Sustainable Computing-Informatics & Systems最新文献

{"title":"Improved sensor localization with intelligent trust model in heterogeneous wireless sensor network in Internet of Things (IoT) environment","authors":"Priyan Malarvizhi Kumar ,&nbsp;Tayyaba Shahwar ,&nbsp;C. Gokulnath","doi":"10.1016/j.suscom.2025.101122","DOIUrl":"10.1016/j.suscom.2025.101122","url":null,"abstract":"<div><div>Heterogeneous Wireless Sensor Network (HWSN) based Internet of Things (IoT) applications are highly trending. It also consists of serious challenges in improving the network longitivity and security regarding data processing and communication. In earlier research several algorithms and models were introduced to enhance the performance of HWSN network in various applications in terms of energy saving process. But still management of a huge number of devices in IoT is under an open research area. Still, it needs improvement in energy efficiency improvisation and device safety. To improve the overall performance of the HWSN network in IoT background the authors of this paper proposed an improved sensor localization with an intelligent trust model in HWSN (ISL-ITMH) network. The major categories of this proposed ISL-ITMH include data transmission process, energy consumption model, trust based communication among the devices in the network and threshold-based energy model. With these processes' presence, communication among each device in the network is effectively analyzed and monitored so that both the trust calculations and energy efficiency is maximum and that helps to attend maximum performance in data transmission in the HWSN network. The implementation of this concept is demonstrated in the software NS3 and certain parameters are measured for the result analysis: energy efficiency, packet delivery ratio, routing overhead, trust score, network throughput and end to end delay. From the result outcome, the proposed ISL-ITMH obtained results which increases the energy efficiency, throughput and data delivery rate throughout the entire iteration compared with the earlier baseline methodologies.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101122"},"PeriodicalIF":3.8,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843898","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":"Joint deep reinforcement learning strategy in MEC for smart internet of vehicles edge computing networks","authors":"Jiabin Luo ,&nbsp;Qinyu Song ,&nbsp;Fusen Guo ,&nbsp;Haoyuan Wu ,&nbsp;Hafizan Mat Som ,&nbsp;Saad Alahmari ,&nbsp;Azadeh Noori Hoshyar","doi":"10.1016/j.suscom.2025.101121","DOIUrl":"10.1016/j.suscom.2025.101121","url":null,"abstract":"<div><div>The Internet of Vehicles (IoV) has a limited computing capacity, making processing computation tasks challenging. These vehicular services are updated through communication and computing platforms. Edge computing is deployed closest to the terminals to extend the cloud computing facilities. However, the limitation of the vehicular edge nodes, satisfying the Quality of Experience (QoE) is the challenge. This paper developed an imaginative IoV scenario supported by mobile edge computing (MEC) by constructing collaborative processes such as task offloading decisions and resource allocation in various roadside units (RSU) environments that cover multiple vehicles. After that, Deep reinforcement Learning (DRL) is employed to solve the joint optimisation issue. Based on this joint optimisation model, the offloading decisions and resource allocations are gained to reduce the cost obtained in end-to-end delay and expense of resource computation. This problem is formulated based on the Markov Decision Process (MDP) designed functions like state, action, and reward. The proposed model's performance evaluations and numerical results achieve less average delay for 30 vehicle nodes in simulation.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101121"},"PeriodicalIF":3.8,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817182","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 Unscented Transformation based approach for probabilistic design of Wide-Area Damping Controllers","authors":"Wesley Peres","doi":"10.1016/j.suscom.2025.101116","DOIUrl":"10.1016/j.suscom.2025.101116","url":null,"abstract":"<div><div>Low-frequency oscillations with insufficient damping can compromise the integrity of power systems. Various control mechanisms exist to mitigate these oscillations, with Wide Area Damping Controllers (WADC) being notably efficient by utilizing remote signals captured by Phasor Measurement Units. A primary challenge in developing a WADC framework is the latency associated with transmitting these remote signals. This challenge is exacerbated by uncertainties, making deterministic methods for WADC tuning potentially impractical. This study introduces an optimization methodology for the probabilistic design of a WADC that accounts for uncertainties in power loads and signal transmission delays. This methodology incorporates the likelihood of meeting security and stability criteria as constraints and employs Particle Swarm Optimization and Unscented Transformation for problem resolution. The efficacy of the proposed method is demonstrated through its application to a Brazilian test system, highlighting its promising results in terms of precision and computational efficiency compared to the Monte Carlo simulation.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101116"},"PeriodicalIF":3.8,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738663","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":"Advanced frequency stabilization framework for multi-area renewable energy grids with EV aggregator support: A multi-stage control perspective","authors":"Muhammad Inshal Shahzad ,&nbsp;Muhammad Majid Gulzar ,&nbsp;Salman Habib ,&nbsp;Md Shafiullah ,&nbsp;Aqsa Shahzad ,&nbsp;Muhammad Khalid","doi":"10.1016/j.suscom.2025.101120","DOIUrl":"10.1016/j.suscom.2025.101120","url":null,"abstract":"<div><div>In the modern world, the surge of renewable energy has become a focal point, drawing global attention due to its ability to merge cost-effectiveness with sustainability. This shift has made renewable energy an inescapable component of our power grids. A fresh approach is being proposed for load frequency control (LFC) in multi-area power systems, integrating diverse energy sources like photovoltaic (PV), electric vehicles, wind turbines, and thermal plants. This study dives deep into the complex domain of control systems by examining the intercommunication between multi-stage controllers, specifically comparing the 2DOF proportional integral and derivative with filter-PI (2 Degrees of Freedom PIDn-PI) models against the classic PI and 2DOF-PIDn controllers. The key differentiator here lies in introducing an enhanced coyote optimization algorithm (ECOA), aimed at determining the optimal parameters for these advanced controllers. A unique facet of this research is its inclusion of uncertainty, addressing variability by allowing the system parameters to fluctuate within a range of ± 40 %. The robustness of the suggested controllers is tested under dynamic load changes, with these variations applied independently across multiple regions. Two distinct test scenarios are employed, each subject to varying disturbances, to gauge the controllers' adaptability. The operational restrictions of the governor dead band (GDB) impact on the reheat thermal governor unit and generation rate constraint (GRC) in the reheat thermal generating units are simulated using the proper dynamic models. This research includes GDB after the governor unit and GRC after re-heat unit and studies the effect of nonlinearity in power system. CTD is added before the controller and because, in a realistic scenario, there is a time delay in communication with the system. So, the proposed controller helps to give the results as close as the real-time scenario. The findings reveal that by incorporating the GRC, GDB and CTD, the oscillations are damp successfully and even rise under uncertainty situations. The stability analysis also performs the proposed technique upon comparison with previously established methods. The simulated results imply that the proposed multi-staged 2DOF PIDn-PI control system, optimized by ECOA, exhibits remarkable efficiency and resilience. For instance, in case of perturbation in the system, the cumulative settling time of the proposed controller is 1.137 sec while compared with GA-PI, PSO-PID, ABC-PIDn, COA-PI, COA-PIDn, MPA-PIDn has settling time of 28.972 sec, 26.42 sec, 24.52 sec, 17.68 sec, 15.125 sec and 14.01 sec respectively. Its ability to manage load frequency control across multi-area power systems sets it apart, offering a sophisticated solution to the complexities of modern energy management.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101120"},"PeriodicalIF":3.8,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777337","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":"Cloud-IoT framework for EV charge station allocation and scheduling: A spotted hyena jellyfish search optimization approach","authors":"Gopal Saravanan ,&nbsp;Ramamani Tripathy ,&nbsp;Rayavarapu Umamaheswara Rao ,&nbsp;Manikonda Srinivasa Seshasai","doi":"10.1016/j.suscom.2025.101118","DOIUrl":"10.1016/j.suscom.2025.101118","url":null,"abstract":"<div><div>Electric Vehicles (EVs) represent a technological advancement that promises a solution to reduce pollution and fuel consumption. This EV technology is obstructed by various factors, like the size of the battery, charging time, short driving ranges, and uneven scheduling. Cloud-based Internet of Things (IoT) technology enables EVs to plan routes and process information with smart wireless charging. This study introduces the Spotted Hyena Jellyfish Search Optimization (SHJSO) for scheduling EV charges. Initially, cloud simulations are performed to replicate charging stations and EV locations. SHJSO schedules EV charges considering average waiting time, distance, power prediction, charging cost, user preference, arrival time, and the number of EVs. DNFN predicts power, and SHJSO combines Spotted Hyena Optimization (SHO) and Jellyfish Search Optimization (JSO). Metrics like waiting time is 27.72 s, distance is 1.067 m, EVs charged is 60, and power is 53.67 W show the effectiveness. Compared to Fractional Feedback Tree Algorithm (FFTA), Smart charge scheduling, Self-Controlling Express Station Management (SC-EXP), and Charging control deep deterministic policy gradient (CDDPG) methods, SHJSO increases the number of charged EVs by 10 %, 36.6 %, 28.3 %, and 18.3 %.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101118"},"PeriodicalIF":3.8,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143724357","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":"Sustainable green supply chain and logistics management using adaptive fuzzy-based particle swarm optimization","authors":"Hatim Bukhari ,&nbsp;Mohammed Salem Basingab ,&nbsp;Ali Rizwan ,&nbsp;Manuel Sánchez-Chero ,&nbsp;Christos Pavlatos ,&nbsp;Leandro Alonso Vallejos More ,&nbsp;Georgios Fotis","doi":"10.1016/j.suscom.2025.101119","DOIUrl":"10.1016/j.suscom.2025.101119","url":null,"abstract":"<div><div>Sustainable Green Supply Chain and Logistics Management are crucial to reap environmental and economic wins in today’s complex and competitive global business environment. However, conventional optimization planning techniques can prove inadequate for green supply chain networks. This study proposes a sustainable green supply chain and logistics network that adopts a novel Adaptive Fuzzy Particle Swarm Optimization (AFPSO) method. This study presents a multi-objective mathematical model in combination with Mixed-Integer Linear Programming (MILP) and Multi-Adjacent Descent Traversal Algorithm (MADTA). AFPSO approach bases particle swarm optimization on fuzzy logic to improve efficiency in various conditions. Performance is assessed using parameters such as energy consumption, implementation cost, error values, and enabler applications. Performance assessment is carried out through MATLAB simulations, where the proposed AFPSO-MADTA is compared against Back-Propagation Neural Network (BPNN), the Traditional Particle Swarm Optimization Back-Propagation Neural Network (Traditional PSO-BPNN), and Improved Particle Swarm Optimization Back-Propagation Neural Network (IPSO-BPNN) methods. The results demonstrate that the proposed AFPSO-MADTA approach demonstrates greater energy efficiency, lower costs, higher accuracy, and better sustainability enabler stabilization than traditional optimization methodologies. These findings show the value of AFPSO-MADTA in achieving sustainable supply chain and logistics management.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101119"},"PeriodicalIF":3.8,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697478","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":"Designing a sustainable and smart supply chain considering a green computing approach in the post-COVID period","authors":"Sina Abbasi ,&nbsp;Ehsan Soltanifar ,&nbsp;Dariush Tahmasebi Aghbelaghi ,&nbsp;Peiman Ghasemi","doi":"10.1016/j.suscom.2025.101117","DOIUrl":"10.1016/j.suscom.2025.101117","url":null,"abstract":"<div><div>The present research aims to present a mathematical framework for a smart Supply Chain (SC) and Logistics under the inventory management policy by the seller using the Internet of Things (IoT) approach in the period following the COVID-19 pandemic for optimizing and solving the challenges of the traditional SC. Based on the needs, a mathematical two-objective was presented. The first and second objectives are to reduce SC's cost and time. In this model, a four-stage SC is considered, which enables direct communication from suppliers to the production centers, production centers to sellers, and sellers to clients. The nature of this chain is smart, and the layers of the SC utilize the technologies of Wireless Sensor Network (WSN) and Radio-Frequency Identification (RFID), blockchain, and internet sales in the post-COVID. In this research, after validating the model using the LP-metric method with LINGO software for small and medium sizes. The suggested mathematical model for the large dimensions needs to be checked using MATLAB software and the Tunicate Swarm Algorithm (TSA). We briefly describe the highlights of contribution and innovation to our knowledge. In that case, this is the first research that designed a sustainable and smart SC that simultaneously encompasses all the concepts of blockchain, internet sales, IoT (including RFID and WSN), and the post-COVID era. The final result of the research is briefly stated: In the post-COVID-19 era, online sales of goods by sellers, production costs, transportation costs, and consumer demand have significantly increased.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101117"},"PeriodicalIF":3.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143704160","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":"A multilevel decentralized trust management-aware OS-GRU and S-fuzzy-based dynamic task offloading in block-chain enabled Edge-Cloud","authors":"Raj Kumar Gudivaka ,&nbsp;Dinesh Kumar Reddy Basani ,&nbsp;Sri Harsha Grandhi ,&nbsp;Basava Ramanjaneyulu Gudivaka ,&nbsp;Rajya Lakshmi Gudivaka ,&nbsp;M.M. Kamruzzaman","doi":"10.1016/j.suscom.2025.101111","DOIUrl":"10.1016/j.suscom.2025.101111","url":null,"abstract":"<div><div>Recently, the dynamic Task Offloading (TO) in the cloud computing paradigm has gained immense popularity among researchers. However, the traditional systems were ineffective due to the absence of multilevel decentralized trust management. Hence, this work proposed a multilevel decentralized trust management framework named OS-GRU and S-FUZZY-based dynamic TO in Blockchain (BC)- aided IoT Edge-Cloud Computing (ECC). Initially, the IoT devices are registered in the edge layer. Also, the DL-Scrypt is used to generate the hash code of the SLA. Then, the devices log in to the network to access the cloud resources using SLA. Similarly, the trust evaluation is done between the edge layer and fog layer using S-Fuzzy. If the trust is high then the hash code is verified in the blockchain. If verified, the tasks are clustered using k-means. Thereafter, the trust between the fog layer and cloud layer is validated, followed by load-balancing. Subsequently, the load-balanced data is inputted to the workload prediction framework. Now, the proposed OS-GRU is utilized to identify the cloud server’s workload. Next, the task’s features and cloud features are used to perform dynamic task offloading. Thus, the experimental outcomes proved that the proposed methodology had higher significance with an accuracy of 98.63 %.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101111"},"PeriodicalIF":3.8,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143697479","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":"Sustainable cost-energy aware load balancing in cloud environment using intelligent optimization","authors":"Garima Verma","doi":"10.1016/j.suscom.2025.101115","DOIUrl":"10.1016/j.suscom.2025.101115","url":null,"abstract":"<div><div>Managing a distributed environment with a shared resource pool in cloud computing requires efficient task scheduling across multiple Virtual Machines (VMs). The effectiveness of the load-balancing algorithm used largely influences the system's performance. However, traditional load-balancing methods often neglect critical factors such as cost and energy consumption, which are vital for both economic and environmental sustainability. To tackle these challenges, this study introduces a new approach, Cost-Energy Aware Spider Monkey Optimization (CE-SMO). This improved version of the Spider Monkey Optimization (SMO) algorithm incorporates cost and energy efficiency into the load-balancing process. CE-SMO seeks to enhance performance by considering economic aspects like computing, storage, data transfer costs, and energy consumption. The algorithm ensures balanced, cost-efficient, and eco-friendly resource allocation. Simulations demonstrate that CE-SMO outperforms existing methods in load balancing, reaction time, makespan, and resource utilization while addressing cost and energy efficiency concerns.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101115"},"PeriodicalIF":3.8,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143644580","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":"SURETY-Fog: Secure Data Query and Storage Processing in Fog Driven IoT Environment","authors":"Pratibha Sharma ,&nbsp;Hemraj Saini ,&nbsp;Arvind Kalia","doi":"10.1016/j.suscom.2025.101113","DOIUrl":"10.1016/j.suscom.2025.101113","url":null,"abstract":"<div><div>Fog computing is an important paradigm in the current scenario among many sensing application services based on the Internet of Things (IoT). A traditional IoT environment suffers from a significant latency where all the devices access data from the cloud. To overcome this problem, fog computing is introduced to reduce the latency. However, several security limitations associated with fog computing have not been addressed. This research proposed the Secure Data Query and Storage Processing (SURETY-fog) method, which overcomes the security limitation. The proposed work has different processes to enhance security and efficiency including IoT device and user registration based on the Naor Reingold generator and Prince algorithm, Authentication by using a Multi-Factor Authentication model, secure optimized fog node selection and secure sensed data storage by using Deer Hunting Optimization (DHO) algorithm, Deep Q learning based secure data storage with improved trust in fog, and Lightweight based secure data transmission in fog layer by using bliss signature. The simulation is conducted by using iFogSim and evaluating the performance based on the following metrics, response time, attack detection rate, resource utilization, number of queries processed, transmission latency, and processing latency.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"46 ","pages":"Article 101113"},"PeriodicalIF":3.8,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143621018","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}