (D2C/E2C) communications, benefiting both end users and servers. These networks leverage Deep Learning (DL) to discern patterns, evaluate potential offloading decisions, and adapt in real time to dynamic environments. We compared our proposed algorithm against other state-of-the-art methods. Through rigorous simulations, we achieved remarkable improvements across key metrics: an increase in energy efficiency by 29.8%, a 27.5% reduction in latency, and a 43.1% surge in utility.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 100992"},"PeriodicalIF":4.5,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140906411","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy efficiency enhancement in millimetre-wave MIMO-NOMA using three layer user grouping and adaptive power allocation algorithm 利用三层用户分组和自适应功率分配算法提高毫米波 MIMO-NOMA 的能效

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-04-24 DOI: 10.1016/j.suscom.2024.100991

K. Ramesh Chandra , Somasekhar Borugadda

{"title":"Energy efficiency enhancement in millimetre-wave MIMO-NOMA using three layer user grouping and adaptive power allocation algorithm","authors":"K. Ramesh Chandra , Somasekhar Borugadda","doi":"10.1016/j.suscom.2024.100991","DOIUrl":"10.1016/j.suscom.2024.100991","url":null,"abstract":"<div><p>Massive multi-input multi-output (MIMO) is realized as the principal technology in the emerging fifth generation communication network system. Hybrid structure uplink communication is considered for the MIMO Non-orthogonal multiple access (MIMO-NOMA) system’s beam forming and power efficiency improvement through the novel three-layer user grouping. In the three-layer user grouping, the K-means algorithm is adopted in the initial layer for grouping users among different clusters and rectifying clustering errors in the third layer. The second layer used the agglomerative nesting (AGNES) algorithm for merging smaller clusters based on the channel correlation and angles of arrival similarity. The beam selection is carried out to minimize the intrusion of defined beam elements and to overcome beam overlapping problems. The non-convex optimization of the power allocating problem is modified as a convex problem by introducing a Quadratic transform (QT) to minimize each user’s data rate requirement. The algorithm of coati optimization is proposed to iteratively optimize the power allocation problem. The simulation results show that our proposed methodology goes beyond the existing schemes in terms of energy efficiency beyond the maximum power and achievable sum rate can be achieved.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 100991"},"PeriodicalIF":4.5,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140790307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Development of an IoT smart energy meter with power quality features for a smart grid architecture 为智能电网架构开发具有电能质量功能的物联网智能电表

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-04-20 DOI: 10.1016/j.suscom.2024.100990

Omar Munoz, Adolfo Ruelas, Pedro F. Rosales-Escobedo, Alexis Acuña, Alejandro Suastegui, Fernando Lara, Ruben A. Reyes-Zamora, Angel Rocha

{"title":"Development of an IoT smart energy meter with power quality features for a smart grid architecture","authors":"Omar Munoz, Adolfo Ruelas, Pedro F. Rosales-Escobedo, Alexis Acuña, Alejandro Suastegui, Fernando Lara, Ruben A. Reyes-Zamora, Angel Rocha","doi":"10.1016/j.suscom.2024.100990","DOIUrl":"10.1016/j.suscom.2024.100990","url":null,"abstract":"<div><p>Electricity consumption has been intensifying due to population growth, climate change, urbanization, and the growing use of electronic devices, which are increasingly non-linear loads that cause poor power quality conditions. The trend of the Internet of Things has led to the creation of devices that encourage the efficient and effective utilization of electrical power. This in turn facilitates the development of modern power distribution structures such as smart grids. Consequently, this paper presents in detail the design, construction, and validation of a three-phase IoT smart meter intended to form part of the end-user demand side of a smart grid. The compact embedded system, with a manufacturing cost below $80 USD, features a unique electronic design that enables its installation in any load center and employs a straightforward IoT structure that includes WiFi technology for Internet communication. Also, a deployed web application was developed specifically to display the smart meter measurements. Unlike other smart meters, the proposed meter not only provides the amount of active energy consumption, but total and fundamental RMS current and voltage, active, reactive, and apparent power, reactive energy, power factor, and some power quality parameters such as, line frequency, amplitude of 64 current harmonics, and total harmonic distortion. Additionally, this study shows that the prototype achieves an absolute error of less than 1% in all its measurements. Finally, real-life applications of the developed device are demonstrated in residential environments.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 100990"},"PeriodicalIF":4.5,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140768592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ETFC: Energy-efficient and deadline-aware task scheduling in fog computing ETFC：雾计算中的高能效和截止时间感知任务调度

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-04-16 DOI: 10.1016/j.suscom.2024.100988

Amir Pakmehr, Majid Gholipour, Esmaeil Zeinali

{"title":"ETFC: Energy-efficient and deadline-aware task scheduling in fog computing","authors":"Amir Pakmehr, Majid Gholipour, Esmaeil Zeinali","doi":"10.1016/j.suscom.2024.100988","DOIUrl":"10.1016/j.suscom.2024.100988","url":null,"abstract":"<div><p>The Internet of Things (IoT) is constantly evolving and expanding. However, due to the limited IoT resources, it is intertwined with fog computing to use their resources to compensate for the limitations of IoT resources. On the other hand, fog devices face challenges, such as resource heterogeneity, high distribution, dynamism, and limitations, so an efficient task scheduling approach is needed to deploy fog computing resources effectively and improve the quality of service (QoS). This work mathematically formulates the task scheduling problem to minimize energy consumption and cost and improve QoS by reducing response time and deadline violation times of IoT tasks. Then, it proposes an Energy-efficient and deadline-Aware Task scheduling in Fog Computing (ETFC) method that predicts the traffic of fog nodes by a Support Vector Machine (SVM) and divides them into low-traffic and high-traffic groups. Next, the ETFC method schedules the low-traffic part with an algorithm based on reinforcement learning using the proposed ICLA-SOA, which is an algorithm based on irregular cellular learning automata and schedules the tasks of the high-traffic part with a metaheuristic algorithm using the proposed Non-dominated Sorting Genetic Algorithm (NSGA-III). The simulation results demonstrate that the ETFC method exhibits up to an 84 % enhancement in response time, up to a 33 % reduction in energy consumption, up to a 30 % decrease in costs, and up to a 28 % advancement in meeting task deadlines compared to other methods.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"43 ","pages":"Article 100988"},"PeriodicalIF":4.5,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140788029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A hybrid fennec fox and sand cat optimization algorithm for clustering scheme in VANETs 用于 VANET 聚类方案的狐狸和沙猫混合优化算法

IF 4.5 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2024-04-01 DOI: 10.1016/j.suscom.2024.100983

V. Krishna Meera , C. Balasubramanian

{"title":"A hybrid fennec fox and sand cat optimization algorithm for clustering scheme in VANETs","authors":"V. Krishna Meera , C. Balasubramanian","doi":"10.1016/j.suscom.2024.100983","DOIUrl":"https://doi.org/10.1016/j.suscom.2024.100983","url":null,"abstract":"<div><p>The popularity of intelligent vehicles with cutting-edge vehicular applications has fueled the rapid expansion of Vehicular Ad hoc Networks (VANETs) in recent years. VANETs are a network of vehicles designed to exchange and explore real-time data using a well-developed and effectively organized data transport technology. However, the major issue of dynamic topology and cluster stability always has an impact on choosing an optimal path between the cars. At this point, an intelligent clustering technique in VANETs that handles dynamic topology and cluster stability is critical for efficient route selection between vehicular nodes. This is an NP-hard issue that can be effectively solved using an intelligent nature-inspired algorithm that can discover near-optimal solutions in the search space. An Intelligent Hybrid Fennec Fox and Sand Cat Optimization Algorithm (HFFSCOA) -Based Clustering Scheme is proposed in this paper as a novel route clustering optimization strategy that takes grid size, orientation, velocity node density, and communication range into account while achieving its goal. This HFFSCOA contributed to the route clustering process, which determines dependable and optimal routes between vehicular nodes for the purpose of building and evaluating ideal Cluster Heads (CHs) in the network. HFFSCOA's findings clearly demonstrated its usefulness and efficacy in terms of the number of vehicles, network size, changeable communication ranges, and number of clusters built in the network. The statistical results of HFFSCOA also confirmed an enhanced cluster Optimization rate of 56.21% and an increased cluster stability of 92.34.</p></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"42 ","pages":"Article 100983"},"PeriodicalIF":4.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140350846","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0