Sustainable Computing-Informatics & Systems最新文献

{"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}

{"title":"Efficient and adaptive design of RBF neural network for maximum energy harvesting from standalone PV system","authors":"Mohand Akli Kacimi ,&nbsp;Celia Aoughlis ,&nbsp;Toufik Bakir ,&nbsp;Ouahib Guenounou","doi":"10.1016/j.suscom.2025.101083","DOIUrl":"10.1016/j.suscom.2025.101083","url":null,"abstract":"<div><div>This paper deals with a topical topic, the maximum energy harvest of standalone PV system under varying conditions. It introduces a new approach based on the use of artificial intelligence and machine learning to overcome the usual weaknesses of conventional Maximum Power Point Tracking (MPPT) techniques and to improve solutions to meet growing energy demand and further promote sustainable development. The proposal consists of using Radial Basis Function Neural Network (RBFNN) tuned by a PSO algorithm as MPPT controller. The main aim of this combination (RBFNN-PSO) is to achieve the best compromise between the control accuracy and complexity, while using a simple optimization algorithm. This aim is motivated by the potential of the neural networks to learn from any tasks and to generalize the acquired knowledge to other situation never seen before. The proposal reaches a high efficiency and high energy harvesting with a yield greater than 99 %. The performed comparative study with other intelligent techniques from literature prove the superiority and the promising potential of the introduced approach. The developed work presented in this paper is developed with MatLab/Simulink environment.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101083"},"PeriodicalIF":3.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172618","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":"An energy efficient location aware geographic routing protocol based on anchor node path planning and optimized Q-learning model","authors":"K. Bhadrachalam ,&nbsp;B. Lalitha","doi":"10.1016/j.suscom.2025.101084","DOIUrl":"10.1016/j.suscom.2025.101084","url":null,"abstract":"<div><div>A wireless sensor network (WSN) is made up of many nodes that can send sensed data to the base station or sink directly or through intermediary nodes. However, geographically based routing requires accurate sensor node location data. The precise localization of dispersed sensors within a designated region is a critical problem in WSN development. This study proposes a new location-aware geographic routing protocol, which is based on the Q-learning model and anchor node path planning. Initially, the location of an unknown node is detected using an Integrated Received Signal Strength Indicator (RSSI) and Cosine rule-based path planning model. After detecting the unknown nodes, each node is forwarded through a HELLO message to identify the routing neighbour nodes. Then, the Optimal Osprey Q-Learning (O²QL) model is used in multi-objective optimization to choose the best path routing. Then, the Q-learning model's reward function is responsible for both end-to-end latency and energy consumption. Moreover, the Q-learning parameters of the suggested protocol can be adaptively updated to accommodate the high process degrees found in WSNs. Simulations have been conducted to prove the efficacy of the method based on different metrics. The proposed approach has been compared with the existing recently introduced routing protocols in WSN. As a result, the proposed location-aware energy-efficient geographic routing techniques show performance in terms of average end-to-end delay of nodes (2.88), packet loss ratio of nodes (0.058), residual energy of nodes (0.199), average energy consumption of nodes (1.53) and packet delivery rate of nodes (98.96).</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101084"},"PeriodicalIF":3.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172619","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":"Strategic feasibility outlook for blue energy investments using an integrated decision-making approach","authors":"Serkan Eti ,&nbsp;Serhat Yüksel ,&nbsp;Hasan Dinçer","doi":"10.1016/j.suscom.2025.101085","DOIUrl":"10.1016/j.suscom.2025.101085","url":null,"abstract":"<div><div>Conducting feasibility analysis in blue energy investments is very critical to provide performance analysis of the projects. However, a significant portion of the studies in the literature focus on general energy projects. Nevertheless, there are not enough studies for a more specific area such as blue energy. This situation significantly increases the need for this type of priority analysis. Accordingly, the purpose of this study is to identify the most appropriate strategies to increase the effectiveness of the feasibility analysis of blue energy investments via a novel decision-making model. In the first stage of the model, the importance levels of experts are computed using machine learning technique. The second stage includes weighting the feasibility criteria set for blue energy project investment by Fermatean fuzzy entropy. After that, the strategic alternatives for increasing the capacity of blue energy projects are ranked with Fermatean fuzzy CoCoSo. The main contribution of this study to the literature is making a detailed evaluation to generate appropriate strategies for the feasibility analysis of the blue energy investments via a novel decision-making model. The integration of AI system provides some advantages to the proposed model. In this way, the decision matrix is obtained by calculating the importance weights of each expert. This situation allows to have more accurate analysis results. It is defined that the technological infrastructure of the company plays the most critical role (weight: 0.173) when conducting feasibility analysis for blue energy investments. Similarly, it is also identified that the financial performance of the business (weight: 0.172) is also important to conduct a more successful feasibility analysis for blue energy investments. On the other side, the ranking results demonstrate that collaborating with the investment-ready companies for increasing the innovative technologies is the most appropriate strategy to increase the capacity of blue energy projects.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101085"},"PeriodicalIF":3.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143172617","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":"Diagnostic analysis and performance optimization of scalable computing systems in the context of industry 4.0","authors":"John William Vásquez Capacho ,&nbsp;G. Pérez-Zuñiga ,&nbsp;L. Rodriguez-Urrego","doi":"10.1016/j.suscom.2024.101067","DOIUrl":"10.1016/j.suscom.2024.101067","url":null,"abstract":"<div><div>Escalating energy costs and sustainability concerns in high-performance computing (HPC) and industrial-scale systems demand advanced models for energy-efficient operations. Traditional discrete event system (DES) models, while valuable tools, often struggle with the complexities of real-world systems, particularly when dealing with simultaneous events, partial sequences, and false positives. To address these limitations, this paper introduces V-nets, a novel formalism that offers a more robust approach to modeling and analyzing complex event sequences. V-nets excel at handling concurrent events, incorporating temporal constraints, and accurately detecting partial sequences, leading to improved system diagnostics and energy efficiency. By leveraging V-nets, we can gain deeper insights into the behavior of complex systems, identify potential bottlenecks, and optimize resource allocation. This can lead to significant energy savings and improved system performance. For example, in HPC systems, V-nets can be used to monitor the energy consumption of individual components, identify idle resources, and optimize workload scheduling. In industrial settings, V-nets can help detect anomalies in production processes, leading to timely interventions and reduced downtime. The potential applications of V-nets are vast, extending beyond HPC systems to various industrial domains. As AI-driven workloads continue to grow in complexity, V-nets can play a crucial role in monitoring and optimizing energy consumption in these systems. By bridging the gap between theoretical advancements and real-world applications, V-nets have the potential to revolutionize the field of DES modeling and contribute to the development of more sustainable and efficient systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101067"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096401","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}