IEEE Transactions on Sustainable Computing最新文献

{"title":"APPARENT: AI-Powered Platform Anomaly Detection in Edge Computing","authors":"Chandrajit Pal;Sangeet Saha;Xiaojun Zhai;Gareth Howells;Klaus D. McDonald-Maier","doi":"10.1109/TSUSC.2025.3562738","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3562738","url":null,"abstract":"Embedded systems serving as IoT nodes are often vulnerable to malicious and unknown runtime software that could compromise the system, steal sensitive data, and cause undesirable system behaviour. Commercially available embedded systems used in automation, medical equipment, and automotive industries, are especially exposed to this vulnerability since they lack the resources to incorporate conventional safety features and are challenging to mitigate through conventional approaches. We propose a novel system design coined as APPARENT which can identify program characteristics by monitoring and counting the maximum possible low-level hardware events from Hardware Performance Counters (HPCs) that occur during the program's execution and analyse the correlation among the counts of various monitored events. To further utilise these captured events as features we propose a self-supervised machine learning algorithm that combines a Graph Attention Network GAT and a Generative Topographic Mapping GTM to detect unusual program behaviour as anomalies to enhance the system security. Our proposed methodology takes advantage of attributes like program counter, cycles per instruction, and physical and virtual timers at various exception levels of the embedded processor to identify abnormal activity. APPARENT identifies unknown program behaviours not present in the training phase with an accuracy of over 98.46% on Autobench EEMBC benchmarks.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"965-981"},"PeriodicalIF":3.9,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248060","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":"Explainable AI-Guided Neural Architecture Search for Adversarial Robustness in Approximate DNNs","authors":"Ayesha Siddique;Khaza Anuarul Hoque","doi":"10.1109/TSUSC.2025.3561603","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3561603","url":null,"abstract":"Deep neural networks are lucrative targets of adversarial attacks and approximate deep neural networks (AxDNNs) are no exception. Searching manually for adversarially robust AxDNN architectures incurs outrageous time and human effort. In this paper, we propose XAI-NAS, an explainable neural architecture search (NAS) method that leverages explainable artificial intelligence (XAI) to efficiently co-optimize the adversarial robustness and hardware efficiency of AxDNN architectures on systolic-array hardware accelerators. During the NAS process, AxDNN architectures are evolved layer-wise with heterogeneous approximate multipliers to deliver the best trade-offs between adversarial robustness, energy consumption, latency, and memory footprint. The most suitable approximate multipliers are automatically selected from an open-source Evoapprox8b library. Our extensive evaluations provide a set of Pareto optimal hardware efficient and adversarially robust solutions. For example, a Pareto-optimal DNN AxDNN for the MNIST and CIFAR-10 datasets exhibits up to 1.5× higher adversarial robustness, 2.1× less energy consumption, 4.39× reduced latency, and 2.37× low memory footprint when compared to the state-of-the-art NAS approaches.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"949-964"},"PeriodicalIF":3.9,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248094","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":"Novel Stealth Communication Round Attack and Robust Incentivized Federated Averaging for Load Forecasting","authors":"Habib Ullah Manzoor;Kamran Arshad;Khaled Assaleh;Ahmed Zoha","doi":"10.1109/TSUSC.2025.3570096","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3570096","url":null,"abstract":"Federated learning (FL) has gained prominence in energy forecasting applications. Despite its advantages, FL remains vulnerable to adversarial attacks that threaten the reliability of predictive models. This study introduces a stealth attack, Federated Communication Round Attack (Fed-CRA), which increases communication rounds without affecting forecasting accuracy. Increased communication rounds can delay decision-making, reducing system responsiveness and cost-effectiveness in dynamic energy forecasting scenarios. Experimental validation on two datasets demonstrated that Fed-CRA increased communication rounds by 574% (from 72 to 485) in the AEP dataset and by 237% (from 92 to 310) in the COMED dataset. This led to a corresponding rise in energy consumption by 573% (from 41.04 kWh to 276.35 kWh) and 237% (from 52.44 kWh to 176.65 kWh), respectively, while preserving forecasting accuracy. To counter this attack, we proposed Federated Incentivized Averaging (Fed-InA), a game theory-inspired framework that rewards honest clients and penalizes dishonest ones based on their contributions. Results showed that Fed-InA reduced the additional communication rounds caused by Fed-CRA by 85% in the AEP dataset and 70% in the COMED dataset, while maintaining forecasting performance. Fed-InA achieves resource efficiency comparable to Federated Averaging (FedAvg) and demonstrates robustness in handling non-IID data.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"1007-1018"},"PeriodicalIF":3.9,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248080","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":"Wireless Rechargeable Sensor Networks: Energy Provisioning Technologies, Charging Scheduling Schemes, and Challenges","authors":"Samah Abdel Aziz;Xingfu Wang;Ammar Hawbani;Bushra Qureshi;Saeed H. Alsamhi;Aisha Alabsi;Liang Zhao;Ahmed Al-Dubai;A.S. Ismail","doi":"10.1109/TSUSC.2025.3549414","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3549414","url":null,"abstract":"Recently, a plethora of promising green energy provisioning technologies has been discussed in the orientation of prolonging the lifetime of energy-limited devices (e.g., sensor nodes). Wireless rechargeable sensor networks (WRSNs) have emerged among other fields that could greatly benefit from such technologies. Such an ad-hoc network comprises a base station(s) and multiple sensor nodes, which are primarily deployed in harsh environments, meeting the requirements of transmitting, receiving, collecting, and processing data. Unlike existing works, this survey paper focuses on energy provisioning technologies within the context of WRSNs by reviewing two interrelated domains. First, we introduce various energy provisioning techniques and their associated challenges, including conventional energy harvesting methods (e.g., solar, thermal, and mechanical). We highlight wireless power transfer (WPT) as one of the most applicable technologies for WRSNs, covering both radiative and non-radiative WPT. Additionally, we present radio frequency (RF) energy harvesting, including simultaneous wireless information and power transfer (SWIPT) and wireless powered communication networks (WPCNs), as well as backscatter communications. Furthermore, we compare hybrid energy harvesting techniques (e.g., solar-RF, vibro-acoustic, solar-thermal, etc.). Second, we introduce the fundamentals of wireless charging, reviewing various charger types (static and mobile), charging policies (including full and partial charging), charging modes (offline and online), and charging schemes (periodic and on-demand). We also present the collaborative charging mechanisms. Additionally, we address several key challenges facing WRSNs, such as energy consumption, multi-charger coordination, dynamic network recharging, monitoring & security threats, vehicle-to-vehicle (V2V) charging, and hybrid WRSNs Finally, we highlight trends and future directions for integrating advanced artificial intelligence (AI) technologies into WRSNs.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"873-890"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248058","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":"$x$xPUE: Extending Power Usage Effectiveness Metrics For Cloud Infrastructures","authors":"Guillaume Fieni;Romain Rouvoy;Lionel Seinturier","doi":"10.1109/TSUSC.2025.3549687","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3549687","url":null,"abstract":"The energy consumption analysis and optimization of data centers have been an increasingly popular topic over the past few years. It is widely recognized that several effective metrics exist to capture the efficiency of hardware and/or software hosted in these infrastructures. Unfortunately, choosing the corresponding metrics for specific infrastructure and assessing its efficiency over time is still considered an open problem. For this purpose, energy efficiency metrics, such as the <i>Power Usage Effectiveness</i> (PUE), assess the efficiency of the computing equipment of the infrastructure. However, this metric stops at the power supply of hosted servers and fails to offer a finer granularity to bring a deeper insight into the <i>Power Usage Effectiveness</i> of hardware and software running in cloud infrastructure. Therefore, we propose to leverage complementary PUE metrics, coined <inline-formula><tex-math>$x$</tex-math></inline-formula>PUE, to compute the energy efficiency of the computing continuum from hardware components, up to the running software layers. Our contribution aims to deliver real-time energy efficiency metrics from different perspectives for cloud infrastructure, hence helping cloud ecosystems—from cloud providers to their customers—to experiment and optimize the energy usage of cloud infrastructures at large.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"908-920"},"PeriodicalIF":3.9,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248043","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":"Data-Driven Software-Based Power Estimation for Embedded Devices","authors":"Haoyu Wang;Xinyi Li;Ti Zhou;Man Lin","doi":"10.1109/TSUSC.2025.3567856","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3567856","url":null,"abstract":"Energy measurement of computer devices, which are widely used in the Internet of Things (IoT), is an important yet challenging task. Most of these IoT devices lack ready-to-use hardware or software for power measurement. In this paper, we propose an easy-to-use approach to derive a software-based energy estimation model with external low-end power meters based on data-driven analysis. Our solution is demonstrated with a Jetson Nano board and Ruideng UM25C USB power meter. Various machine learning methods combined with our smart data collection & profiling method and physical measurement are explored. Periodic Long-duration measurements are utilized in the experiments to derive and validate power models, allowing more accurate power readings from the low-end power meter. Benchmarks were used to evaluate the derived software-power model for the Jetson Nano board and Raspberry Pi. The results show that 92% accuracy can be achieved by the software-based power estimation compared to measurement. A kernel module that can collect running traces of utilization and frequencies needed is developed, together with the power model derived, for power prediction for programs running in a real environment. Our cost-effective method facilitates accurate instantaneous power estimation, which low-end power meters cannot directly provide.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"937-948"},"PeriodicalIF":3.9,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248099","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-Aware Virtual Machine Scheduling Approach for Cloud Data Centers","authors":"Jie Li;Yuhui Deng;Zijie Zhong;Zhaorui Wu;Shujie Pang;Lin Cui;Geyong Min","doi":"10.1109/TSUSC.2025.3549001","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3549001","url":null,"abstract":"The reduction of energy consumption will be even more urgent in cloud data centers due to the explosive increase of application data. Virtual machine (VM) integration is a relatively standard technology currently applied for computing facilities of data centers. However, excessive VM consolidation can easily lead to local hot spots that lower the energy efficiency and reliability of data centers. In addition, on account of the impact of heat recirculation in data centers, the traditional VM scheduling strategy cannot comprehensively ponder optimizing the holistic data center energy, which encompasses both server energy and cooling energy. To handle these issues, we proposed <i>EAVMS</i>- an Energy-Aware VM Scheduling approach for minimizing the holistic energy consumption of data centers. EAVMS adopts a two-phase approach to gain energy efficiency while guaranteeing QoS. First, EAVMS leverages a Blended Genetic algorithm and Simulated Annealing algorithm (BGSA) to optimize the initial placement of VMs. Second, EAVMS utilizes a dynamic migration algorithm to achieve effective migration by setting a maximum server temperature threshold without violating the service level agreement (SLA) that cuts down energy consumption by moderating the hot spots of servers. We conducted extensive experiments using two real-world traces (i.e., PlanetLab and Google Cluster datasets) to evaluate the effectiveness of EAVMS. The experimental results unveil that our approach is capable of saving 3.23<inline-formula><tex-math>$ %$</tex-math></inline-formula>–43.07<inline-formula><tex-math>$ %$</tex-math></inline-formula> in the holistic energy consumption of cloud data centers with only a tiny service performance degradation compared to other state-of-the-art alternatives (e.g., MJPM, GRANITE, TAS, XINT-GA, and Random).","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"891-907"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248077","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":"Speed up Federated Unlearning With Temporary Local Models","authors":"Muhammad Ameen;Pengfei Wang;Weijian Su;Xiaopeng Wei;Qiang Zhang","doi":"10.1109/TSUSC.2025.3549112","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3549112","url":null,"abstract":"Federated unlearning (FUL) is a solution aimed at addressing the problem of removing data contributions from trained federated learning (FL) models. Existing FUL methods only focus on iterative unlearning of clients’ contributions and fail to perform unlearning in scenarios where multiple clients request to remove their data at a time. Additionally, FUL still needs to address issues, including convergence speed, maintaining the global model’s performance, and parallel unlearning to expedite the unlearning process. To fill this gap, we introduce Federated Clients Forgetting (FedCF), a fast and accurate FUL method that can eliminate single client contributions similar to existing methods, eliminate multiple clients’ contributions on the global model parallelly, ensure the performance of the unlearned global model, and reduce the unlearning time. The key idea is to construct a temporary model by extracting knowledge from the remaining clients’ updates and adding it to the corresponding parameters of the initial global model and then leverage a temporary model to reconstruct the unlearned global model. Extensive experiments on three benchmark datasets, FedCF demonstrates its efficiency and effectiveness for single client contribution unlearning, achieving an average time efficiency of 8.3x, 6.5x, and 4.1x over existing methods FedRetrain, FedEraser, and FUL with knowledge distillation, respectively. Additionally, FedCF showcases the time efficiency and performance guarantee after unlearning the contributions of multiple clients in parallel.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"921-936"},"PeriodicalIF":3.9,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248089","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":"Semantic Communication-Based Low-Carbon Sustainable Framework for Person Re-Identification","authors":"Hao Liu;Wenhan Long;Xinlong Wen;Zhida Guo;Lu Liu;Rongbo Zhu","doi":"10.1109/TSUSC.2025.3566622","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3566622","url":null,"abstract":"Person re-identification (Re-ID) is a critical technology in security systems and video surveillance. However, most of the existing methods focused on precise Re-ID, which not only neglect the transmission overheads, computing energy consumption and carbon emissions, but are unsustainable. Furthermore, the personal semantics is usually blurred and distorted in real-world scenarios due to the bird’s eye view (BEV) of cameras. Cross-illumination and face-coverings also weakened the key personal semantics. Such deficiencies have resulted in a substantial amount of carbon emissions and poor Re-ID performance. To reduce the video transmission overheads, computing energy consumption and carbon emissions yet guaranteeing the accuracy of Re-ID, this paper proposes a novel semantic communication-based low-carbon sustainable framework (SC-LCSF) for Re-ID. SC-LCSF adopts the semantic encoder based on an enhanced semantics-aware attention mechanism (ESA-SE) to extract the personal semantics. Only semantic information is transmitted at the semantic layer, which is then decoded into personal IDs by the multi-granularity semantic decoder (MG-SD). Two widely used public datasets, Market-1501 and CUHK03, and a newly curated real-world dataset, HZAU-SCUEC01, are used to train SC-LCSF and to evaluate its performance. Experimental results show that compared to the state-of-the-art (SOTA) methods, SC-LCSF achieves the best Rank-1 and mAP accuracy on all the datasets. Furthermore, SC-LCSF has a significant performance enhancement in low-carbon sustainable computing – the transmission data amount, CPU power consumption, CPU temperature, GPU power consumption, GPU temperature and Re-ID delay have a reduction of 96.8%, 39.6%, 27.9%, 40.9%, 29.7%, and 76.6%, respectively.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"982-992"},"PeriodicalIF":3.9,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248097","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":"Storage Scalability Oriented Segment Allocation Based on Cost Clustering in Sharding Blockchains","authors":"Liping Tao;Yang Lu;Yuqi Fan;Lei Shi;Zhen Wei","doi":"10.1109/TSUSC.2025.3566072","DOIUrl":"https://doi.org/10.1109/TSUSC.2025.3566072","url":null,"abstract":"Blockchain technology has garnered significant attention from academia and industry, with scalability remaining a key challenge. Sharding is a promising solution, dividing the blockchain into smaller partitions called shards, each processing a portion of the transactions to increase throughput. This approach is critical for enabling efficient Proof of Stake (PoS) consensus mechanisms, as demonstrated by the transition of Dogecoin to PoS, where sharding reduces the computational burden on validators and enhances scalability. However, sharding introduces high storage redundancy, as nodes in each shard must collectively maintain a copy of the entire blockchain, imposing substantial storage pressure. To address this, segments are introduced to divide the main chain into smaller parts distributed across nodes. Existing methods, however, randomly assign segments to nodes, resulting in high costs for node setup and segment queries. This paper investigates the optimal allocation of segments within shards to minimize these costs, proposing a Segment Allocation algorithm based on Cost Clustering (SACC). Theoretical analysis and simulations demonstrate that SACC achieves lower setup, query, and total costs while maintaining security and scalability, offering a more efficient solution for sharding-based PoS blockchains like Dogecoin.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 5","pages":"993-1006"},"PeriodicalIF":3.9,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145248066","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}