Sustainable Computing-Informatics & Systems最新文献

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"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}

{"title":"Secured Energy Efficient Chaotic Gazelle based Optimized Routing Protocol in mobile ad-hoc network","authors":"Gajendra Kumar Ahirwar,&nbsp;Ratish Agarwal,&nbsp;Anjana Pandey","doi":"10.1016/j.suscom.2025.101086","DOIUrl":"10.1016/j.suscom.2025.101086","url":null,"abstract":"<div><div>In this research, a Secured Energy Efficient Chaotic Gazelle based Optimized Routing Protocol (SE²CG-ORP) is proposed to enhance the security for routing. The Feistel Structured Tiny Encryption Scheme (FS_TES) performs encryption after the data packets are initially created to enhance their secrecy and security. The nodes are then grouped using the K-Means Clustering technique to reduce network communication lag. The Type-II Fuzzy-C-Means technique considers high energy, trust value, and node centrality when selecting the cluster leader. The chosen cluster head sends the data packets to the base station using the Secured Energy Efficient Chaotic Gazelle-based Optimized Routing Protocol (SE2CG-ORP). Here, the residual energy and node distance parameters are satisfied using the Chaotic Gazelle Optimization (CGO) method to identify the most effective route for data transmission. The proposed model is compared to several current models in the results section using a variety of performance metrics, including PDR, residual energy, throughput, encryption and decryption times, delays, and network lifespan. By varying the number of rounds, the proposed approach obtained 62 Mbps, 96.65 %, and 92.07 % of throughput, residual energy, and PDR. Moreover, 0.77 ms of delay is obtained by varying the number of nodes. The PDR value of 79 % and the network lifespan of 1473.63 h were acquired by varying the number of nodes. The consumed energy of the network is 44.59 J, while the encryption and decryption times are 1831.36 ms and 1641.48 ms.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101086"},"PeriodicalIF":3.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172632","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}

{"title":"Optimizing IoT network lifetime through an enhanced hybrid energy harvesting system","authors":"Sirine Rabah ,&nbsp;Aida Zaier ,&nbsp;Sandra Sendra ,&nbsp;Jaime Lloret ,&nbsp;Hassen Dahman","doi":"10.1016/j.suscom.2025.101081","DOIUrl":"10.1016/j.suscom.2025.101081","url":null,"abstract":"<div><div>The growing need for sustainable and renewable energy sources has become critical with the Internet of Things (IoT) advancement. IoT relies on low-power, battery-operated devices, but the limited lifespan of these batteries requires frequent recharging or replacement, which is costly and time-consuming. Researchers have proposed energy harvesting systems that capture sustainable ambient energy from the environment to address this issue. This paper presents a hybrid system for harvesting sustainable energy from solar and wind sources. The system features a boost converter controlled by a novel hybrid method combining the Honey Badger Algorithm (HBA) and Harris Hawks Optimization (HHO). This method maximizes power extraction from solar and wind sources, enhancing overall system efficiency. Additionally, the system includes an innovative energy management algorithm that selects the most powerful input source while protecting the storage battery from overcharging or complete depletion, thereby extending its lifespan. The proposed design is validated through MATLAB/Simulink simulations. The HHO–HBA MPPT is compared with existing MPPT methods, evaluating efficiency, battery charge curves, and IoT network energy status. Simulation results show that the proposed approach significantly increases network longevity, offering a cost-effective and sustainable solution for the energy needs of Wireless Sensor Network (WSN)-IoT devices.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101081"},"PeriodicalIF":3.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143171729","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}