IEEE Transactions on Sustainable Computing最新文献_第3页

Stochastic Computation Model for Solar Panel Size and Cost of Sustainable IoT Networks 可持续物联网网络太阳能电池板尺寸和成本的随机计算模型

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-08-14 DOI: 10.1109/TSUSC.2024.3443450

Atul Banotra;Deepak Mishra;Sudhakar Modem

{"title":"Stochastic Computation Model for Solar Panel Size and Cost of Sustainable IoT Networks","authors":"Atul Banotra;Deepak Mishra;Sudhakar Modem","doi":"10.1109/TSUSC.2024.3443450","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3443450","url":null,"abstract":"The Internet of Things (IoT) applications require uninterrupted network operation which is often hindered by battery energy constraints. Literature suggests that solar energy harvesting is a promising approach to powering IoT devices in a sustainable manner. However, the available literature overlooks key factors of determining effective solar panel size and cost while considering the IoT consumption for sustainable operation. This article tackles these pivotal aspects by investigating viability of commercially available solar panels as a sustainable energy source for IoT applications. A novel stochastic computation model is introduced to characterize the unpredictability of solar irradiance across three different time regions of the day. By employing distribution fitting models, the proposed computation model accurately determines the required solar panel size in cm<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> and panel cost in Indian Rupees for the sustainable operation of the IoT application. Further, the proposed model incorporates the assessment of outage and sustainability probabilities for user-specified solar panel size and cost. These insights are significant in settings where energy efficiency and sustainability are crucial. Numerical results are presented to validate the derived distribution models and performance metrics for sustainable IoT applications. The effectiveness and accuracy of the proposed model are validated by comparing results with baseline model.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"317-332"},"PeriodicalIF":3.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769461","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

Cybersecurity Solutions and Techniques for Internet of Things Integration in Combat Systems 作战系统中物联网集成的网络安全解决方案和技术

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-08-14 DOI: 10.1109/TSUSC.2024.3443256

Amirmohammad Pasdar;Nickolaos Koroniotis;Marwa Keshk;Nour Moustafa;Zahir Tari

{"title":"Cybersecurity Solutions and Techniques for Internet of Things Integration in Combat Systems","authors":"Amirmohammad Pasdar;Nickolaos Koroniotis;Marwa Keshk;Nour Moustafa;Zahir Tari","doi":"10.1109/TSUSC.2024.3443256","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3443256","url":null,"abstract":"The Internet of Things (IoT) has enabled pervasive networking and multi-modal sensing, offering various services such as remote operations and augmenting existing processes. The military setting has increasingly and notably adopted IoT technologies, such as sensor-rich drones or autonomous vehicles, which provide military personnel with enhanced situational awareness, faster decision-making capabilities, and improved operational precision. However, integrating IoT into military systems introduces new security challenges due to increased connectivity and susceptibility to vulnerabilities. Cyberattacks on military IoT systems can have severe consequences, including operational disruptions and compromises of sensitive information. This article proposes a new perspective on examining threat models in IoT-enhanced combat systems, emphasising approaches for identifying threats, conducting vulnerability assessments, and suggesting countermeasures. It delves into the characteristics and structures of IoT-enhanced combat systems, exploring technical implementations and technologies. Additionally, it outlines five significant areas of focus, including blockchain, machine learning, game theory, protocols, and algorithms, to enhance understanding of IoT-enhanced combat systems. The insights gained from this analysis can inform the development of secure and resilient military IoT systems, ultimately enhancing the safety and effectiveness of military operations.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"345-365"},"PeriodicalIF":3.0,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769463","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 Distributed Data-Driven and Machine Learning Method for High-Level Causal Analysis in Sustainable IoT Systems 可持续物联网系统中高层次因果分析的分布式数据驱动和机器学习方法

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-08-13 DOI: 10.1109/TSUSC.2024.3441722

Wangyang Yu;Jing Zhang;Lu Liu;Yuan Liu;Xiaojun Zhai;Ruhul Kabir Howlader

{"title":"A Distributed Data-Driven and Machine Learning Method for High-Level Causal Analysis in Sustainable IoT Systems","authors":"Wangyang Yu;Jing Zhang;Lu Liu;Yuan Liu;Xiaojun Zhai;Ruhul Kabir Howlader","doi":"10.1109/TSUSC.2024.3441722","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3441722","url":null,"abstract":"A causal relationship forms when one event triggers another's change or occurrence. Causality helps to understand connections among events, explain phenomena, and facilitate better decision-making. In IoT systems, massive consumption of energy may lead to specific types of air pollution. There are causal relationships among air pollutants. Analyzing their interactions allows for targeted adjustments in energy use, like shifting to cleaner energy and cutting high-emission sources. This reduces air pollution and boosts energy sustainability, aiding sustainable development. This paper introduces a distributed data-driven machine learning method for high-level causal analysis (DMHC), which extracts general and high-level Complex Event Processing (CEP) rules from unlabeled data. CEP rules can capture the interactions among events and represent the causal relationships among them. DMHC deploys a two-layer LSTM attention mechanism model and decision tree algorithm to filter and label data, extracting general CEP rules. Afterward, it proceeds to generate event logs based on general rules with heuristic mining (HM), extracting high-level CEP rules that pertain to causal relationships. These high-level rules complement the extracted general rules and reflect the causal relationships among the general rules. The proposed high-level methodology is validated using a real air quality dataset.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"274-286"},"PeriodicalIF":3.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769426","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

Restoration-Aware Sleep Scheduling Framework in Energy Harvesting Internet of Things: A Deep Reinforcement Learning Approach 能量收集物联网中的恢复感知睡眠调度框架：深度强化学习方法

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-08-13 DOI: 10.1109/TSUSC.2024.3442918

Haneul Ko;Hongrok Choi;Sangheon Pack

{"title":"Restoration-Aware Sleep Scheduling Framework in Energy Harvesting Internet of Things: A Deep Reinforcement Learning Approach","authors":"Haneul Ko;Hongrok Choi;Sangheon Pack","doi":"10.1109/TSUSC.2024.3442918","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3442918","url":null,"abstract":"Energy harvesting Internet of Things (IoT) devices are capable of sensing only intermittent and coarse-grained data due to sleep scheduling; therefore, we develop a restoration mechanism (e.g., probabilistic matrix factorization (PMF)) that exploits spatial and temporal correlations of data to build up an environmental monitoring system. However, even with a well-designed restoration mechanism, a high accuracy of the environmental map cannot be achieved if an appropriate sleep scheduling of IoT devices is not incorporated (e.g., if IoT devices at necessary locations are in sleep mode or are not involved in restoration due to their insufficient energy). In this paper, we propose a restoration-aware sleep scheduling (RASS) framework for energy harvesting IoT-based environmental monitoring systems. Here, RASS involves customized deep reinforcement learning (DRL) considering the restoration mechanism, using which the controller performs sleep scheduling to achieve high accuracy of the restored environmental map while avoiding energy outage of IoT devices. The evaluation results demonstrate that RASS can achieve an environmental map with 5% or a lower difference from the actual values and fair energy consumption among IoT devices.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"190-198"},"PeriodicalIF":3.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184141","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

Efficient Federated Learning via Adaptive Model Pruning for Internet of Vehicles With a Constrained Latency 基于自适应模型剪枝的时延受限车联网高效联邦学习

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-08-12 DOI: 10.1109/TSUSC.2024.3441658

Xing Chang;Mohammad S. Obaidat;Jingxiao Ma;Xiaoping Xue;Yantao Yu;Xuewen Wu

{"title":"Efficient Federated Learning via Adaptive Model Pruning for Internet of Vehicles With a Constrained Latency","authors":"Xing Chang;Mohammad S. Obaidat;Jingxiao Ma;Xiaoping Xue;Yantao Yu;Xuewen Wu","doi":"10.1109/TSUSC.2024.3441658","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3441658","url":null,"abstract":"In the Internet of Vehicles (IoV), data privacy concerns have prompted the adoption of Federated Learning (FL). Efficiency improvements in FL remain a focal area of research, with recent studies exploring model pruning to lessen both computation and communication overhead. However, in the IoV, model pruning presents unique challenges and remains underexplored. Pruning strategy design is critical as it directly impacts each vehicle's learning latency and capacity to participate in FL. Furthermore, FL performance and model pruning are intricately connected. Additionally, the fluctuating number and mobility states of vehicles per round complicate determining the optimal pruning ratio, closely intertwining pruning with vehicle selection. This study introduces Vehicular Federated Learning with Adaptive Model Pruning (VFed-AMP) to tackle these challenges by integrating adaptive pruning with dynamic vehicle selection and resource allocation. We analyze the impact of pruning ratios on learning latency and convergence rate. Then, guided by these findings, a joint optimization problem is formulated to maximize the convergence rate concerning optimal vehicle selection, bandwidth allocation, and pruning ratios. Finally, a low-complexity algorithm for joint adaptive pruning and vehicle scheduling is proposed to address this problem. Through theoretical analysis and system design, VFed-AMP enhances FL efficiency and scalability in the IoV, offering insights into optimizing FL performance through strategic model adjustments. Numerical results on various datasets show VFed-AMP achieves superior training accuracy (e.g., at least 13.4% improvement for BelgiumTS) and significantly reduces training time (e.g., at least up to <inline-formula><tex-math>$1.8times$</tex-math></inline-formula> for CIFAR-10) compared to traditional FL methods.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"300-316"},"PeriodicalIF":3.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769510","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

User Preferences-Based Proactive Content Caching With Characteristics Differentiation in HetNets 基于用户偏好的HetNets中具有特征差异的主动内容缓存

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-08-12 DOI: 10.1109/TSUSC.2024.3441606

Na Lin;Yamei Wang;Enchao Zhang;Shaohua Wan;Ahmed Al-Dubai;Liang Zhao

{"title":"User Preferences-Based Proactive Content Caching With Characteristics Differentiation in HetNets","authors":"Na Lin;Yamei Wang;Enchao Zhang;Shaohua Wan;Ahmed Al-Dubai;Liang Zhao","doi":"10.1109/TSUSC.2024.3441606","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3441606","url":null,"abstract":"With the proliferation of mobile applications, the explosion of mobile data traffic imposes a significant burden on backhaul links with limited capacity in heterogeneous cellular networks (HetNets). To alleviate this challenge, content caching based on popularity at Small Base Stations (SBSs) has emerged as a promising solution. However, accurately predicting the file popularity profile for SBSs remains a key challenge due to variations in content characteristics and user preferences. Moreover, factors such as content size and the length of time slots (that is, the time duration of the update cycle for SBSs) critically impact the performance of caching schemes with limited storage capacity. In this paper, a <underline>realism-ori<underline>en<underline>ted <underline>intellige<underline>nt c<underline>aching (RETINA) is proposed to address the problem of content caching with unknown file popularity profiles, considering varying content sizes and time slots lengths. Our simulation results demonstrate that RETINA can significantly enhance the cache hit rate by 4%–12% compared to existing content caching schemes.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"333-344"},"PeriodicalIF":3.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769464","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

Self-Sustainable Reconfigurable Intelligent Surface-Empowered D2D Communication Network 自我可持续可重构智能表面授权D2D通信网络

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-08-09 DOI: 10.1109/TSUSC.2024.3441103

Zhixiang Yang;Lei Feng;Fanqin Zhou;Kunyi Xie;Xuesong Qiu;Wenjing Li

{"title":"Self-Sustainable Reconfigurable Intelligent Surface-Empowered D2D Communication Network","authors":"Zhixiang Yang;Lei Feng;Fanqin Zhou;Kunyi Xie;Xuesong Qiu;Wenjing Li","doi":"10.1109/TSUSC.2024.3441103","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3441103","url":null,"abstract":"The reconfigurable intelligent surface (RIS) is a green and promising technology that provides passive beamforming through a large amount of low-power reflecting elements, to realizes expected coverage extension and interference signal suppression. In this paper, we investigate a self-sustainable RIS-empowered D2D communication network, where the RIS first harvests energy from the D2D signals, and then uses energy collected to sustain its passive beamforming operation. We aim to characterize the energy efficiency (EE) maximization under imperfect channel state information conditions by jointly optimizing the transmit precoding in both two stages, RIS passive beamforming design, and energy harvesting time allocation. An efficient alternating optimization algorithm is proposed to deal with the difficult non-convex optimization problem. Specifically, transmit precoding is optimized by using the Dinkelbach's method, Lagrangian dual transform, quadratic transform and S-procedure. The penalty convex-concave procedure is adopted to solve the optimal phase shift of RIS. A closed-form expression for the optimal energy harvesting duration is derived. The simulation results show that the proposed scheme further enhances the EE compared with the active RIS and no RIS schemes in various scenarios.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"287-299"},"PeriodicalIF":3.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769465","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

An Accuracy-Preserving Neural Network Compression via Tucker Decomposition 基于Tucker分解的保持精度的神经网络压缩

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-07-29 DOI: 10.1109/TSUSC.2024.3425962

Can Liu;Kun Xie;Jigang Wen;Gaogang Xie;Kenli Li

{"title":"An Accuracy-Preserving Neural Network Compression via Tucker Decomposition","authors":"Can Liu;Kun Xie;Jigang Wen;Gaogang Xie;Kenli Li","doi":"10.1109/TSUSC.2024.3425962","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3425962","url":null,"abstract":"Deep learning has made remarkable progress across many domains, enabled by the capabilities of over-parameterized neural networks with increasing complexity. However, practical applications often necessitate compact and efficient networks because of device constraints. Among recent low-rank decomposition-based neural network compression techniques, Tucker decomposition has emerged as a promising method which effectively compresses the network while preserving the high-order structure and information of the parameters. Despite its promise, designing an efficient Tucker decomposition approach for compressing neural networks while maintaining accuracy is challenging, due to the complexity of setting ranks across multiple layers and the need for extensive fine-tuning. This paper introduces a novel accuracy-aware network compression problem under Tucker decomposition, which considers both network accuracy and compression performance in terms of parameter size. To address this problem, we propose an efficient alternating optimization algorithm that iteratively solves a network training sub-problem and a Tucker decomposition sub-problem to compress the network with performance assurance. The proper Tucker ranks of multiple layers are selected during network training, enabling efficient compression without extensive fine-tuning. We conduct extensive experiments, implementing image classification on five neural networks using four benchmark datasets. The experimental results demonstrate that, without the need for extensive fine-tuning, our proposed method significantly reduces the model size with minimal loss in accuracy, outperforming baseline methods.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"262-273"},"PeriodicalIF":3.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769406","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

Beyond Text: Detecting Image Propaganda on Online Social Networks 超越文字：检测在线社交网络上的图像宣传

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-07-09 DOI: 10.1109/TSUSC.2024.3424773

Ming-Hung Wang;Yu-Lin Chen

{"title":"Beyond Text: Detecting Image Propaganda on Online Social Networks","authors":"Ming-Hung Wang;Yu-Lin Chen","doi":"10.1109/TSUSC.2024.3424773","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3424773","url":null,"abstract":"The rapid expansion of social media has notably transformed political communication, with politicians and activists increasingly adopting multimedia formats to disseminate their ideologies and policy proposals. This transformation poses a significant risk of propaganda through coordinated campaigns that leverage template-based imagery to spread political messages. To tackle this challenge, our research focuses on developing a detection framework for identifying political images crafted from similar templates, which are a common tool in such propaganda efforts. During a national referendum held in 2021 in Taiwan, we collected visual content from various social networks and implemented a hybrid approach that combines object recognition, textual analysis, and pixel-level information. This methodology is specifically designed to detect patterns and similarities within propaganda images, enabling us to trace and analyze the potentially manipulative content. Our hybrid feature combination technique has demonstrated superior performance compared to several established baseline methods in identifying template-based images. This advancement in detection technology not only enhances the efficiency of researchers studying political communication but also serves as a crucial tool in uncovering and understanding the mechanisms behind potential political propaganda and coordinated efforts to shape public opinion on social media platforms.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"120-131"},"PeriodicalIF":3.0,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184035","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

AOIFF: A Precise Attack Method for PLCs Based on Awareness of Industrial Field Information 基于工业现场信息感知的plc精确攻击方法

IF 3 3区计算机科学

IEEE Transactions on Sustainable Computing Pub Date : 2024-06-26 DOI: 10.1109/TSUSC.2024.3419126

Wenjun Yao;Yanbin Sun;Guodong Wu;Binxing Fang;Yuan Liu;Zhihong Tian

引用次数: 0