Sustainable Computing-Informatics & Systems最新文献_第2页

A parallel computational approach for energy-efficient hydraulic analysis of water distribution networks using learning automata 基于学习自动机的配水管网节能水力分析并行计算方法

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-10 DOI: 10.1016/j.suscom.2025.101135

Ali Suvizi, Ruhollah Ahmadi, Morteza Saheb Zamani, Mohammad Reza Meybodi

{"title":"A parallel computational approach for energy-efficient hydraulic analysis of water distribution networks using learning automata","authors":"Ali Suvizi, Ruhollah Ahmadi, Morteza Saheb Zamani, Mohammad Reza Meybodi","doi":"10.1016/j.suscom.2025.101135","DOIUrl":"10.1016/j.suscom.2025.101135","url":null,"abstract":"<div><div>The hydraulic analysis of water distribution networks (WDNs) is crucial for ensuring efficient management of water resources, a key aspect of sustainable urban development. Formulation and steady-state hydraulic analysis of these networks have been conducted using both numerical and non-numerical methods. WDN hydraulic equations are complex and non-linear, requiring multiple executions, making their hydraulic analysis computationally demanding and energy intensive. This paper introduces an energy-efficient parallel computing approach using learning automata to significantly enhance the speed and energy efficiency of hydraulic analysis. By employing a cellular automaton framework that reflects the WDN structure, and a solution methodology based on the Taylor series enhanced with learning automata, we propose a system that reduces computational time and energy consumption. We compare the performance of our proposed approach with the EPANET software across networks of varying complexity and topologies. The results suggest our parallel algorithm not only accelerate the hydraulic analysis process up to 60 times compared to existing methods, but also significantly decrease the energy consumption, highlighting its potential for sustainable water management practices.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101135"},"PeriodicalIF":3.8,"publicationDate":"2025-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071617","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-efficient transfer learning for water consumption forecasting

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-07 DOI: 10.1016/j.suscom.2025.101130

A. Gil-Gamboa, J.F. Torres, F. Martínez-Álvarez, A. Troncoso

{"title":"Energy-efficient transfer learning for water consumption forecasting","authors":"A. Gil-Gamboa, J.F. Torres, F. Martínez-Álvarez, A. Troncoso","doi":"10.1016/j.suscom.2025.101130","DOIUrl":"10.1016/j.suscom.2025.101130","url":null,"abstract":"<div><div>Artificial intelligence is expanding at an unprecedented rate due to the numerous advantages it provides to all types of businesses and industries. Water utilities are adopting artificial intelligence models to optimize water management in cities nowadays. However, the substantial computational demands of artificial intelligence present challenges, particularly regarding energy consumption and environmental impact. This paper addresses this problem by proposing a transfer learning approach for water consumption forecasting that reduces computational time, energy usage, and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions. The proposed methodology consists in developing a transfer learning approach based on a deep learning model already trained for a task with similar characteristics such as predicting electricity consumption. Thus, a pre-trained deep learning model designed for electricity consumption prediction is adapted to the water consumption domain, leveraging shared characteristics between these tasks. Experiments are conducted to determine the optimal amount of knowledge transfer and compare the performance of this approach with other state-of-the-art time-series forecasting models. Using real data from a water company in Spain, the transfer learning model achieves a similar or better accuracy than the other methods, while demonstrating significantly lower computational times, energy consumption and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emissions. In addition, a scalability analysis has been conducted leading to the conclusion that the proposed transfer learning model is highly suitable to deal with big data. These findings highlight the potential of transfer learning as a sustainable and scalable solution for big data challenges in water management systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101130"},"PeriodicalIF":3.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943214","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

Deep learning BiLSTM and Branch-and-Bound based multi-objective virtual machine allocation and migration with profit, energy, and SLA constraints 基于利润、能量和SLA约束的深度学习BiLSTM和Branch-and-Bound多目标虚拟机分配和迁移

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-05-05 DOI: 10.1016/j.suscom.2025.101128

Neeraj Kumar Sharma , Sriramulu Bojjagani , Ravi Uyyala , Anup Kumar Maurya , Saru Kumari

{"title":"Deep learning BiLSTM and Branch-and-Bound based multi-objective virtual machine allocation and migration with profit, energy, and SLA constraints","authors":"Neeraj Kumar Sharma , Sriramulu Bojjagani , Ravi Uyyala , Anup Kumar Maurya , Saru Kumari","doi":"10.1016/j.suscom.2025.101128","DOIUrl":"10.1016/j.suscom.2025.101128","url":null,"abstract":"<div><div>This paper highlights a novel approach to address multiple networking-based VM allocation and migration objectives at the cloud data center. The proposed approach in this paper is structured into three distinct phases: firstly, we employ a Bi-Directional Long Short Term Memory (BiLSTM) model to predict Virtual Machines (VMs) instance’s prices. Subsequently, we formulate the problem of allocating VMs to Physical Machines (PMs) and switches in a network-aware cloud data center environment as a multi-objective optimization task, employing Linear Programming (LP) techniques. For optimal allocation of VMs, we leverage the Branch-and-Bound (BaB) technique. In the third phase, we implement a VM migration strategy sensitive to SLA requirements and energy consumption considerations. The results, conducted using the CloudSim simulator, demonstrate the efficacy of our approach, showcasing a substantial 35% reduction in energy consumption, a remarkable decrease in SLA violations, and a notable 18% increase in the cloud data center’s profit. Finally, the proposed multi-objective approach reduces energy consumption and SLA violation and makes the data center sustainable.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101128"},"PeriodicalIF":3.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143923424","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

Corrigendum to “An energy efficient and secure model using chaotic levy flight deep Q-learning in healthcare system” [Sustain. Comput.: Inform. Syst. 39 (2023) 100894] “在医疗保健系统中使用混沌征费飞行深度q学习的节能和安全模型”的更正[可持续]。第一版。:通知。系统39 (2023)100894]

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-28 DOI: 10.1016/j.suscom.2025.101131

V. Gowri , B. Baranidharan

引用次数: 0

A predominant intrusion detection system in IIoT using ELCG-DSA AND LWS-BiOLSTM with blockchain 基于ELCG-DSA和LWS-BiOLSTM的工业物联网入侵检测系统

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-19 DOI: 10.1016/j.suscom.2025.101127

Basava Ramanjaneyulu Gudivaka , Rajya Lakshmi Gudivaka , Raj Kumar Gudivaka , Dinesh Kumar Reddy Basani , Sri Harsha Grandhi , Sundarapandian Murugesan , M.M. Kamruzzaman

{"title":"A predominant intrusion detection system in IIoT using ELCG-DSA AND LWS-BiOLSTM with blockchain","authors":"Basava Ramanjaneyulu Gudivaka , Rajya Lakshmi Gudivaka , Raj Kumar Gudivaka , Dinesh Kumar Reddy Basani , Sri Harsha Grandhi , Sundarapandian Murugesan , M.M. Kamruzzaman","doi":"10.1016/j.suscom.2025.101127","DOIUrl":"10.1016/j.suscom.2025.101127","url":null,"abstract":"<div><div>The growing connectivity of Industrial Internet of Things (IIoT) systems has increased cyber threats, necessitating early detection of intrusions. However, existing systems often lack focus on intermediate and continuous multifactor authorization between IIoT and Industrial Control Systems (ICS). To overcome this, an efficient IDS for IIoT using an Exponential Linear Congruential Generator - Digital Signature Algorithm (ELCG-DSA) and Log Wave Sigmoid-Bidirectional Once Long Short-Term Memory (LWS-BiOLSTM) is proposed. Initially, the industry and vehicle details are registered in the blockchain network, and the Polychoric Entropy Correlation-Tiger Hashing Algorithm (PEC-Tiger) generates hash codes through smart contract creation. From the generated hash codes, a partial digital signature is created by using the ELCG-DSA technique. After login, the registered details are processed for enhancing security using Montgomery Modulo Curve Cryptography (MMCC). Then, the details are verified by using PEC-Tiger, and if the hash code matches, the key generation centre is notified for the creation of a fully digital signature. After verification, the Luus–Jaakola Sequence-based Pelican Optimization Algorithm (LJS-POA) is applied for load balancing. Next, the data security is verified in the IDS training set, in which the features are extracted from preprocessed data. Then, the Synthetic Minority Oversampling Technique (SMOTE) is utilized for data balancing, and LWS-BiOLSTM is implemented to classify attacks. Furthermore, the attacked data is blocked, and non-attacked data is stored in the ICS through digital signature verification. Thus, the experimental results of the proposed framework outperform the other conventional techniques by achieving 98.78 % accuracy and 98.71 % security level.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101127"},"PeriodicalIF":3.8,"publicationDate":"2025-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873980","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

Sustainable energy harvesting techniques for underwater aquatic systems with multi-source and low-energy solutions 具有多源和低能量解决方案的水下水生系统的可持续能量收集技术

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-11 DOI: 10.1016/j.suscom.2025.101126

S. Jayanthi , R. Lakshmana Kumar , P. Punitha , BalaAnand Muthu , C.B. Sivaparthipan

{"title":"Sustainable energy harvesting techniques for underwater aquatic systems with multi-source and low-energy solutions","authors":"S. Jayanthi , R. Lakshmana Kumar , P. Punitha , BalaAnand Muthu , C.B. Sivaparthipan","doi":"10.1016/j.suscom.2025.101126","DOIUrl":"10.1016/j.suscom.2025.101126","url":null,"abstract":"<div><div>Underwater Internet of Things (IoT) systems are essential for monitoring and conserving aquatic ecosystems. These systems are deployed with limited energy resources, harsh environmental conditions and high energy consumption during communication. Standalone solar or wave-based energy harvesting techniques are insufficient because of ecological conditions and variable energy availability. In addition, conventional communication protocols are power-hungry and limit the operation time of underwater nodes. This work introduces a strong, energy-efficient combination of Multi-Source Energy Harvesting Systems and Low-Energy Communication Protocols. The proposed approach will ensure constant energy flow irrespective of the submarine's changing environment by intermixing wave energy with thermal energy, microbial fuel cells, and backup solar systems. The supplementary alternative energy sources avoid the need for batteries, which could result in sustainable operations. Additionally, incorporating low-power communication techniques such as Frequency Shift Keying (FSK) and sleep-wake scheduling significantly reduces energy consumption during data transmission. It is the most power-intensive operation in IoT networks. The proposed work addresses gaps in existing energy harvesting and communication frameworks by optimizing energy generation and consumption. This dual approach enhances the sustainability of underwater IoT systems and improves reliability in diverse and unpredictable aquatic environments. The proposed low-energy communication protocol achieves a transmission success rate of 98.5 %, energy harvesting efficiency of 90 %, and a battery lifetime of over 72 h, optimized for underwater environments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101126"},"PeriodicalIF":3.8,"publicationDate":"2025-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143851450","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

Deep reinforcement learning-driven intelligent portfolio management with green computing: Sustainable portfolio optimization and management 基于绿色计算的深度强化学习驱动的智能投资组合管理：可持续投资组合优化与管理

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-07 DOI: 10.1016/j.suscom.2025.101125

Yi Xu

引用次数: 0

A tri-objective model for cloudlet server placement problem in wireless metropolitan area networks 无线城域网中云服务器布局问题的三目标模型

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-05 DOI: 10.1016/j.suscom.2025.101124

Bahareh Bahrami , Mohammad Reza Khayyambashi

{"title":"A tri-objective model for cloudlet server placement problem in wireless metropolitan area networks","authors":"Bahareh Bahrami , Mohammad Reza Khayyambashi","doi":"10.1016/j.suscom.2025.101124","DOIUrl":"10.1016/j.suscom.2025.101124","url":null,"abstract":"<div><div>To reduce latency and save energy, cloudlet computing enables tasks to be offloaded from user equipment to Cloudlet Servers (CSs). Determining the optimal number of CSs and the appropriate locations for their placement are two major challenges in building an efficient computing platform. Placing a CS at the closest location to the user can improve the QoS. Additionally, providing additional CSs to cover each user ensures that the user's needs are met even if the designated server is unable to provide services. However, to minimize energy consumption and costs, service providers tend to use a minimum number of CSs. Since the coverage zones of different CSs may overlap, fewer additional servers need to be deployed in such areas. This paper examines the problem of CS placement in a Wireless Metropolitan Area Network (WMAN) and introduces a three-objective model that aims to optimize transmission distance, coverage with overlap control, and energy consumption. To obtain an appropriate Pareto front, the performance of the NSGA-II, binary MOPSO, and binary MOGWO algorithms is examined through four different scenarios under the Shanghai Telecom dataset. Comparing the results of the Hyper-Volume (HV) indicator reveals that the NSGA-II algorithm has higher values in all studied scenarios. A higher HV value means that the solution set is closer to an optimal Pareto set. In the best and worst case, the HV values for the NSGA-II were equal to 0.2275 and 0.1883, respectively.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101124"},"PeriodicalIF":3.8,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143799113","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

IoT-based optical sensor network for precision agriculture 面向精准农业的物联网光传感器网络

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-04 DOI: 10.1016/j.suscom.2025.101112

Amit Sharma , Diksha Srivastava , Ramkumar Krishnamoorthy , Sanjay Kumar Sinha , P. Jhansirani , Amit barve

{"title":"IoT-based optical sensor network for precision agriculture","authors":"Amit Sharma , Diksha Srivastava , Ramkumar Krishnamoorthy , Sanjay Kumar Sinha , P. Jhansirani , Amit barve","doi":"10.1016/j.suscom.2025.101112","DOIUrl":"10.1016/j.suscom.2025.101112","url":null,"abstract":"<div><div>Precision agriculture is a modern agricultural method that employs state-of-the-art technology and data-driven decision-making to increase yields. In this context, there is much potential to improve agricultural operations by integrating Internet of Things devices and optical sensors. The accurate data extraction and analysis provided by sensor networks and Machine Learning based tracking devices are in high demand. This study aims to promote intelligent farming while lowering agricultural risks. Insects and other pathogens can cause plant illnesses, which may decrease yield output if not handled promptly. Therefore, in this research, we provide a novel Artificial Swarm Fish Optimized Naïve Bayes technique to monitor the soil's quality and guard against diseases that affect cotton leaves. The present study uses Internet of Things devices with optical sensors to track several metrics vital to crop development and health. These sensors record information about temperature, humidity, light intensity, chlorophyll content, and other important environmental variables. The acquired data is then wirelessly communicated to a centralized server, where the suggested approach is used to process and analyze the data. After identifying the infection, through an Android app. Soil parameter like humidity, temperature, and moisture may be presented with the chemical level in a container using the Android app. The power source and chemical sprinkler system may be managed by turning the relay on or off using an Android app. The experimental results show that the suggested strategy performs better when compared to conventional methods of illness detection.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101112"},"PeriodicalIF":3.8,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906112","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

Optimizing packet routing and security in MANETs with the H-MAntnetSVM algorithm for energy efficiency and blackhole detection 利用H-MAntnetSVM算法优化manet中的数据包路由和安全性，以提高能源效率和黑洞检测

IF 3.8 3区计算机科学

Sustainable Computing-Informatics & Systems Pub Date : 2025-04-03 DOI: 10.1016/j.suscom.2025.101123

Kalaiselvi Gopalasamy , Kavitha Govindarajan Muthaiya

{"title":"Optimizing packet routing and security in MANETs with the H-MAntnetSVM algorithm for energy efficiency and blackhole detection","authors":"Kalaiselvi Gopalasamy , Kavitha Govindarajan Muthaiya","doi":"10.1016/j.suscom.2025.101123","DOIUrl":"10.1016/j.suscom.2025.101123","url":null,"abstract":"<div><div>With increasing use of mobile adhoc networks (MANETs) in various applications, the demand for powerful routing protocols has also surged to cater for possible failures or security threats within the network. The operation of efficient packet routing proves highly essential in maintaining reliable communication in MANETs. Energy efficiency plays a crucial role in determining the suitability of a routing technique in MANETS. Packet transmission can be threatened by many types of attacks as Black Hole, gray hole, and sybil attacks. This research proposes a novel hybrid Antnet and Support Vector Machine-The H-MAntnetSVM routing algorithm for energy-efficient routing and Black Hole detection of optimal routing solution. It is basically an adaptive machine learning algorithm that makes the overall network function more effectively in shifting scenarios. The Antnet protocol increases energy usage and provides effective packet routing, while the integration of SVM identifies alibes and basically isolates them so that they do not disturb the routing process. The results obtained with the proposed method show 92.31 % accuracy in detecting the Black Hole attack and 75 % improvement in throughput along with 13.34 % enhancement in the packet delivery ratio. This gives a prominent development in terms of network performance and safety against Black Hole threats.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101123"},"PeriodicalIF":3.8,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143835145","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