IEEE Transactions on Sustainable Computing最新文献_第6页

WaWoR: Wasted Work Reduction During Snapshotting in EH-WSNs WaWoR: EH-WSNs在快照期间减少浪费的工作

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-06-23 DOI: 10.1109/TSUSC.2025.3582254

Priyanka Singla;Smruti R. Sarangi

{"title":"WaWoR: Wasted Work Reduction During Snapshotting in EH-WSNs","authors":"Priyanka Singla;Smruti R. Sarangi","doi":"10.1109/TSUSC.2025.3582254","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3582254","url":null,"abstract":"Energy harvesting wireless sensor networks (EH-WSNs) are useful for ambient monitoring, especially in hazardous and hard-to-reach environments, where sensor nodes sense and transmit data to a remote sink via multi-hop communication, enabling informed decision making. Accurate decisions require a global snapshot that comprises environmental parameters simultaneously sensed from all nodes. To realize an efficient snapshot collection scheme, all nodes should send an equal number of messages where each message contributes to the snapshot; excessive messages from some nodes are ineffective and in-turn increase the network traffic and contribute to the wasteful work done by nodes. Reducing such ineffectual messages is challenging due to the variable ambient energy supply at nodes, network congestion, limited information about the overall state of the system and varying node distances from the sink. To achieve this objective, we introduce WaWoR, a novel distributed system where nodes dynamically decide when to sense and transmit snapshot messages based on their physical locations, energy availability and perceived network congestion. WaWoR outperforms two theoretical hypothetical baselines and two state-of-the-art-systems. It captures <inline-formula><tex-math>$[90 - 175]%$</tex-math></inline-formula> of the snapshots compared to the theoretical baselines and <inline-formula><tex-math>$[1.02 - 4.26]times$</tex-math></inline-formula> more snapshots than state-of-the-art systems. Additionally, it reduces wasted messages, which further reduces the total energy consumption by <inline-formula><tex-math>$[1.6 - 9.9]times$</tex-math></inline-formula>.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1070-1084"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148478869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SEOF: Reducing Spikes for Efficient SCNN Accelerators With Approximate Computing SEOF：用近似计算减少高效SCNN加速器的尖峰

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-10-10 DOI: 10.1109/TSUSC.2025.3620000

Ming Han;Jin Wu;Ye Wang;Heng Liu;Gang Qu;Jian Dong

{"title":"SEOF: Reducing Spikes for Efficient SCNN Accelerators With Approximate Computing","authors":"Ming Han;Jin Wu;Ye Wang;Heng Liu;Gang Qu;Jian Dong","doi":"10.1109/TSUSC.2025.3620000","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3620000","url":null,"abstract":"Spiking Convolutional Neural Networks (SCNNs), a variant of Spiking Neural Networks (SNNs) based on the architecture of Convolutional Neural Networks (CNNs), have demonstrated superior performance in tasks compared to traditional SNNs. However, this enhanced capability results in higher energy consumption due to increased computation and weight access. Research has extensively demonstrated that SCNN computation is directly related to spike counts. Thus, reducing the number of spikes can lead to more efficient implementations of SCNN inference accelerators. However, arbitrarily reducing spikes often results in an uncontrollable decline in accuracy. In this paper, we present a spike-efficient optimization framework, SEOF, which integrates approximate computing principles and exploits SCNN-specific characteristics to achieve energy savings while maintaining low inference accuracy loss. SEOF incorporates a novel spike-efficient controller for spiking neurons, a spike ratio-oriented objective function designed to produce accelerator-friendly SCNN models, and a spatial mask that lowers energy consumption by reducing weight accesses. Our experimental results indicate that SEOF can achieve 31%–51% computation energy savings by reducing spikes, with an accuracy loss of less than 1%, and a speedup of up to 1.56×. Additionally, SEOF can reduce weight access by up to 54.93%, leading to overall energy savings of up to 50.77%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1323-1336"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Residential Load Modeling With Generative Adversarial Networks 基于生成对抗网络的住宅负荷建模

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-09-03 DOI: 10.1109/TSUSC.2025.3605668

Marco Castangia;Benedetta Giorgi;Stefano Quer;Lorenzo Bottaccioli;Edoardo Patti

{"title":"Residential Load Modeling With Generative Adversarial Networks","authors":"Marco Castangia;Benedetta Giorgi;Stefano Quer;Lorenzo Bottaccioli;Edoardo Patti","doi":"10.1109/TSUSC.2025.3605668","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3605668","url":null,"abstract":"Precise residential load modeling is indispensable for crafting effective demand-side management strategies and simulating realistic household power consumption under diverse conditions. This paper introduces a novel generative framework, leveraging the power of Generative Adversarial Networks (GANs), to synthesize highly realistic daily activity patterns. By training on detailed Italian time-use data, the model captures nuanced behavioral statistics, reflecting the inherent variability of human routines. Furthermore, incorporating conditional generation based on the day of the week allows for contextually rich and adaptable simulations, capturing weekly lifestyle variations. Household power profiles are reconstructed by meticulously mapping the generated activities to the characteristic power signatures of common household appliances, resulting in simulations that exhibit strong concordance with empirical load data at both granular, appliance-level, and aggregated household levels. Critically, our GAN-based approach demonstrably accelerates simulation throughput compared to conventional Markov chain methodologies, enabling the efficient and scalable analysis of complex residential energy scenarios, and opening avenues for real-time applications and large-scale urban energy studies.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1216-1226"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Deep Reinforcement Learning for Efficient Task Scheduling in Green Energy Powered Cloud Data Center 基于自适应深度强化学习的绿色能源云数据中心高效任务调度

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-08-05 DOI: 10.1109/TSUSC.2025.3591510

Chengyu Hu;Yangmin Wang;Pengcheng Kong;Xuesong Yan;Deze Zeng

引用次数: 0

FedTCTF: Tensor Completion-Based Federated Learning for Device Heterogeneity FedTCTF：基于张量完成的设备异构联邦学习

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-09-25 DOI: 10.1109/TSUSC.2025.3614896

Cangming Liang;Wei Liang;Yuxiang Chen;Kuanching Li;Albert Y. Zomaya

{"title":"FedTCTF: Tensor Completion-Based Federated Learning for Device Heterogeneity","authors":"Cangming Liang;Wei Liang;Yuxiang Chen;Kuanching Li;Albert Y. Zomaya","doi":"10.1109/TSUSC.2025.3614896","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3614896","url":null,"abstract":"With the advancement of Artificial Intelligence of Things (AIoT) systems, edge devices are increasingly capable of participating in Federated Learning (FL) by collaboratively training a global model while preserving data privacy. However, the low bit-width and limited storage capacity of edge devices often degrade global model accuracy, leading traditional FL frameworks to resource waste. To address this issue and enhance edge device utilization, we propose FedTCTF, an innovative framework that integrates optimized tensor completion with a novel traversal-based Layer-Wise Model Partitioning strategy. Our approach tackles both bit-width heterogeneity and capacity constraints through two key innovations: (1) A bit-width-aware tensor completion method using adaptive Tucker and CP decomposition to align parameter precision across devices; (2) A layer-wise dynamic sub-model extraction mechanism that systematically cycles through all parts of the global model via a traversal window. Specifically, the server dynamically extracts sub-models from the global model based on device capacities, with the traversal window advancing layer-by-layer in each training round to extract layers not covered in the previous rounds, ensuring resource-constrained devices evenly train the full model. Experimental results demonstrate FedTCTF’s superiority: It achieves up to 20.77% higher accuracy on CIFAR-10 with ResNet18 compared to state-of-the-art methods, and consistently outperforms all baselines on NLP tasks, including AGNews and StackOverflow with Transformer, particularly excelling when 80% of clients are low-capacity devices. By enabling comprehensive device participation through precision recovery and uniform model coverage, FedTCTF establishes a sustainable learning paradigm for AIoT ecosystems.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1227-1239"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Foundation Models for Structural Health Monitoring 结构健康监测的基础模型

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-07-23 DOI: 10.1109/TSUSC.2025.3592097

Luca Benfenati;Daniele Jahier Pagliari;Luca Zanatta;Yhorman Alexander Bedoya Velez;Andrea Acquaviva;Massimo Poncino;Enrico Macii;Luca Benini;Alessio Burrello

{"title":"Foundation Models for Structural Health Monitoring","authors":"Luca Benfenati;Daniele Jahier Pagliari;Luca Zanatta;Yhorman Alexander Bedoya Velez;Andrea Acquaviva;Massimo Poncino;Enrico Macii;Luca Benini;Alessio Burrello","doi":"10.1109/TSUSC.2025.3592097","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3592097","url":null,"abstract":"Structural Health Monitoring (SHM) is a critical task for ensuring the safety and reliability of civil infrastructures, typically realized on bridges and viaducts by means of vibration monitoring. In this paper, we propose for the first time the use of Transformer neural networks, with a Masked Auto-Encoder architecture, as <italic>Foundation Models for SHM. We demonstrate the ability of these models to learn generalizable representations from multiple large datasets through self-supervised pre-training, which, coupled with task-specific fine-tuning, allows them to outperform state-of-the-art traditional methods on diverse tasks, including Anomaly Detection (AD) and Traffic Load Estimation (TLE). We then extensively explore model size versus accuracy trade-offs and experiment with Knowledge Distillation (KD) to improve the performance of smaller Transformers, enabling their embedding directly into the SHM edge nodes. We showcase the effectiveness of our foundation models using data from three operational viaducts. For AD, we achieve a near-perfect 99.9% accuracy with a monitoring time span of just 15 windows. In contrast, a state-of-the-art method based on Principal Component Analysis (PCA) obtains its first good result (95.03% accuracy), only considering 120 windows. On two different TLE tasks, our models obtain state-of-the-art performance on multiple evaluation metrics (R2 score, MAE% and MSE%). On the first benchmark, we achieve an R2 score of 0.97 and 0.90 for light and heavy vehicle traffic, respectively, while the best previous approach (a Random Forest) stops at 0.91 and 0.84. On the second one, we achieve an R2 score of 0.54 versus the 0.51 of the best competitor method, a Long-Short Term Memory network.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1103-1115"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things With Respect to Partial Coverage and Energy Harvesting Requirements 基于局部覆盖和能量收集需求的太阳能杀虫灯物联网节点部署策略

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-07-24 DOI: 10.1109/TSUSC.2025.3591808

Fan Yang;Xiaoyu Tian;Zhaojun Zhang;Lei Shu;Xiaoyuan Jing

{"title":"A Node Deployment Strategy in Solar Insecticidal Lamps Internet of Things With Respect to Partial Coverage and Energy Harvesting Requirements","authors":"Fan Yang;Xiaoyu Tian;Zhaojun Zhang;Lei Shu;Xiaoyuan Jing","doi":"10.1109/TSUSC.2025.3591808","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3591808","url":null,"abstract":"Coverage is a fundamental issue in the Solar Insecticidal Lamps IoTs (SIL-IoTs). Compared to complete coverage, partial coverage emerges as the preferred strategy for deploying SILs within a limited budget, as this deployment solution offers the highest cost-effectiveness. In this paper, we concentrate on studying the constrained SILs deployment problem, taking into account partial coverage and energy harvesting requirements, which we refer to as the cSILDP-PCEH problem. In this context, the positions for deploying SILs are restricted to a weighted set of candidate locations on the ridges. The weight assigned to each candidate location reflects the energy harvesting potential of the SIL deployed at that position. Our objective is to deploy a group of SILs in a subset of these candidate locations, ensuring a high overall energy harvesting potential, network connectivity, and achieving partial coverage. Due to the NP-hard nature of the problem, we introduce an approximation algorithm with a provable performance ratio tailored to our problem. Finally, we conduct a theoretical analysis of our proposed algorithm and perform extensive simulations. The simulation results demonstrate that the proposed algorithm achieves a minimum improvement of 16.45% in energy harvesting potential while preserving network connectivity and maintaining a comparable coverage level.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1147-1157"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing State Retention With Energy-Efficient Memory Tracing in Intermittent Systems 间歇系统中节能记忆跟踪增强状态保持

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-10-14 DOI: 10.1109/TSUSC.2025.3621509

Osama Bin Tariq;Theodoros D. Verykios;Geoff V. Merrett;Domenico Balsamo

{"title":"Enhancing State Retention With Energy-Efficient Memory Tracing in Intermittent Systems","authors":"Osama Bin Tariq;Theodoros D. Verykios;Geoff V. Merrett;Domenico Balsamo","doi":"10.1109/TSUSC.2025.3621509","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3621509","url":null,"abstract":"Intermittent systems powered by harvested energy frequently encounter power outages, requiring efficient mechanisms to save and restore their internal state, to ensure computational progress. In these systems, minimising the overhead of state retention, comprising CPU core registers and main volatile memory contents (a snapshot), is essential to maximise computational progress within the constraints of limited energy availability. This paper introduces a hardware module, <italic>MeTra (MEmory TRAcing), designed to enhance energy-efficient state retention in intermittent systems. This is achieved by tracing and selectively saving the modified parts of the main volatile memory (RAM) to non-volatile memory (NVM), i.e. FRAM. Additionally, <italic>MeTra dynamically adjusts the voltage threshold that initiates state saving, optimizing energy usage for each snapshot and enabling the system to dedicate more harvested energy to useful computations. <italic>MeTra was integrated with an Arm Cortex-M1 processor on an FPGA and evaluated using benchmarks including matrix multiplication, array sorting, and advanced encryption standard (AES), demonstrating its effectiveness in reducing state-saving time and improving energy efficiency by selectively saving modified RAM regions instead of the entire memory. Experimental results demonstrate that snapshot time can be reduced by 48.34% to 57.56% in FRAM-based systems, leading to an improvement in energy efficiency of 65.24% to 77.76%. These gains are achieved by selectively saving 5.66% to 19.82% of RAM, using <italic>MeTra, compared to saving entire RAM.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1310-1322"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Revealing the Veil of Greenwashing in ESG Reports: Predicting the Degree of Corporate Greenwashing Based on Thematic and Sentiment Features of Text 揭开ESG报告中“漂绿”的面纱：基于文本主题特征和情感特征的企业“漂绿”程度预测

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-07-31 DOI: 10.1109/TSUSC.2025.3594401

Xiaojia Wang;Ya Chen;Haipeng Yao

{"title":"Revealing the Veil of Greenwashing in ESG Reports: Predicting the Degree of Corporate Greenwashing Based on Thematic and Sentiment Features of Text","authors":"Xiaojia Wang;Ya Chen;Haipeng Yao","doi":"10.1109/TSUSC.2025.3594401","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3594401","url":null,"abstract":"The global rise of the green economy has positioned green financing as a critical method for enterprises to secure external funding. However, due to the high costs and imperfect regulations associated with environmental information disclosure, some enterprises establish the green image through greenwashing to attract external investment and minimize production inefficiencies, caused by abnormalities such as material shortages. The greenwashing behavior of enterprises is essentially a deception intended for investors, making it challenging for them to make informed decisions. To protect investors’ interests, and maintain the market and social order, this paper conducts a study on corporate greenwashing based on the environmental module of ESG reports issued by Chinese A-share listed companies. First, this paper utilizes MacBERT-LDA to extract topic distribution features (explicit features) and perform topic clustering. It then uses Bi-LSTM to extract topic sentiment features (implicit features) based on the clustering results. The two mutually independent feature extraction networks form a dual-channel structure, fully capturing the explicit and implicit information of the text data. Second, a Transformer model is utilized to fuse the dual-channel feature extraction results, producing richer and more comprehensive semantic features. Finally, these fused features are input into IT2F-BLS to achieve an accurate prediction of the degree of corporate greenwashing, which provides a powerful reference for investors to make decisions. We refer to this ensembled model as the IT2F-BLS greenwashing degree prediction model based on explicit and implicit textual features. Experimental results demonstrate that the model effectively captures both types of information and predicts corporate greenwashing with higher accuracy.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1173-1188"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148480699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Sustaining the Emergency Wireless Infrastructures With Differentiation-Landscape Based Energy Provisioning 基于差异化景观能源供应的应急无线基础设施维护

IF 5.3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2025-11-01 Epub Date: 2025-10-14 DOI: 10.1109/TSUSC.2025.3621464

Rui Teng

{"title":"Sustaining the Emergency Wireless Infrastructures With Differentiation-Landscape Based Energy Provisioning","authors":"Rui Teng","doi":"10.1109/TSUSC.2025.3621464","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3621464","url":null,"abstract":"With the advancements in information technology, our daily lives increasingly rely on the robustness and reliability of the information infrastructure. However, emergencies such as natural disasters may cause temporary disruptions to this infrastructure, resulting in the temporary loss of information functionality. It is an important issue for the emergency networks to sustain their information services when the conventional information services fail to operate. An Emergency Wireless Infrastructure (EWI)consists of flexibly deployed wireless routers to support the Internet of Things (IoT) and smart cities. Although advances in green and renewable energy resources have improve the long-term operation of EWI, intermittent availability of these green energy resources, such as solar energy, poses significant challenges during emergency periods and off-charging period. To address this issue, a thorough analysis of network-wide matching between energy resources and energy-consuming activities is essential for ensuring network sustainability. This paper studies the energy provisioning to an EWI with an aim to improve the sustainability of EWI. In order to qualitatively identify the difference of network activities among BSs we examine the differentiation of activity and energy consumption at BSs, taking account of both topological structure and the global activity at wireless routers called EWI base stations (BSs). By analysing the difference of network activity among BSs, a method of energy provisioning is proposed based on the differentiation landscape in terms of the topological positions of BSs and the connectivity between BSs. The proposed method enhances the sustainability of an EWI by enabling network-wide matching between energy requirement and provision. Moreover, the proposed method demonstrates high accuracy in estimating the energy-consumption differentiation in an EWI compared to other alternative methods.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 6","pages":"1378-1392"},"PeriodicalIF":5.3,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148479772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0