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CoSAF: Toward a Secure meta-computing IIoT Infrastructure Through Collaborative Source Address Filtering

IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 13709-13722页

作者： Yang, Shu Zhang, Zequn Cui, Laizhong Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen 518060 Peoples R China

The rapid growth of the Industrial Internet of Things (IIoT) requires a secure meta-computing environment to support applications like industrial monitoring and remote control. However, this environment faces major security challenges, especially the risk of source address forgery, which can enable DDoS and botnet attacks, disrupting operations and compromising equipment. Current Internet infrastructure forwards packets based only on destination addresses, lacking the capability for source address verification. Although edge-based solutions like firewalls and systems, such as source address validation architecture (SAVA) and source address validation improvement (SAVI), are deployed, they fall short of comprehensive source address validation (SAV), allowing malicious traffic to propagate through core networks. To enhance security, a collaborative approach based on meta-computing principles is needed, allowing routers to verify source addresses cooperatively. Given the impracticality of fully upgrading routers, incremental deployment is essential. We show that optimizing incremental SAV deployment is NP-hard. To address this, we propose collaborative optimized source address filtering (COSAF), a heuristic algorithm that uses a sink-tree structure to effectively filter attack flows and optimize resource allocation. COSAF also takes SAV table capacity into account to improve resource utilization. Extensive simulations demonstrate that COSAF outperforms traditional methods.

关键词： Industrial Internet of Things Source address validation Collaboration IP networks Scalability Network topology Faces Denial-of-service attack Resource management Heuristic algorithms Incremental deployment algorithm Industrial Internet of Things (IIoT) intradomain meta-computing source address validation (SAV)

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A meta-computing Framework for Collaborative Federated Graph Learning in Industrial IoT

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 13828-13837页

作者： Zheng, Xu Hu, Xinzhe Wang, Tingqi Huang, Qian Zhang, Lizong Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China Univ Elect Sci & Technol China Sch Aeronaut & Astronaut Chengdu 610054 Peoples R China

Owing to strong capabilities in capturing interactions among objects and concepts, graph data has been treated as an important type of information collected by smart devices in Industrial Internet of Things (IoT), and the distributed training of graph learning models over these devices brings fundamental supports for intelligent services and operations. However, different IoT devices may collect Non-IID graph data due to different roles in the system, and suffer poor performance when only one unified instance of model is trained. Besides, IoT devices usually belong to different communities in Industrial IoT, such that each community pursues both optimized and rational performance when joining in the training process. Considering both challenges, this article proposes a novel meta-computing framework for federated graph learning in Industrial IoT. A collaborative resource allocation task is formulated where devices belonging to different communities adopt limited resources to participate in the training of multiple instances either within or across communities. Two algorithms are introduced for adaptive and rational resource allocation based on whether devices are owned by single or multiple communities. Both algorithms provide guaranteed performance on efficiency and effectiveness, and the fairness among IoT devices are proved. Finally, extensive numerical results have demonstrated the performance of the proposed framework in handling collaborative graph model learning within Industrial IoT.

关键词： Industrial Internet of Things Training Resource management Computational modeling Edge computing Federated learning Collaboration Performance evaluation Data models Distributed databases graph learning industrial Internet of Things (IoT) meta-computing

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Toward Collaborative Intelligence for meta-computing-Driven IIoT Based on Vertical Federated Learning With Fast Convergence

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 13806-13816页

作者： Li, Youqi Liu, Shuangji Meng, Yanchen Qi, Shenyi Qu, Zhe Li, Fan Wang, Yu Beijing Inst Technol Sch Comp Sci & Technol Beijing 100081 Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA

Industrial Internet of Things (IIoT) is an emerging technology that digitizes industrial production and realizes Industry 4.0. However, it shows that IIoT is difficult to enable sophisticated downstream applications without eliciting all devices to achieve collaborative intelligence. Existing works on IIoT either require the consolidation of various IIoT devices' data into a single centralized server which has potential privacy breach, or coordinate devices to learn a global model in privacy-preserving federated learning (FL) but assume data across devices has the sample feature space and neglect the heterogeneity of IIoT devices. In this article, we propose meta-computing-driven vertical FL (VFL) algorithms to achieve collaborative intelligence in IIoT where heterogeneous devices have imperfect data with incomplete features. Specifically, we first provide the modeling of N devices' VFL to collectively train the submodels and the common model. We present the computing graph to clearly indicate the gradient evaluation. To enable a fast convergence performance, we design a variance-reduced gradient estimator that can be seamlessly integrated into the basic VFL. Finally, we evaluate our proposed VFL by conducting experiments on the MNIST dataset regarding image recognition and the DAWM dataset for detecting anomalies in wafer manufacturing. The experimental results show that our VFL for IIoT is both effective and efficient.

关键词： Industrial Internet of Things Collaborative intelligence Convergence Collaboration Computational modeling Artificial intelligence Performance evaluation Predictive models Data models Training Industrial Internet of Things (IIoT) meta-computing vertical federated learning (VFL)

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Area Coverage Reliability Evaluation for Collaborative Intelligence and meta-computing of Decentralized Industrial Internet of Things

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IEEE INTERNET OF THINGS JOURNAL 2025年第10期12卷 13734-13745页

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Liu, Shenghao Yin, Duan Gao, Hanjun Yang, Laurence T. Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China China Mobile Commun Grp Terminal Co Beijing Dept Beijing Peoples R China CNNC Wuhan Nucl Power Operat Technol Co Cyber Secur Technol Dept Wuhan Peoples R China

Industrial Internet of Things (IIoT) is evolving toward decentralization and autonomous operation. Nodes in decentralized IIoT collaboratively sense data and communicate to participate in meta-computing and provide diverse intelligent services. Area coverage reliability evaluates the information collaborative intelligence sensing and communication capabilities of nodes in decentralized IIoT for a target area. It serves as a crucial performance evaluation metric for the meta-computing and intelligent services of decentralized IIoT. Existing area coverage reliability evaluation algorithms do not consider the dynamic node states in decentralized IIoT, and overlook the capability of nodes to collaborate in meta-computing. To address these issues, this article proposes a novel confident information coverage (CIC)-based area coverage reliability metric (CACREL), which comprehensively considers node multistate, node collaboration, network coverage rate, and network connectivity. To effectively evaluate CACREL, a CIC-based coverage reliability evaluation algorithm (CACR) is proposed. Specifically, CACR transforms complex networks into grid networks and utilizes a coverage table (CT) to describe different coverage states of each grid, which reduces the complexity of the area coverage reliability evaluation. Additionally, CACR employs a reliable path algorithm to converge the CT of each grid to the sink node based on grid connectivity. Simulation results demonstrate that the proposed CACR can accurately evaluate the area coverage reliability of decentralized IIoT and enhance its service performance.

关键词： Reliability Industrial Internet of Things Sensors Measurement Collaboration Collaborative intelligence Fans Lower bound Heuristic algorithms Data models Area coverage reliability collaborative intelligence sensing and communication Industrial Internet of Things (IIoT) meta-computing

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Verifiable decentralized identity-based meta-computing in Industrial Internet of Things (IIoT)

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JOURNAL OF SYSTEMS ARCHITECTURE 2025年 162卷

作者： Ding, Kai Xie, Tianxiu Gai, Keke Guo, Chennan Lei, Liangqi Wang, Dongjue Yu, Jing Zhu, Liehuang Meng, Weizhi Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China Minzu Univ China Sch Informat & Engn Minzu Peoples R China Univ Lancaster Sch Comp & Commun Lancaster England

meta-computing in Industrial Internet of Things (IIoT) has triggered a dramatic advance due to the gigantic supports of computation power for processing complex IIoT tasks. However, users identities are encountering security and verification issues since emerging threats derive from dynamic inter-operations in the cross- organization context. Even though blockchain-based Decentralized Identity (DID) is an alternative for offering a strengthened identity governance, current verifiability of DID documents still encounters vulnerabilities due to the involvement of the less trustful third parties that maintain the storage of binding relationships between DID identifiers and public keys. In this paper, we propose a novel Verifiable and Searchable Decentralized Identity (VS-DID) model. We focus on the verifiability of DID documents and propose a verifiable registry scheme that ensures verifiable binding relationships. In order to enable efficient queries in large-scale users' identities in meta-computing IIoT, we develop an on-chain-off-chain query strategy that adopts a slide window accumulator. The experimental results show that our scheme reduces aggregate proof time and commitment time by 93.5% and 96.5%, respectively, compared to the Merkle SNARK scheme, while maintaining reasonable verification time, significantly improving the efficiency of DID registry in large-scale IIoT environments.

关键词： Decentralized identity Verifiable registry meta-computing Trustworthy authentication Blockchain

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Enhanced meta-IDS: Adaptive multi-stage IDS with sequential model adjustments

High-Confidence Computing

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High-Confidence computing 2025年

作者： Nadia Niknami Vahid Mahzoon Slobadan Vucetic Jie Wu Center for Networked Computing Temple University Philadelphia PA USA Center for Hybrid Intelligence Temple University Philadelphia PA USA

Traditional single-machine Network Intrusion Detection Systems (NIDS) are increasingly challenged by rapid network traffic growth and the complexities of advanced neural network methodologies. To address these issues, we propose an Enhanced meta-IDS framework inspired by meta-computing principles, enabling dynamic resource allocation for optimized NIDS performance. Our hierarchical architecture employs a three-stage approach with iterative feedback mechanisms. We leverage these intervals in real-world scenarios with intermittent data batches to enhance our models. Outputs from the third stage provide labeled samples back to the first and second stages, allowing retraining and fine-tuning based on the most recent results without incurring additional latency. By dynamically adjusting model parameters and decision boundaries, our system optimizes responses to real-time data, effectively balancing computational efficiency and detection accuracy. By ensuring that only the most suspicious data points undergo intensive analysis, our multi-stage framework optimizes computational resource usage. Experiments on benchmark datasets demonstrate that our Enhanced meta-IDS improves detection accuracy and reduces computational load or CPU time, ensuring robust performance in high-traffic environments. This adaptable approach offers an effective solution to modern network security challenges.

关键词： Adaptive IDS CPU time Dynamic adaptation Intrusion detection system(IDS) meta-computing

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Dedalo: A Middleware with a Single Point of Access for Heterogeneous Distributed computing Environments

Dedalo: A Middleware with a Single Point of Access for Heter...

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IEEE Symposium on Computers and Communication (ISCC)

作者： Inacio, Eduardo C. Siqueira, Frank Dantas, Mario A. R. Fed Univ Santa Catarina UFSC Dept Informat & Stat INE Florianopolis SC Brazil

ISBN: (纸本)9781509006793

A great challenge faced by many organizations refers to translating their large computing infrastructure into an effective tool to support their productive chain. In this paper, we introduce a modular single point of access middleware approach focused on integrating distributed and heterogeneous computing resources, coined as Dedalo. Since its design, which follows a software product line (SPL) paradigm, Dedalo aims at providing a production-oriented approach to integrate a variety of existing resource managers under a user-friendly interface, with some context-aware approach. Along with the project detailing, a real use case is discussed, presenting the deployment of the Dedalo middleware to support the execution of oil and gas reservoir engineering simulations in an energy company appearing at the 2015 Forbes' Global 2000 companies.

关键词： Grid computing resource management system meta-scheduling meta-computing

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Performance modeling and analysis of heterogeneous meta-computing systems interconnection networks

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COMPUTERS & ELECTRICAL ENGINEERING 2008年第6期34卷 488-502页

作者： Javadi, Bahman Abawajy, Jemal H. Akbari, Mohammad K. Deakin Univ Sch Informat Technol & Engn Geelong Vic 3217 Australia Amirkabir Univ Technol Comp Engn & Informat Technol Dept Tehran Iran

The overall performance of a distributed system often depends oil the effectiveness of its interconnection network. Thus, the study of the communication networks for distributed systems is very important, which is the focus of this paper. In particular, we address the problem of interconnection networks performance modeling for heterogeneous meta-computing systems. We consider the meta-computing system as a typical multi-cluster system. Since the heterogeneity is becoming common in such systems, we take into account network its well as cluster size heterogeneity to propose the model. To this end, we present all analytical network model and validate the model through comprehensive simulation. The results of the simulation demonstrated that the proposed model exhibits a good degree of accuracy for various system organizations and Under different working conditions. (C) 2008 Elsevier Ltd. All rights reserved.

关键词： Analytical modeling Performance analysis meta-computing Multi-cluster Communication networks Heterogeneity

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A Probabilistic Fault-Tolerant Recovery Mechanism for Task and Result Certification of Large-Scale Distributed Applications

A Probabilistic Fault-Tolerant Recovery Mechanism for Task a...

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4th International Conference on Grid and Pervasive computing

作者： Chayeh, Rim Cerin, Christophe Jemni, Mohamed Unite Rech UTIC Ecole Super Sci & Tech Tunis 5 Av Taha HusseinBP 56 Tunis Tunisia Univ Paris CNRS UMR 7030 Inst Galilee LIPN F-93430 Villetaneuse France

ISBN: (纸本)9783642016707

This paper deals with fault tolerant recovery mechanisms and probabilistic results certification issues on large scale architectures. The related works in the result certification domain are based on a total or a partial duplication of the application. However, they are limited to independent tasks executions. In the present work, we extend these mechanisms to dependant tasks applications. First of all we propose an approach, based on an abstract representation of a parallel execution called macro-dataflow graph. Second we introduce probabilistic certification algorithms that avoid the re-execution of the program, allowing for recovery on different platforms under different number of processors. We also sketch how to simulate our framework according to state of the art, modeling workloads and fault injection tools.

关键词： meta-computing Distributed systems Probabilistic certification Recovery Result checking Fault-Tolerance by value

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Simulation of hierarchical resource management for meta-computing systems

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International Journal of Foundations of Computer Science 2001年第5期12卷 629-643页

作者： Santoso, J. Van Albada, G.D. Sloot, P.M.A. Nazief, B.A.A. Department of Computer Science Universiteit Van Amsterdam 1098 SJ Amsterdam Kruislaan 403 Netherlands Department of Computer Science University of Indonesia Jakarta Indonesia

Optimal scheduling in meta-computing environments still is an open research question. Various resource management (RM) architectures have been proposed in the literature (e.g. [2][13][12]). In the present paper we explore, through simulation, various multi-level scheduling strategies for compound computing environments comprising several clusters of workstations. We study global and local RM and their interaction. The local RM comprises both the cluster management and operating system schedulers. Each level refines the scheduling decisions of the layer above it, taking into account the latest resource information. Our experiments explore conventional strategies like First Come, First Served (FCFS) and Shortest Job First (SJF) at the global RM level. At all levels, the schedulers strive to maintain a good load balance. The unit of load balancing at the global level is the job consisting of one or more parallel tasks;at the local level it is the task. The results of our simulations indicate that, especially at high system loads, the use of a global RM can result in a significant performance gain. © 2001 World Scientific Publishing Company.

关键词： Hierarchical RM meta-computing Multilevel scheduling Simulation environment

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