首页 > 最新文献

Sustainable Computing-Informatics & Systems最新文献

英文 中文
A hybrid learning technique for intrusion detection system for smart grid
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-27 DOI: 10.1016/j.suscom.2025.101102
Najet Hamdi
Smart grid is becoming more interconnected with external networks as a result of integrating IoT technologies, making its supervisory control and data acquisition (SCADA) vulnerable to serious cyberattacks. Therefore, early detection of suspicious activities is of utmost importance to safeguard SCADA systems. Machine learning (ML) algorithms are effective methods for developing intrusion detection systems. However, developing an efficient and reliable detection system for smart grids remains challenging: Most suggested ML-based intrusion detection methods are based on centralized learning, in which data is collected from smart meters and transferred to a central server for training. Transferring sensitive data adds another burden to safeguarding smart grids, since it may result in significant privacy breaches and data leaks in the event of attacking the central server. In contrast to centralized learning, federated learning (FL) offers data privacy protection. FL is an emerging cooperative learning that enables training between smart devices (clients) using local datasets which are kept on the clients’ sides. The resilience of FL-based detection systems in real-world situations, however, has not yet been examined, as clients may encounter various assaults, resulting in their local datasets having more or fewer attacks than others participating in the learning process. Motivated by this concern, we propose a FL-based intrusion detection for SCADA systems where clients have different attacks. We examine the impact of having missing attacks in local datasets on the performance of FL-based classifier. The experimental findings demonstrate a significant performance degradation of the FL-based model. As a remedy, we suggest a novel learning method – hybrid learning – that combines centralized and federated learning. The experimental results show that the hybrid learning classifier succeeds in identifying unseen attacks.
{"title":"A hybrid learning technique for intrusion detection system for smart grid","authors":"Najet Hamdi","doi":"10.1016/j.suscom.2025.101102","DOIUrl":"10.1016/j.suscom.2025.101102","url":null,"abstract":"<div><div>Smart grid is becoming more interconnected with external networks as a result of integrating IoT technologies, making its supervisory control and data acquisition (SCADA) vulnerable to serious cyberattacks. Therefore, early detection of suspicious activities is of utmost importance to safeguard SCADA systems. Machine learning (ML) algorithms are effective methods for developing intrusion detection systems. However, developing an efficient and reliable detection system for smart grids remains challenging: Most suggested ML-based intrusion detection methods are based on centralized learning, in which data is collected from smart meters and transferred to a central server for training. Transferring sensitive data adds another burden to safeguarding smart grids, since it may result in significant privacy breaches and data leaks in the event of attacking the central server. In contrast to centralized learning, federated learning (FL) offers data privacy protection. FL is an emerging cooperative learning that enables training between smart devices (clients) using local datasets which are kept on the clients’ sides. The resilience of FL-based detection systems in real-world situations, however, has not yet been examined, as clients may encounter various assaults, resulting in their local datasets having more or fewer attacks than others participating in the learning process. Motivated by this concern, we propose a FL-based intrusion detection for SCADA systems where clients have different attacks. We examine the impact of having missing attacks in local datasets on the performance of FL-based classifier. The experimental findings demonstrate a significant performance degradation of the FL-based model. As a remedy, we suggest a novel learning method – hybrid learning – that combines centralized and federated learning. The experimental results show that the hybrid learning classifier succeeds in identifying unseen attacks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101102"},"PeriodicalIF":3.8,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519514","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 extensible lightweight framework for distributed telemetry of microservices
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-26 DOI: 10.1016/j.suscom.2025.101100
Manuel Otero , José María García , Pablo Fernandez
Microservice architectures have become the standard for developing scalable distributed systems that offer significant benefits in managing the integration and evolution of complex applications. However, they face challenges in effectively diagnosing and resolving performance and reliability issues. Traditional centralized telemetry models and cloud-based monitoring platforms often require complex or costly configurations and are not optimized for RESTful microservices. In fact, although the OpenAPI Specification (OAS) has become a key standard for describing microservice APIs, existing telemetry tools do not leverage this information to enhance service analysis and diagnostics. This paper introduces a lightweight and distributed approach to telemetry that uses OAS-based API information, offering an automated, configuration-free system that enables developers and operations teams to perform root cause analysis more efficiently. Moreover, we propose a plugin system to incorporate intelligent behavior into the telemetry system, such as an adaptive proactive alert mechanism when response-time anomalies are detected. By incorporating this extensibility mechanism, the framework paves the way to address issues such as energy consumption and performance, allowing the system to dynamically adjust its monitoring activities to optimize resource usage and minimize the carbon footprint of microservice deployment and execution. This adaptability reduces operational overhead and supports sustainable computing practices. To validate our approach, we present a proof-of-concept in the form of a ready-to-use package for the NodeJS ecosystem, demonstrating that this distributed telemetry model can operate with minimal impact on system performance and resource usage, proving its effectiveness to support more robust and sustainable IT systems.
{"title":"An extensible lightweight framework for distributed telemetry of microservices","authors":"Manuel Otero ,&nbsp;José María García ,&nbsp;Pablo Fernandez","doi":"10.1016/j.suscom.2025.101100","DOIUrl":"10.1016/j.suscom.2025.101100","url":null,"abstract":"<div><div>Microservice architectures have become the standard for developing scalable distributed systems that offer significant benefits in managing the integration and evolution of complex applications. However, they face challenges in effectively diagnosing and resolving performance and reliability issues. Traditional centralized telemetry models and cloud-based monitoring platforms often require complex or costly configurations and are not optimized for RESTful microservices. In fact, although the OpenAPI Specification (OAS) has become a key standard for describing microservice APIs, existing telemetry tools do not leverage this information to enhance service analysis and diagnostics. This paper introduces a lightweight and distributed approach to telemetry that uses OAS-based API information, offering an automated, configuration-free system that enables developers and operations teams to perform root cause analysis more efficiently. Moreover, we propose a plugin system to incorporate intelligent behavior into the telemetry system, such as an adaptive proactive alert mechanism when response-time anomalies are detected. By incorporating this extensibility mechanism, the framework paves the way to address issues such as energy consumption and performance, allowing the system to dynamically adjust its monitoring activities to optimize resource usage and minimize the carbon footprint of microservice deployment and execution. This adaptability reduces operational overhead and supports sustainable computing practices. To validate our approach, we present a proof-of-concept in the form of a ready-to-use package for the NodeJS ecosystem, demonstrating that this distributed telemetry model can operate with minimal impact on system performance and resource usage, proving its effectiveness to support more robust and sustainable IT systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101100"},"PeriodicalIF":3.8,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143552705","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
Fuzzy fair efficiency assessment in network data envelopment analysis models for complex system: An application in sustainable supply chain management
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-25 DOI: 10.1016/j.suscom.2025.101105
Mohammad Tavassoli
Network data envelopment analysis (NDEA) is a common mathematical technique to evaluate the performance of a set of homogeneous decision-making units (DMUs) with a network structure. In an open-series system with a multi-stage structure, it is crucial to determine fair efficiency for each stage when multiple optimal weights exist, so that the stages have incentives to cooperate with each other to achieve the highest possible performance of the entire system. This study suggests a novel approach based on the NDEA model to assess the fair efficiency of an open-series system with a multi-stage structure. Then, to deal with qualitative data and uncertainty in the values of some variables, the suggested NDEA model is developed in a fuzzy setting, using linguistic terms parameterized through fuzzy sets. The proposed method in this study has the following features that cannot be found in previous studies. First, the proposed method can provide a unique and fair efficiency decomposition for the stages of a system at any level of uncertainty while the overall efficiency of the system remains unchanged. Second, the proposed methodology proves that the achieved efficiency decomposition shows a fair trade-off among the stages. Third, the proposed method can provide fair efficiency decomposition in multi-stage systems in the presence of undesirable intermediate outputs, in which undesirable intermediate output can be reused as input after processing. The application of the proposed methodology is justified by two real-case studies that include performance evaluations of 9 tomato paste producer supply chains and 22 home appliance supply chains.
{"title":"Fuzzy fair efficiency assessment in network data envelopment analysis models for complex system: An application in sustainable supply chain management","authors":"Mohammad Tavassoli","doi":"10.1016/j.suscom.2025.101105","DOIUrl":"10.1016/j.suscom.2025.101105","url":null,"abstract":"<div><div>Network data envelopment analysis (NDEA) is a common mathematical technique to evaluate the performance of a set of homogeneous decision-making units (DMUs) with a network structure. In an open-series system with a multi-stage structure, it is crucial to determine fair efficiency for each stage when multiple optimal weights exist, so that the stages have incentives to cooperate with each other to achieve the highest possible performance of the entire system. This study suggests a novel approach based on the NDEA model to assess the fair efficiency of an open-series system with a multi-stage structure. Then, to deal with qualitative data and uncertainty in the values of some variables, the suggested NDEA model is developed in a fuzzy setting, using linguistic terms parameterized through fuzzy sets. The proposed method in this study has the following features that cannot be found in previous studies. <em>First</em>, the proposed method can provide a unique and fair efficiency decomposition for the stages of a system at any level of uncertainty while the overall efficiency of the system remains unchanged. <em>Second</em>, the proposed methodology proves that the achieved efficiency decomposition shows a fair trade-off among the stages. <em>Third</em>, the proposed method can provide fair efficiency decomposition in multi-stage systems in the presence of undesirable intermediate outputs, in which undesirable intermediate output can be reused as input after processing. The application of the proposed methodology is justified by two real-case studies that include performance evaluations of 9 tomato paste producer supply chains and 22 home appliance supply chains.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101105"},"PeriodicalIF":3.8,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143519513","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
Simulation and real-time implementation of a combined control strategy-based shunt active power filter in microgrid
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-21 DOI: 10.1016/j.suscom.2025.101103
Prasanta Kumar Barik , Gauri Shankar , Pradeepta Kumar Sahoo , Sarita Samal
Renewable energy is rapidly being employed in power networks to meet energy demands, changing the traditional power distribution system into a microgrid (MG)-based system. Additionally, nonlinear loads in the MG system have a tendency to produce undesirable power quality (PQ) problems that need to be properly addressed. In the present work, the MG system is designed using solar PV, wind energy, and fuel cell-based distributed generations, and the PQ concerns of the MG system are addressed in the presence of a combined control technique-based shunt active power filter (SAPF). The combined control technique used for the generation of compensating current of SAPF consists of a negative feedback phase locked loop (NFPLL) based modified synchronous reference frame (MSRF) technique for improving the synchronization performance of SAPF, fuzzy inverted error deviation (FIED) based dc-link voltage controller and adaptive fuzzy hysteresis current controller (AFHCC) based switching pulse generation. The conventional MSRF method, HCC methodology, and fuzzy logic controller (FLC) approach are used by the majority of SAPFs to generate the compensating current for SAPF, but these methods do not completely eliminate harmonics. Hence, in this work, a FIED based control approach is used to improve the performance of SAPF by controlling the VDCunder load changing condition. Apart from FIED technique, NFPLL based MSRF technique is used for quickly and accurately extracts the reference signal during load perturbations and AFHCC scheme is used for switching pulse generation. The suggested combined control strategy (NFPLL-MSRF-FIED-AFHCC) is first evaluated on the MATLAB/Simulink environment and then validated on the OPAL-RT 4510 real-time digital simulator platform. The simulation and real-time outcomes show that the proposed technique works effectively in different scenarios.
{"title":"Simulation and real-time implementation of a combined control strategy-based shunt active power filter in microgrid","authors":"Prasanta Kumar Barik ,&nbsp;Gauri Shankar ,&nbsp;Pradeepta Kumar Sahoo ,&nbsp;Sarita Samal","doi":"10.1016/j.suscom.2025.101103","DOIUrl":"10.1016/j.suscom.2025.101103","url":null,"abstract":"<div><div>Renewable energy is rapidly being employed in power networks to meet energy demands, changing the traditional power distribution system into a microgrid (MG)-based system. Additionally, nonlinear loads in the MG system have a tendency to produce undesirable power quality (PQ) problems that need to be properly addressed. In the present work, the MG system is designed using solar PV, wind energy, and fuel cell-based distributed generations, and the PQ concerns of the MG system are addressed in the presence of a combined control technique-based shunt active power filter (SAPF). The combined control technique used for the generation of compensating current of SAPF consists of a negative feedback phase locked loop (NFPLL) based modified synchronous reference frame (MSRF) technique for improving the synchronization performance of SAPF, fuzzy inverted error deviation (FIED) based dc-link voltage controller and adaptive fuzzy hysteresis current controller (AFHCC) based switching pulse generation. The conventional MSRF method, HCC methodology, and fuzzy logic controller (FLC) approach are used by the majority of SAPFs to generate the compensating current for SAPF, but these methods do not completely eliminate harmonics. Hence, in this work, a FIED based control approach is used to improve the performance of SAPF by controlling the <span><math><msub><mrow><mi>V</mi></mrow><mrow><mi>D</mi><mi>C</mi></mrow></msub></math></span>under load changing condition. Apart from FIED technique, NFPLL based MSRF technique is used for quickly and accurately extracts the reference signal during load perturbations and AFHCC scheme is used for switching pulse generation. The suggested combined control strategy (NFPLL-MSRF-FIED-AFHCC) is first evaluated on the MATLAB/Simulink environment and then validated on the OPAL-RT 4510 real-time digital simulator platform. The simulation and real-time outcomes show that the proposed technique works effectively in different scenarios.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101103"},"PeriodicalIF":3.8,"publicationDate":"2025-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511000","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
LESP:A fault-aware internet of things service placement in fog computing
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-13 DOI: 10.1016/j.suscom.2025.101097
Hemant Kumar Apat , Bibhudatta Sahoo
The rapid advancement of 5G networks enables increase adoption of Industrial Internet of Things (IIoT) devices which introduces variety of time-sensitive applications requires low-latency, fault-tolerant, and energy-efficient computing environments. Fog computing infrastructure that extends cloud computing capabilities at the network edge to provide computation, communication, and storage resources. Due to the limited computing capacity of the Fog node, it restricts the number of tasks executed. The other key challenges are the risk of hardware and software failure during task execution. These failures tend to disrupt the configuration of fog computing nodes, affecting the reliability and availability of services. As a result, this can negatively impact the overall performance and service level objectives. The fault-tolerant-based IoT service placement problem in the fog computing environment primarily focuses on optimal placement of IoT services on fog and cloud resources with the objective of maximizing fault tolerance while satisfying network and storage capacity constraints. In this study, we compared different community-based techniques Girvan-Newman and Louvain with Integer Linear Programming (ILP) for fault tolerance in fog computing using the Albert-Barabási network model. In addition, it proposed a novel Louvian based on eigenvector centrality service placement (LESP) to improve conventional Louvian methods. The proposed algorithm is simulated in iFogSim2 simulator under three different scenario such as under 100, 200 and 300 nodes. The simulation results exemplify that LESP improves fault tolerance and energy efficiency, with an average improvement of approximately 20% over Girvan-Newman, 25% over ILP, and 12.33% over Louvain. These improvements underscore LESP’s strong efficiency and capability in improving service availability across a wide range of network configurations.
{"title":"LESP:A fault-aware internet of things service placement in fog computing","authors":"Hemant Kumar Apat ,&nbsp;Bibhudatta Sahoo","doi":"10.1016/j.suscom.2025.101097","DOIUrl":"10.1016/j.suscom.2025.101097","url":null,"abstract":"<div><div>The rapid advancement of 5G networks enables increase adoption of Industrial Internet of Things (IIoT) devices which introduces variety of time-sensitive applications requires low-latency, fault-tolerant, and energy-efficient computing environments. Fog computing infrastructure that extends cloud computing capabilities at the network edge to provide computation, communication, and storage resources. Due to the limited computing capacity of the Fog node, it restricts the number of tasks executed. The other key challenges are the risk of hardware and software failure during task execution. These failures tend to disrupt the configuration of fog computing nodes, affecting the reliability and availability of services. As a result, this can negatively impact the overall performance and service level objectives. The fault-tolerant-based IoT service placement problem in the fog computing environment primarily focuses on optimal placement of IoT services on fog and cloud resources with the objective of maximizing fault tolerance while satisfying network and storage capacity constraints. In this study, we compared different community-based techniques Girvan-Newman and Louvain with Integer Linear Programming (ILP) for fault tolerance in fog computing using the Albert-Barabási network model. In addition, it proposed a novel Louvian based on eigenvector centrality service placement (LESP) to improve conventional Louvian methods. The proposed algorithm is simulated in iFogSim2 simulator under three different scenario such as under 100, 200 and 300 nodes. The simulation results exemplify that LESP improves fault tolerance and energy efficiency, with an average improvement of approximately 20% over Girvan-Newman, 25% over ILP, and 12.33% over Louvain. These improvements underscore LESP’s strong efficiency and capability in improving service availability across a wide range of network configurations.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101097"},"PeriodicalIF":3.8,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143453121","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
Blockchain integrated multi-objective optimization for energy efficient and secure routing in dynamic wireless sensor networks
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-12 DOI: 10.1016/j.suscom.2025.101101
Vidhya Sachithanandam , D. Jessintha , Hariharan Subramani , V. Saipriya
Wireless Sensor Networks (WSNs) form the backbone of many key use cases, from environmental monitoring to healthcare to smart cities. But their use case is limited in terms of energy, latency, scalability, and security. To combat such problems, the paper suggests a new algorithm, the Energy-based Multi-Objective Donkey Smuggler Optimization Algorithm (EM-DSOA). This approach combines multi-aspect optimization and a thin blockchain protocol, making it a one-stop shop to optimize WSN’s efficiency, security, and stability. EM-DSOA as proposed optimizes energy utilization with dynamic clustering and adaptive routing with safe data transfer via blockchain integration. The approach is compared against current best practices like Multi Weight Chicken Swarm Based Genetic Algorithm (MWCSG) and Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization (AHCS-GWO) by simulation examples of different network densities. The results are marked by significant improvement with energy efficiency of 99.13 %, packet loss reduction of 91 percent and throughput increase of 1000 %. The model likewise has very low end-to-end latency, which is perfect for real-time workloads. The study points out that EM-DSOA can be scalable and flexible, with a high performance across diverse and changing scenarios. With an eye towards energy efficiency, low latency and secure communications in the one, the proposed model takes WSN optimization to a new level of knowledge. This is a work that’s not only up to the challenge of technology now but it also serves as a solid basis for future IoT and smart city deployments and will provide long-term, secure networks.
从环境监测、医疗保健到智能城市,无线传感器网络(WSN)是许多关键应用案例的支柱。但是,它们在能源、延迟、可扩展性和安全性方面受到限制。为了解决这些问题,本文提出了一种新算法--基于能量的多目标驴子偷渡者优化算法(EM-DSOA)。该方法结合了多方面优化和薄区块链协议,可一站式优化 WSN 的效率、安全性和稳定性。所提出的 EM-DSOA 通过动态聚类和自适应路由优化了能源利用率,并通过区块链集成实现了安全数据传输。该方法通过不同网络密度的仿真实例,与当前的最佳实践(如基于多权重鸡群的遗传算法(MWCSG)和自适应混合布谷鸟搜索和灰狼优化(AHCS-GWO))进行了比较。结果表明,该模型的能效显著提高了 99.13%,丢包率降低了 91%,吞吐量提高了 1000%。同样,该模型的端到端延迟非常低,非常适合实时工作负载。研究指出,EM-DSOA 具有可扩展性和灵活性,可在各种不断变化的场景中实现高性能。着眼于能源效率、低延迟和安全通信,所提出的模型将 WSN 优化提升到了一个新的高度。这项工作不仅能应对当前的技术挑战,还能为未来的物联网和智慧城市部署奠定坚实的基础,并提供长期、安全的网络。
{"title":"Blockchain integrated multi-objective optimization for energy efficient and secure routing in dynamic wireless sensor networks","authors":"Vidhya Sachithanandam ,&nbsp;D. Jessintha ,&nbsp;Hariharan Subramani ,&nbsp;V. Saipriya","doi":"10.1016/j.suscom.2025.101101","DOIUrl":"10.1016/j.suscom.2025.101101","url":null,"abstract":"<div><div>Wireless Sensor Networks (WSNs) form the backbone of many key use cases, from environmental monitoring to healthcare to smart cities. But their use case is limited in terms of energy, latency, scalability, and security. To combat such problems, the paper suggests a new algorithm, the Energy-based Multi-Objective Donkey Smuggler Optimization Algorithm (EM-DSOA). This approach combines multi-aspect optimization and a thin blockchain protocol, making it a one-stop shop to optimize WSN’s efficiency, security, and stability. EM-DSOA as proposed optimizes energy utilization with dynamic clustering and adaptive routing with safe data transfer via blockchain integration. The approach is compared against current best practices like Multi Weight Chicken Swarm Based Genetic Algorithm (MWCSG) and Adaptive Hybrid Cuckoo Search and Grey Wolf Optimization (AHCS-GWO) by simulation examples of different network densities. The results are marked by significant improvement with energy efficiency of 99.13 %, packet loss reduction of 91 percent and throughput increase of 1000 %. The model likewise has very low end-to-end latency, which is perfect for real-time workloads. The study points out that EM-DSOA can be scalable and flexible, with a high performance across diverse and changing scenarios. With an eye towards energy efficiency, low latency and secure communications in the one, the proposed model takes WSN optimization to a new level of knowledge. This is a work that’s not only up to the challenge of technology now but it also serves as a solid basis for future IoT and smart city deployments and will provide long-term, secure networks.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101101"},"PeriodicalIF":3.8,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563063","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
Novel sustainable green transportation: A neutrosophic multi-objective model considering various factors in logistics 新颖的可持续绿色运输:考虑物流中各种因素的中性多目标模型
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-08 DOI: 10.1016/j.suscom.2025.101096
Kalaivani Kaspar, Palanivel K.
Growing environmental concerns are driving the logistics operations in industry towards sustainable practices, known as green logistics. Optimizing transportation for solid goods are facing challenges to handle complex issues, though traditional methods are often focusing only on single objective like minimizing cost or maximizing the profit. However, to overcome all the possible challenges based on recent requirements, the multi-objective solid transportation problems (MOSTPs) will handle effectively by considering environmental factors like carbon emissions alongside cost and travel time. This research study contributes to the development of robust and eco-friendly transportation solutions by providing a framework for handling uncertainties in MOSTPs. Further, the model influenced in the neutrosophic set (NS) theory, which is an emerging tool to address inherent uncertainties in real-world data associated with environmental impacts and resource limitations. The NS theory incorporates truth-membership, indeterminacy, and falsity-membership functions, allowing for effective modeling of ambiguity. This model presents a Multi-Objective Fixed Charge Solid Transportation Problem (MOFCSTP) using a bi-polar single-valued neutrosophic set to handle all these uncertainties related to green sustainable transportation. Further, different approaches for achieving optimal solutions are explored, including Neutrosophic Compromise Programming Approach (NCPA), M-Pareto Optimal Solution Approach (M-POSA), Weighted Sum Method (WSM), Neutrosophic Goal Programming (NGP), Neutrosophic Global Criterion Method (NGCM), and Fuzzy Goal Programming (FGP). Lastly, the obtained results are then discussed and compared with sensitivity analysis, which is conducted to evaluate the strengths and limitations of each method to justify the effectiveness of the model.
{"title":"Novel sustainable green transportation: A neutrosophic multi-objective model considering various factors in logistics","authors":"Kalaivani Kaspar,&nbsp;Palanivel K.","doi":"10.1016/j.suscom.2025.101096","DOIUrl":"10.1016/j.suscom.2025.101096","url":null,"abstract":"<div><div>Growing environmental concerns are driving the logistics operations in industry towards sustainable practices, known as green logistics. Optimizing transportation for solid goods are facing challenges to handle complex issues, though traditional methods are often focusing only on single objective like minimizing cost or maximizing the profit. However, to overcome all the possible challenges based on recent requirements, the multi-objective solid transportation problems (MOSTPs) will handle effectively by considering environmental factors like carbon emissions alongside cost and travel time. This research study contributes to the development of robust and eco-friendly transportation solutions by providing a framework for handling uncertainties in MOSTPs. Further, the model influenced in the neutrosophic set (NS) theory, which is an emerging tool to address inherent uncertainties in real-world data associated with environmental impacts and resource limitations. The NS theory incorporates truth-membership, indeterminacy, and falsity-membership functions, allowing for effective modeling of ambiguity. This model presents a Multi-Objective Fixed Charge Solid Transportation Problem (MOFCSTP) using a bi-polar single-valued neutrosophic set to handle all these uncertainties related to green sustainable transportation. Further, different approaches for achieving optimal solutions are explored, including Neutrosophic Compromise Programming Approach (NCPA), M-Pareto Optimal Solution Approach (M-POSA), Weighted Sum Method (WSM), Neutrosophic Goal Programming (NGP), Neutrosophic Global Criterion Method (NGCM), and Fuzzy Goal Programming (FGP). Lastly, the obtained results are then discussed and compared with sensitivity analysis, which is conducted to evaluate the strengths and limitations of each method to justify the effectiveness of the model.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101096"},"PeriodicalIF":3.8,"publicationDate":"2025-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437971","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
Federated learning at the edge in Industrial Internet of Things: A review
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-07 DOI: 10.1016/j.suscom.2025.101087
Dinesh kumar sah, Maryam Vahabi, Hossein Fotouhi
The convergence of Federated learning (FL) and Edge computing (EC) has emerged as an essential paradigm, particularly within the Industrial Internet of Things (IIoT) to enable the intelligent decision making. This work diligently examines the current state-of-the-art research at the intersection of FL, EC, and IIoT. An extensive review of the literature explores the diverse applications and challenges associated with this integration. The challenges range from privacy preservation and communication overhead to resource allocation. The incorporation of edge devices at which ensuring the federated learning in distributed manner helps to minimize energy consumption in IIoT, ultimately leads to a sustainable computing environment. By exploring the existing literature and research advancements, our goal is to highlight existing Edge-IoT software and hardware platforms and assess their usability in addressing challenges. In addition, we review existing recent frameworks, methodologies, and models employed to address these challenges, focusing on key performance matrices and its domain such as application, networking, and learning. We highlight the achievements and potential of FL and EC and underscore the need for tailored solutions to suit the unique demands of IIoT. Furthermore, we identify some of the major challenges as opportunities for future research, requires interdisciplinary collaboration and innovative algorithmic solutions. This work can help navigate through the challenges and unlock the full potential, contributing to the advancement of future IIoT applications.
{"title":"Federated learning at the edge in Industrial Internet of Things: A review","authors":"Dinesh kumar sah,&nbsp;Maryam Vahabi,&nbsp;Hossein Fotouhi","doi":"10.1016/j.suscom.2025.101087","DOIUrl":"10.1016/j.suscom.2025.101087","url":null,"abstract":"<div><div>The convergence of Federated learning (FL) and Edge computing (EC) has emerged as an essential paradigm, particularly within the Industrial Internet of Things (IIoT) to enable the intelligent decision making. This work diligently examines the current state-of-the-art research at the intersection of FL, EC, and IIoT. An extensive review of the literature explores the diverse applications and challenges associated with this integration. The challenges range from privacy preservation and communication overhead to resource allocation. The incorporation of edge devices at which ensuring the federated learning in distributed manner helps to minimize energy consumption in IIoT, ultimately leads to a sustainable computing environment. By exploring the existing literature and research advancements, our goal is to highlight existing Edge-IoT software and hardware platforms and assess their usability in addressing challenges. In addition, we review existing recent frameworks, methodologies, and models employed to address these challenges, focusing on key performance matrices and its domain such as application, networking, and learning. We highlight the achievements and potential of FL and EC and underscore the need for tailored solutions to suit the unique demands of IIoT. Furthermore, we identify some of the major challenges as opportunities for future research, requires interdisciplinary collaboration and innovative algorithmic solutions. This work can help navigate through the challenges and unlock the full potential, contributing to the advancement of future IIoT applications.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101087"},"PeriodicalIF":3.8,"publicationDate":"2025-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Enhancing economic and environmental performance of energy communities: A multi-objective optimization approach with mountain gazelle optimizer
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-02-06 DOI: 10.1016/j.suscom.2025.101098
Hong Zheng , Zhixin Wu
This research explores three distinct configurations of energy communities, collectives of local consumers utilizing renewable electrical and thermal energy. The study aims to enhance economic outcomes while addressing climate change and meeting energy demands through advanced strategies. The optimization framework focuses on refining the design, capacity, and efficiency of energy conversion and storage systems, balancing investment and operational costs with greenhouse gas emissions (GhGE) across their lifecycle. Two innovative demand-side management (DSM) strategies are introduced: a downstream pricing-based demand response program (DRP) and an upstream DSM model aligning electricity demand with locally available renewable energy. The study employs a multi-objective modeling approach using the novel mountain gazelle optimizer (MGO), which integrates fuzzy theory and Pareto optimization to minimize costs and emissions. Results demonstrate significant benefits of the proposed DSM strategies. DSM 2 enhances self-consumption rates by approximately 17 % for individual prosumers (IP) and 14–17 % for energy communities, while DSM 1 effectively reduces grid exchanges by 9 % for prosumers and up to 17 % for energy communities. The optimization framework also facilitates a more balanced distribution of generation and demand, alleviating grid stress. These findings underscore the potential of integrated DSM strategies and multi-objective optimization in advancing the performance and sustainability of renewable energy systems, offering diverse advantages in self-consumption and grid interaction.
{"title":"Enhancing economic and environmental performance of energy communities: A multi-objective optimization approach with mountain gazelle optimizer","authors":"Hong Zheng ,&nbsp;Zhixin Wu","doi":"10.1016/j.suscom.2025.101098","DOIUrl":"10.1016/j.suscom.2025.101098","url":null,"abstract":"<div><div>This research explores three distinct configurations of energy communities, collectives of local consumers utilizing renewable electrical and thermal energy. The study aims to enhance economic outcomes while addressing climate change and meeting energy demands through advanced strategies. The optimization framework focuses on refining the design, capacity, and efficiency of energy conversion and storage systems, balancing investment and operational costs with greenhouse gas emissions (GhGE) across their lifecycle. Two innovative demand-side management (DSM) strategies are introduced: a downstream pricing-based demand response program (DRP) and an upstream DSM model aligning electricity demand with locally available renewable energy. The study employs a multi-objective modeling approach using the novel mountain gazelle optimizer (MGO), which integrates fuzzy theory and Pareto optimization to minimize costs and emissions. Results demonstrate significant benefits of the proposed DSM strategies. DSM 2 enhances self-consumption rates by approximately 17 % for individual prosumers (IP) and 14–17 % for energy communities, while DSM 1 effectively reduces grid exchanges by 9 % for prosumers and up to 17 % for energy communities. The optimization framework also facilitates a more balanced distribution of generation and demand, alleviating grid stress. These findings underscore the potential of integrated DSM strategies and multi-objective optimization in advancing the performance and sustainability of renewable energy systems, offering diverse advantages in self-consumption and grid interaction.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101098"},"PeriodicalIF":3.8,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143437972","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 consumption and workload prediction for edge nodes in the Computing Continuum
IF 3.8 3区 计算机科学 Q1 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE Pub Date : 2025-01-28 DOI: 10.1016/j.suscom.2025.101088
Sergio Laso , Pablo Rodríguez , Juan Luis Herrera , Javier Berrocal , Juan M. Murillo
The Computing Continuum paradigm provides developers with a distributed infrastructure for deploying applications through the network, improving performance and energy consumption. However, to maintain applications’ efficiency, their deployment in the Computing Continuum has to be continuously adapted to the varying load of different nodes of the network. In practice, existing support frameworks allow developers to automatically identify how to deploy applications based on the infrastructure status. However, as the application takes time to be deployed, the chosen deployment is outdated once it is applied through the network, as workloads change over time. To address this practical engineering challenge and plan deployments that foresee changes in energy consumption and workload, predictive solutions are needed. To fulfill this need, this work presents the Microservice Energy consumption and Workload Forecaster (MEWF), a prediction system that leverages artificial intelligence techniques to precisely predict CPU usage and energy consumption in varying circumstances. Our practical evaluation over multiple real microservices shows that MEWF improves prediction precision by up to 55% w.r.t. state-of-the-art benchmarks, enabling efficient resource management and demonstrating significant value for real-world deployments.
{"title":"Energy consumption and workload prediction for edge nodes in the Computing Continuum","authors":"Sergio Laso ,&nbsp;Pablo Rodríguez ,&nbsp;Juan Luis Herrera ,&nbsp;Javier Berrocal ,&nbsp;Juan M. Murillo","doi":"10.1016/j.suscom.2025.101088","DOIUrl":"10.1016/j.suscom.2025.101088","url":null,"abstract":"<div><div>The Computing Continuum paradigm provides developers with a distributed infrastructure for deploying applications through the network, improving performance and energy consumption. However, to maintain applications’ efficiency, their deployment in the Computing Continuum has to be continuously adapted to the varying load of different nodes of the network. In practice, existing support frameworks allow developers to automatically identify how to deploy applications based on the infrastructure status. However, as the application takes time to be deployed, the chosen deployment is outdated once it is applied through the network, as workloads change over time. To address this practical engineering challenge and plan deployments that foresee changes in energy consumption and workload, predictive solutions are needed. To fulfill this need, this work presents the Microservice Energy consumption and Workload Forecaster (MEWF), a prediction system that leverages artificial intelligence techniques to precisely predict CPU usage and energy consumption in varying circumstances. Our practical evaluation over multiple real microservices shows that MEWF improves prediction precision by up to 55% w.r.t. state-of-the-art benchmarks, enabling efficient resource management and demonstrating significant value for real-world deployments.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101088"},"PeriodicalIF":3.8,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172633","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
期刊
Sustainable Computing-Informatics & Systems
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1