Sustainable Computing-Informatics & Systems最新文献

{"title":"A two-stage optimal pre-scheduling strategy for power system inertia assessment and replenishment under extreme weather events","authors":"Yang Wang ,&nbsp;Pai Pang ,&nbsp;Buyang Qi ,&nbsp;Xianan Wang ,&nbsp;Zhenghui Zhao","doi":"10.1016/j.suscom.2024.101079","DOIUrl":"10.1016/j.suscom.2024.101079","url":null,"abstract":"<div><div>This paper addresses the challenges posed by reduced power system inertia due to the large-scale renewable energy integration and the threats from frequent extreme weather events. It proposes a strategy to enhance power system resilience by incorporating inertia participation during such events. The strategy derives critical inertia demand formulas based on two key factors under extreme weather, establishing a linearized inertia assessment model. Additionally, considering the vulnerability of power lines to extreme weather events, we propose the Resilience Reserve Factor (RRF). It employs three resilience evaluation indexes to delineate the system's demand for inertia supply, efficiently targeting vulnerable areas for inertia reinforcement, thereby comprehensively enhancing the resilience of the power grid. Lastly, based on the critical inertia demand constraint criterion, we establish a two-stage pre-scheduling strategy incorporating both day-ahead planning and real-time correction while considering assessment accuracy. This approach transforms the inertia assessment problem into a resilience optimization problem, yielding the scheduling status of each generator unit and inertia replenishment results during extreme weather after iteration. The optimized strategy is validated through simulations on the improved IEEE39 buses system. Furthermore, this study employs a frequency response model to investigate the spatial distribution characteristics of inertia. The results indicate that this optimization strategy enables efficient scheduling of resources before and after extreme weather events. In addition to improving the economic performance of the power system, it significantly enhances system resilience by reinforcing both global and localized support during critical disaster resistance phases.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101079"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135784","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":"Hybrid Markov chain-based dynamic scheduling to improve load balancing performance in fog-cloud environment","authors":"Navid Khaledian ,&nbsp;Shiva Razzaghzadeh ,&nbsp;Zeynab Haghbayan ,&nbsp;Marcus Völp","doi":"10.1016/j.suscom.2024.101077","DOIUrl":"10.1016/j.suscom.2024.101077","url":null,"abstract":"<div><div>Fog computing is a distributed computing paradigm that has become essential for driving Internet of Things (IoT) applications due to its ability to meet the low latency requirements of increasing IoT applications. However, fog servers can become overburdened as many IoT applications need to run on these resources, potentially leading to decreased responsiveness. Additionally, the need to handle real-world challenges such as load instability, makespan, and underutilization of virtual machine (VM) devices has driven an exponential increase in demand for effective task scheduling in IoT-based fog and cloud computing environments. Therefore, scheduling IoT applications in heterogeneous fog computing systems effectively and flexibly is crucial. The limited processing resources of fog servers make the application of ideal but computationally costly procedures more challenging. To address these difficulties, we propose using an Arithmetic Optimization Algorithm (AOA) for task scheduling and a Markov chain to forecast the load of VMs as fog and cloud layer resources. This approach aims to establish an environmentally load-balanced framework that reduces energy usage and delay. The simulation results indicate that the proposed method can improve the average makespan, delay, and Performance Improvement Rate (PIR) by 8.29 %, 11.72 %, and 4.66 %, respectively, compared to the crow, firefly, and grey wolf algorithms (GWA).</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101077"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135638","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":"Implementation of distributed energy resources along with Network Reconfiguration for cost-benefit analysis","authors":"G. Manikanta ,&nbsp;Ashish Mani ,&nbsp;Anjali Jain ,&nbsp;Ramya Kuppusamy ,&nbsp;Yuvaraja Teekaraman","doi":"10.1016/j.suscom.2024.101078","DOIUrl":"10.1016/j.suscom.2024.101078","url":null,"abstract":"<div><div>Increased demand of power in distribution networks (DN) driven by various sectors like industrial, commercial, municipal, residential, and irrigation necessitates alternative solutions such as Distributed Generators (DGs), capacitors, and Network Reconfiguration (NR). Addressing this challenge involves optimizing the opening of tie line switches and determining the optimal placement and capacity of capacitors and DGs, which poses a complex optimization problem involving both discrete and continuous variables. To tackle this, an Adaptive Quantum-inspired Evolutionary Algorithm (AQiEA), combining principles from Quantum computing and Evolutionary Algorithms, is employed. This study emphasizes holistic benefits, specifically aiming to maximize economic gains in the distribution system with the installation of DGs, capacitors, and NR along with minimization of power losses. In this paper, two cases are explored. In the first case, seven scenarios’ analyses system losses with load variations, each scenario running twenty-five independent iterations. Performance metrics has been computed to reveal that simultaneous implementation of NR, DGs, and capacitors significantly reduces power losses compared to independent implementations. The second case introduces an additional objective of maximizing economic benefits. This involves considering factors like DG and capacitor location, capacity, line losses, and various costs such as operational, maintenance, and installation costs. The results tabulated in paper demonstrate that operating DGs in parallel with capacitors and NR not only minimizes power losses but also maximizes distribution utilities' profits.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101078"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096406","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":"Bio-inspired optimizer with deep learning model for energy management system in electric vehicles","authors":"C. Srinivasan ,&nbsp;C. Sheeba Joice","doi":"10.1016/j.suscom.2025.101082","DOIUrl":"10.1016/j.suscom.2025.101082","url":null,"abstract":"<div><div>The rising popularity of electric vehicles (EVs) stems from their enhanced performance and environmental benefits. A critical challenge exists in optimizing the performance and extending the battery life of EVs, which depends on the accurate prediction of State of Charge (SOC) and State of Health (SOH). The Battery Management Systems (BMS) is essential for an EV’s Energy Management System (EMS). The current methodologies often fail to achieve the required precision, leading to suboptimal BMS that can compromise EV efficiency and reliability. To address these challenges, a merged SOC and SOH prediction approach is proposed. To maximize prediction accuracy, a hybrid Deep Learning (DL) model incorporating bio-inspired optimization algorithms such as Elephant Herding Optimization (EHO), Honey Badger Optimization (HBO), and Moth-Flame Optimization (MFO) is utilized. The architecture comprises two Convolutional Neural Networks (CNN) and an Autoencoder (AE), integrated with a Bidirectional Long Short-Term Memory (BLSTM) layer and a single Long Short-Term Memory (LSTM) layer for encoding and decoding tasks. The three optimized hybrid DL models were validated using standard benchmark datasets such as the Oxford Battery Aging Dataset, NASA, and CALCE. The prediction results of the merged SOC and SOH prediction from the three bio-inspired hybrid DL models were compared with those of the separate SOC prediction technique. The results of the merged SOC and SOH predictions were compared with traditional separate SOC prediction techniques, demonstrating superior performance. Notably, the HBO-Hybrid DL model achieved the highest R-squared (R2) values of 0.991 for SOC and 0.996 for SOH</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101082"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135782","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 optimization of wireless sensor nodes on edge computing platform using hybrid data aggregation scheme and Q-based reinforcement learning technique","authors":"Rupa Kesavan ,&nbsp;Yaashuwanth Calpakkam ,&nbsp;Prathibanandhi Kanagaraj ,&nbsp;Vijayaraja Loganathan","doi":"10.1016/j.suscom.2024.101072","DOIUrl":"10.1016/j.suscom.2024.101072","url":null,"abstract":"<div><div>Wireless Sensor Network (WSN) security and energy consumption is a potential issue. WSN plays an important role in networking technologies to handle edge devices on a heterogeneous edge computing platform. For faster processing of sensor nodes on an Industrial Internet of Everything (IIOE), an efficient computing technique for an emerging networking technology is being explored. As a result, the proposed study provides a chaotic mud ring-based elliptic curve cryptographic (CMR_ECC)-based encryption solution for WSN security. In the proposed WSN environment, various sensor nodes are deployed to collect data. To enhance the network lifetime, the nodes are combined into clusters, and the selection of cluster heads is performed with a fuzzy logic-based osprey algorithm (FL_OA). After the encryption process, the most optimal key selection process is performed with a hybrid chaotic mud ring algorithm, and the encrypted data are optimally routed to varied edge servers with a hybrid Chebyshev Gannet Optimization (CGO) approach. The data aggregation is performed with a Q-reinforcement learning approach. The proposed work is implemented with MATLAB. For 500, 750, and 1000 WSN sensor nodes, the proposed technique resulted in energy consumption values of 0.28780005 mJ, 0.31141 mJ, and 0.339419 mJ, respectively.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101072"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096403","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 MPPT integrated DC-DC boost converter for solar energy harvester using LPWHO approach","authors":"Sneha Pokharkar ,&nbsp;Mahesh D. Goudar ,&nbsp;Vrushali Waghmare","doi":"10.1016/j.suscom.2024.101076","DOIUrl":"10.1016/j.suscom.2024.101076","url":null,"abstract":"<div><div>Due to high maintenance costs and inaccessibility, replacing batteries regularly is a major difficulty for Wireless Sensor Nodes (WSNs) in remote locations. Harvesting energy from multiple resources like sun, wind, thermal, and vibration is one option. Because of its plentiful availability, solar energy harvesting is the finest alternative among them. The battery gets charged during the day by solar energy, and while solar energy is unavailable, the system is powered by the charge stored in the battery. Hence, in this paper, a highly efficient Solar Energy Harvesting (SEH) system is proposed using Leadership Promoted Wild Horse Optimizer (LPWHO). LPWHO refers to the conceptual improvement of the standard Wild Horse optimization (WHO) algorithm. This research is going to focus on overall harvesting efficiency which further depends on MPPT. MPPT is used as it extracts maximal power from the solar panels and reduces power loss. The usage of MPPT enhances the extracted power’s efficiency out of the solar panel when its voltages are out of sync. At last, the supremacy of the presented approach is proved with respect to varied measures.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101076"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096407","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":"Improving energy efficiency and fault tolerance of mission-critical cloud task scheduling: A mixed-integer linear programming approach","authors":"Mohammadreza Saberikia ,&nbsp;Hamed Farbeh ,&nbsp;Mahdi Fazeli","doi":"10.1016/j.suscom.2024.101068","DOIUrl":"10.1016/j.suscom.2024.101068","url":null,"abstract":"<div><div>Cloud services have become indispensable in critical sectors such as healthcare, drones, digital twins, and autonomous vehicles, providing essential infrastructure for data processing and real-time analytics. These systems operate across multiple layers, including edge, fog, and cloud, requiring efficient resource management to ensure reliability and energy efficiency. However, increasing computational demands have led to rising energy consumption and frequent faults in cloud data centers. Inefficient task scheduling exacerbates these issues, causing resource overutilization, execution delays, and redundant processing. Current approaches struggle to optimize energy consumption, execution time, and fault tolerance simultaneously. While some methods offer partial solutions, they suffer from high computational complexity and fail to effectively balance the workloads or manage redundancy. Therefore, a comprehensive task scheduling solution is needed for mission-critical applications. In this article, we introduce a novel scheduling algorithm based on Mixed Integer Linear Programming (MILP) that optimizes task allocation across edge, fog, and cloud environments. Our solution reduces energy consumption, execution time, and failure rates while ensuring balanced distribution of computational loads across virtual machines. Additionally, it incorporates a fault tolerance mechanism that reduces the overlap between primary and backup tasks by distributing them across multiple availability zones. The scheduler’s efficiency is further enhanced by a custom-designed heuristic, ensuring scalability and practical applicability. The proposed MILP-based scheduler demonstrates significant average improvements over the best state-of-the-art algorithms evaluated. It achieves a 9.63% increase in task throughput, reduces energy consumption by 18.20%, shortens execution times by 9.35%, and lowers failure probabilities by 11.50% across all layers of the distributed cloud system. These results highlight the scheduler’s effectiveness in addressing key challenges in energy-efficient and reliable cloud computing for mission-critical applications.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101068"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096460","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 fog-based internet of things framework to combat wildlife poaching","authors":"Rahul Siyanwal ,&nbsp;Arun Agarwal ,&nbsp;Satish Narayana Srirama","doi":"10.1016/j.suscom.2024.101070","DOIUrl":"10.1016/j.suscom.2024.101070","url":null,"abstract":"<div><div>Wildlife trafficking, a significant global issue driven by unsubstantiated medical claims and predatory lifestyle that can lead to zoonotic diseases, involves the illegal trade of endangered and protected species. While IoT-based solutions exist to make wildlife monitoring more widespread and precise, they come with trade-offs. For instance, UAVs cover large areas but cannot detect poaching in real-time once their power is drained. Similarly, using RFID collars on all wildlife is impractical. The wildlife monitoring system should be expeditious, vigilant, and efficient. Therefore, we propose a scalable, motion-sensitive IoT-based wildlife monitoring framework that leverages distributed edge analytics and fog computing, requiring no animal contact. The framework includes 1. Motion Sensing Units (MSUs), 2. Actuating and Processing Units (APUs) containing a camera, a processing unit (such as a single-board computer), and a servo motor, and 3. Hub containing a processing unit. For communication across these components, ESP-NOW, Apache Kafka, and MQTT were employed. Tailored applications (e.g. rare species detection utilizing ML) can then be deployed on these components. This paper details the framework’s implementation, validated through tests in semi-forest and dense forest environments. The system achieved real-time monitoring, defined as a procedure of detecting motion, turning the camera, capturing an image, and transmitting it to the Hub. We also provide a detailed model for implementing the framework, supported by 2800 simulated architectures. These simulations optimize device selection for wildlife monitoring based on latency, cost, and energy consumption, contributing to conservation efforts.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101070"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096402","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":"Deep reinforcement learning and enhanced optimization for real-time energy management in wireless sensor networks","authors":"Vidhya Sachithanandam ,&nbsp;Jessintha D. ,&nbsp;Balaji V.S. ,&nbsp;Mathankumar Manoharan","doi":"10.1016/j.suscom.2024.101071","DOIUrl":"10.1016/j.suscom.2024.101071","url":null,"abstract":"<div><div>Constraints are a major issue in radio-based communication in Wireless Sensor Networks, where each sensor node has a limited amount of power. Conventional clustering and optimization methods have been inappropriate for dynamic conditions which lead to timely energy drainage and reduce the network lifetime. In this research, the novel Deep Reinforcement Learning-Enhanced Hybrid African Vulture and Aquila Optimizer has been proposed that optimizes the dynamic clustering and energy-based parameters in real time. The proposed model is designed for optimizing the Wireless Sensor Networks, by including Deep Reinforcement Learning to adjust the dynamic formation of the base of the cluster on real-time data which leads to efficient energy utilization among all the sensor nodes. It combines the best properties of the Aquila and African Vulture Optimizer to optimize the network lifetime and energy consumption. The network lifetime, which is one of the most crucial characteristics, is optimized by using the global search algorithm of African Vulture Optimiser. In contrast, it is optimized by the localized search of Aquila optimizer to reduce energy consumption. The presented novel African Vulture and Aquila model outperforms the existing methods used convention-based optimization methods. It shows a 20 % improvement in energy efficiency and faster convergence with better robustness while keeping the network scalability. The proposed approach is perfectly suited for the scalable WSNs which are mainly used in the environment such as smart cities and IoT systems where a timely adaptation process is inevitable.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101071"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143096959","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":"Modelling sustainability in cyber–physical systems: A systematic mapping study","authors":"Ankica Barišić ,&nbsp;Jácome Cunha ,&nbsp;Ivan Ruchkin ,&nbsp;Ana Moreira ,&nbsp;João Araújo ,&nbsp;Moharram Challenger ,&nbsp;Dušan Savić ,&nbsp;Vasco Amaral","doi":"10.1016/j.suscom.2024.101051","DOIUrl":"10.1016/j.suscom.2024.101051","url":null,"abstract":"<div><div>Supporting sustainability through modelling and analysis has become an active area of research in Software Engineering. Therefore, it is important and timely to survey the current state of the art in sustainability in Cyber-Physical Systems (CPS), one of the most rapidly evolving classes of complex software systems. This work presents the findings of a Systematic Mapping Study (SMS) that aims to identify key primary studies reporting on CPS modelling approaches that address sustainability <em>over the last 10 years</em>. Our literature search retrieved 2209 papers, of which 104 primary studies were deemed relevant for a detailed characterisation. These studies were analysed based on nine research questions designed to extract information on sustainability attributes, methods, models/meta-models, metrics, processes, and tools used to improve the sustainability of CPS. These questions also aimed to gather data on domain-specific modelling approaches and relevant application domains. The final results report findings for each of our questions, highlight interesting correlations among them, and identify literature gaps worth investigating in the near future.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"45 ","pages":"Article 101051"},"PeriodicalIF":3.8,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143135635","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}