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.
{"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}
Pub Date : 2024-08-13DOI: 10.1109/TSUSC.2024.3442918
Haneul Ko;Hongrok Choi;Sangheon Pack
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.
{"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}
Pub Date : 2024-08-12DOI: 10.1109/TSUSC.2024.3441658
Xing Chang;Mohammad S. Obaidat;Jingxiao Ma;Xiaoping Xue;Yantao Yu;Xuewen Wu
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 $1.8times$ for CIFAR-10) compared to traditional FL methods.
{"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}
Pub Date : 2024-08-12DOI: 10.1109/TSUSC.2024.3441606
Na Lin;Yamei Wang;Enchao Zhang;Shaohua Wan;Ahmed Al-Dubai;Liang Zhao
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 realism-oriented intelligent caching (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.
{"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>r</u>ealism-ori<underline>e</u>n<underline>t</u>ed <underline>i</u>ntellige<underline>n</u>t c<underline>a</u>ching (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}
Pub Date : 2024-08-09DOI: 10.1109/TSUSC.2024.3441103
Zhixiang Yang;Lei Feng;Fanqin Zhou;Kunyi Xie;Xuesong Qiu;Wenjing Li
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.
{"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}
Pub Date : 2024-07-29DOI: 10.1109/TSUSC.2024.3425962
Can Liu;Kun Xie;Jigang Wen;Gaogang Xie;Kenli Li
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.
{"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}
Pub Date : 2024-07-09DOI: 10.1109/TSUSC.2024.3424773
Ming-Hung Wang;Yu-Lin Chen
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.
{"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}
PLC, as the core of industrial control systems, has been turned into a focal point of research for attackers targeting industrial control systems. However, current researched methods for attacking PLCs suffer from issues such as lack of precision and limited specificity. This paper proposes a novel attack method called AOIFF. Specially, AOIFF extracts the binary control logic code from a running PLC and reverses the binary code into assemble code. And then awareness of industrial field information is extracted from assemble code. Finally, it is based on awareness that attack code is generated and injected into a PLC, which can disrupt the normal control logic and then launch precise attacks on industrial control systems. Experimental results demonstrate that AOIFF can effectively perceive information in industrial field and initiate precise and targeted attacks on industrial control systems. Additionally, AOIFF achieves excellent results in the reverse engineering of binary code, enabling effective analysis of binary code.
{"title":"AOIFF: A Precise Attack Method for PLCs Based on Awareness of Industrial Field Information","authors":"Wenjun Yao;Yanbin Sun;Guodong Wu;Binxing Fang;Yuan Liu;Zhihong Tian","doi":"10.1109/TSUSC.2024.3419126","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3419126","url":null,"abstract":"PLC, as the core of industrial control systems, has been turned into a focal point of research for attackers targeting industrial control systems. However, current researched methods for attacking PLCs suffer from issues such as lack of precision and limited specificity. This paper proposes a novel attack method called AOIFF. Specially, AOIFF extracts the binary control logic code from a running PLC and reverses the binary code into assemble code. And then awareness of industrial field information is extracted from assemble code. Finally, it is based on awareness that attack code is generated and injected into a PLC, which can disrupt the normal control logic and then launch precise attacks on industrial control systems. Experimental results demonstrate that AOIFF can effectively perceive information in industrial field and initiate precise and targeted attacks on industrial control systems. Additionally, AOIFF achieves excellent results in the reverse engineering of binary code, enabling effective analysis of binary code.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"232-243"},"PeriodicalIF":3.0,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769462","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}
Pub Date : 2024-06-24DOI: 10.1109/TSUSC.2024.3418136
Walid Osamy;Ahmed M. Khedr;Ahmed Salim
Load balancing in IoT-based Wireless Sensor Networks (WSNs) is essential for improving energy efficiency, reliability, and network lifetime, promoting the development of smart and sustainable cities through informed decision-making and resource optimization. This paper introduces a Workload Aware Clustering Technique (WLACT) to enhance energy efficiency and extend the network lifespan of IoT-based WSNs. WLACT focuses on overcoming challenges such as uneven workload distribution and complex scheme designs in existing clustering methods, highlighting the importance of load balancing, optimized data aggregation, and effective energy resource management in IoT-based heterogeneous WSNs. WLACT adapts Chicken Swarm Optimization (CSO) for efficient workload-aware clustering of WSNs, while also introducing the concept of average imbalanced workload parameter for clustered WSNs and utilizing it as an evaluation metric. By considering node heterogeneity and formulating an objective function to minimize workload imbalances among nodes during clustering, WLACT aims to achieve efficient energy resource utilization, improved reliability, and long-term operational support within smart city environments. A new cluster joining procedure for non-CHs based on multiple factors is also designed. Results reveal the superior performance of WLACT in terms of energy efficiency, workload balance, reliability, and network lifetime, making it a promising technique for sustainable smart city development.
{"title":"Design Workload Aware Data Collection Technique for IoT-enabled WSNs in Sustainable Smart Cities","authors":"Walid Osamy;Ahmed M. Khedr;Ahmed Salim","doi":"10.1109/TSUSC.2024.3418136","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3418136","url":null,"abstract":"Load balancing in IoT-based Wireless Sensor Networks (WSNs) is essential for improving energy efficiency, reliability, and network lifetime, promoting the development of smart and sustainable cities through informed decision-making and resource optimization. This paper introduces a Workload Aware Clustering Technique (WLACT) to enhance energy efficiency and extend the network lifespan of IoT-based WSNs. WLACT focuses on overcoming challenges such as uneven workload distribution and complex scheme designs in existing clustering methods, highlighting the importance of load balancing, optimized data aggregation, and effective energy resource management in IoT-based heterogeneous WSNs. WLACT adapts Chicken Swarm Optimization (CSO) for efficient workload-aware clustering of WSNs, while also introducing the concept of average imbalanced workload parameter for clustered WSNs and utilizing it as an evaluation metric. By considering node heterogeneity and formulating an objective function to minimize workload imbalances among nodes during clustering, WLACT aims to achieve efficient energy resource utilization, improved reliability, and long-term operational support within smart city environments. A new cluster joining procedure for non-CHs based on multiple factors is also designed. Results reveal the superior performance of WLACT in terms of energy efficiency, workload balance, reliability, and network lifetime, making it a promising technique for sustainable smart city development.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 2","pages":"244-261"},"PeriodicalIF":3.0,"publicationDate":"2024-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769402","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}
This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between the high-tier and low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.
{"title":"Merged Path: Distributed Data Dissemination in Mobile Sinks Sensor Networks","authors":"Xingfu Wang;Ammar Hawbani;Liang Zhao;Saeed Hamood Alsamhi;Wajdy Othman;Mohammed A.A. Al-qaness;Alexey V. Shvetsov","doi":"10.1109/TSUSC.2024.3410247","DOIUrl":"https://doi.org/10.1109/TSUSC.2024.3410247","url":null,"abstract":"This paper studies distributed data dissemination in multiple mobile sinks wireless sensor networks. Previous studies employed separated paths to disseminate data packets from a given source to a given set of mobile sinks independently, which exhausts the constrained resources of the network. In this paper, we explore how the merged paths mechanism could rationalize utilizing network resources. To do so, we propose a protocol named Merged Path, which is implemented in four steps in a distributed manner. First, the bifurcation points (i.e., where the path is branched into multiple sub-branches) are discovered. Second, we developed a Discrete Cumulative Clustering algorithm (DCC) to divide the sinks into disjoint clusters at each bifurcation point. Third, we propose a Diagonal Virtual Line (DVL) structure to delegate the communication between the <italic>high-tier</i> and low-tier nodes. Last, on top of DVL and DCC, we propose an opportunistic metric that captures multiple network-layer attributes to disseminate the data packet to the sinks through multiple branches. The simulation results showed that about 50% of the network energy could be saved by merging the paths versus the separate paths, considering an area of interest application with 20 mobile nodes each carrying a sink.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":"10 1","pages":"161-175"},"PeriodicalIF":3.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184133","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}
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