Pub Date : 2024-12-01Epub Date: 2024-03-26DOI: 10.1016/j.hcc.2024.100224
The Graph isomorphism problem involves determining whether two graphs are isomorphic and the computational complexity required for this determination. In general, the problem is not known to be solvable in polynomial time, nor to be NP-complete. In this paper, by analyzing the algebraic properties of the adjacency matrices of the undirected graph, we first established the connection between graph isomorphism and matrix row and column interchanging operations. Then, we prove that for undirected graphs, the complexity in determining whether two graphs are isomorphic is at most .
{"title":"Graph isomorphism—Characterization and efficient algorithms","authors":"","doi":"10.1016/j.hcc.2024.100224","DOIUrl":"10.1016/j.hcc.2024.100224","url":null,"abstract":"<div><div>The Graph isomorphism problem involves determining whether two graphs are isomorphic and the computational complexity required for this determination. In general, the problem is not known to be solvable in polynomial time, nor to be NP-complete. In this paper, by analyzing the algebraic properties of the adjacency matrices of the undirected graph, we first established the connection between graph isomorphism and matrix row and column interchanging operations. Then, we prove that for undirected graphs, the complexity in determining whether two graphs are isomorphic is at most <span><math><mrow><mi>O</mi><mrow><mo>(</mo><msup><mrow><mi>n</mi></mrow><mrow><mn>3</mn></mrow></msup><mo>)</mo></mrow></mrow></math></span>.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100224"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140402465","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-02-28DOI: 10.1016/j.hcc.2024.100212
In the evolving landscape of the digital era, edge computing emerges as an essential paradigm, especially critical for low-latency, real-time applications and Internet of Things (IoT) environments. Despite its advantages, edge computing faces severe limitations in storage capabilities and is fraught with reliability issues due to its resource-constrained nature and exposure to challenging conditions. To address these challenges, this work presents a tailored storage mechanism for edge computing, focusing on space efficiency and data reliability. Our method comprises three key steps: relation factorization, column clustering, and erasure encoding with compression. We successfully reduce the required storage space by deconstructing complex database tables and optimizing data organization within these sub-tables. We further add a layer of reliability through erasure encoding. Comprehensive experiments on TPC-H datasets substantiate our approach, demonstrating storage savings of up to 38.35% and time efficiency improvements by 3.96x in certain cases. Furthermore, our clustering technique shows a potential for additional storage reduction up to 40.41%.
{"title":"Data cube-based storage optimization for resource-constrained edge computing","authors":"","doi":"10.1016/j.hcc.2024.100212","DOIUrl":"10.1016/j.hcc.2024.100212","url":null,"abstract":"<div><div>In the evolving landscape of the digital era, edge computing emerges as an essential paradigm, especially critical for low-latency, real-time applications and Internet of Things (IoT) environments. Despite its advantages, edge computing faces severe limitations in storage capabilities and is fraught with reliability issues due to its resource-constrained nature and exposure to challenging conditions. To address these challenges, this work presents a tailored storage mechanism for edge computing, focusing on space efficiency and data reliability. Our method comprises three key steps: relation factorization, column clustering, and erasure encoding with compression. We successfully reduce the required storage space by deconstructing complex database tables and optimizing data organization within these sub-tables. We further add a layer of reliability through erasure encoding. Comprehensive experiments on TPC-H datasets substantiate our approach, demonstrating storage savings of up to 38.35% and time efficiency improvements by 3.96x in certain cases. Furthermore, our clustering technique shows a potential for additional storage reduction up to 40.41%.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100212"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140464490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-04-16DOI: 10.1016/j.hcc.2024.100228
By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here, in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids instructors in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.
通过分析在线学习（OL）系统收集到的数据，数据挖掘可以用来发现主题之间隐藏的关系和学生成绩的趋势。在本文中，我们展示了如何将聚类和关联规则算法等数据挖掘技术用于历史数据，以开发独特的推荐系统模块。在我们的实施过程中，我们利用历史数据生成关联规则，专门针对低于 60% 临界值的学生考试分数。通过关注低于这一阈值的分数，我们旨在根据过去数据中观察到的薄弱环节模式来识别和建立关联。此外，我们还利用 K 均值聚类为教师提供生成关联的可视化表示。这一策略有助于教师更好地理解算法生成的信息和关联。K-means 聚类有助于以可视化的方式组织数据，使教师更容易分析和洞察，从而为验证主题之间的关系提供支持。这可以成为一个有用的工具，为学生提供更好的反馈，并为教师在制定教学法时提供更好的见解。本文进一步展示了上述概念的原型实现，以获得有关拟议系统可用性和可行性的意见和见解。
{"title":"Unsupervised machine learning approach for tailoring educational content to individual student weaknesses","authors":"","doi":"10.1016/j.hcc.2024.100228","DOIUrl":"10.1016/j.hcc.2024.100228","url":null,"abstract":"<div><div>By analyzing data gathered through Online Learning (OL) systems, data mining can be used to unearth hidden relationships between topics and trends in student performance. Here, in this paper, we show how data mining techniques such as clustering and association rule algorithms can be used on historical data to develop a unique recommendation system module. In our implementation, we utilize historical data to generate association rules specifically for student test marks below a threshold of 60%. By focusing on marks below this threshold, we aim to identify and establish associations based on the patterns of weakness observed in the past data. Additionally, we leverage K-means clustering to provide instructors with visual representations of the generated associations. This strategy aids instructors in better comprehending the information and associations produced by the algorithms. K-means clustering helps visualize and organize the data in a way that makes it easier for instructors to analyze and gain insights, enabling them to support the verification of the relationship between topics. This can be a useful tool to deliver better feedback to students as well as provide better insights to instructors when developing their pedagogy. This paper further shows a prototype implementation of the above-mentioned concepts to gain opinions and insights about the usability and viability of the proposed system.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100228"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140782041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-05-18DOI: 10.1016/j.hcc.2024.100241
In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.
{"title":"A survey of acoustic eavesdropping attacks: Principle, methods, and progress","authors":"","doi":"10.1016/j.hcc.2024.100241","DOIUrl":"10.1016/j.hcc.2024.100241","url":null,"abstract":"<div><div>In today’s information age, eavesdropping has been one of the most serious privacy threats in information security, such as exodus spyware (Rudie et al., 2021) and pegasus spyware (Anatolyevich, 2020). And the main one of them is acoustic eavesdropping. Acoustic eavesdropping (George and Sagayarajan, 2023) is a technology that uses microphones, sensors, or other devices to collect and process sound signals and convert them into readable information. Although much research has been done in this area, there is still a lack of comprehensive investigation into the timeliness of this technology, given the continuous advancement of technology and the rapid development of eavesdropping methods. In this article, we have given a selective overview of acoustic eavesdropping, focusing on the methods of acoustic eavesdropping. More specifically, we divide acoustic eavesdropping into three categories: motion sensor-based acoustic eavesdropping, optical sensor-based acoustic eavesdropping, and RF-based acoustic eavesdropping. Within these three representative frameworks, we review the results of acoustic eavesdropping according to the type of equipment they use and the physical principles of each. Secondly, we also introduce several important but challenging applications of these acoustic eavesdropping methods. In addition, we compared the systems that meet the requirements of acoustic eavesdropping in real-world scenarios from multiple perspectives, including whether they are non-intrusive, whether they can achieve unconstrained word eavesdropping, and whether they use machine learning, etc. The general template of our article is as follows: firstly, we systematically review and classify the existing eavesdropping technologies, elaborate on their working mechanisms, and give corresponding formulas. Then, these eavesdropping methods were compared and analyzed, and each method’s effectiveness and technical difficulty were evaluated from multiple dimensions. In addition to an assessment of the current state of the field, we discuss the current shortcomings and challenges and give a fruitful direction for the future of acoustic eavesdropping research. We hope to continue to inspire researchers in this direction.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100241"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141138505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-04-04DOI: 10.1016/j.hcc.2024.100225
A content delivery network (CDN) aims to reduce the content delivery latency to end-users by using distributed cache servers. Nevertheless, deploying and maintaining cache servers on a large scale is very expensive. To solve this problem, CDN providers have developed a new content delivery strategy: allowing end-users’s IoT edge devices to share their storage/bandwidth resources. This new edge CDN platform must address two core questions: (1) how can we incentivize end users to share IoT devices? (2) how can we facilitate a safe and transparent content transaction environment for end users? This paper introduces SmartSharing, a new content delivery network solution to address these questions. In smartSharing, the over-the-top (OTT) IoT devices belonging to end-users are used as mini-cache servers. To motivate end users to share the idle devices and storage/bandwidth resources, SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms (specifically, a tailored expectation-maximization (EM) algorithm). To facilitate content trading among end users, SmartSharing creates a secure and transparent transaction platform based on smart contracts in Ethereum. In addition, SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing schemes. The evaluation results show that CDN providers, end users and content providers can all benefit from our SmartSharing framework.
{"title":"Intelligent edge CDN with smart contract-aided local IoT sharing","authors":"","doi":"10.1016/j.hcc.2024.100225","DOIUrl":"10.1016/j.hcc.2024.100225","url":null,"abstract":"<div><div>A content delivery network (CDN) aims to reduce the content delivery latency to end-users by using distributed cache servers. Nevertheless, deploying and maintaining cache servers on a large scale is very expensive. To solve this problem, CDN providers have developed a new content delivery strategy: allowing end-users’s IoT edge devices to share their storage/bandwidth resources. This new edge CDN platform must address two core questions: (1) how can we incentivize end users to share IoT devices? (2) how can we facilitate a safe and transparent content transaction environment for end users? This paper introduces SmartSharing, a new content delivery network solution to address these questions. In smartSharing, the over-the-top (OTT) IoT devices belonging to end-users are used as mini-cache servers. To motivate end users to share the idle devices and storage/bandwidth resources, SmartSharing designs the content delivery schedule and the pricing scheme based on game theory and machine learning algorithms (specifically, a tailored expectation-maximization (EM) algorithm). To facilitate content trading among end users, SmartSharing creates a secure and transparent transaction platform based on smart contracts in Ethereum. In addition, SmartSharing’s performance evaluation is through trace-driven simulations in the real world and a prototype using content metadata and the achieved pricing schemes. The evaluation results show that CDN providers, end users and content providers can all benefit from our SmartSharing framework.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100225"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140771215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-02-02DOI: 10.1016/j.hcc.2024.100210
Hongwei Shi , Chenyu Wang , Hang Zhao , Shengling Wang , Yixian Chen
The global maritime trade plays a key role in propagating alien aquatic invasive species, which incurs side effects in terms of environment, human health and economy. The existing biosecurity methods did not take into account the invaded risk as well as the diffusion of invasive species at the same time, which may lead to inadequate bioinvasion control. In addition, the lack of considering the impact of bioinvasion control on shipping also makes their methods cost-ineffective. To solve the problems of the existing methods, we employ the automatic identification system (AIS) data, the ballast water data and the water temperature & salinity data to construct two networks: the species invasion network (SIN) and the global shipping network (GSN). The former is used to analyze the potential of a port in propagating marine invasive species while the latter is employed to evaluate the shipping importance of ports. Based on the analysis of SIN and GSN, two categories of biosecurity triggering mechanisms are proposed. The first category takes into consideration both being bioinvaded and spreading invasive species and the second one concerns the shipping value of each port besides its invasion risk. A lot of case studies have been done to discover the key ports needed to be controlled preferentially under the guide of the proposed biosecurity triggering mechanisms. Finally, our correlation analysis shows that closeness is most highly correlated to the invasion risk.
全球海上贸易在传播外来水生入侵物种方面发挥着关键作用，对环境、人类健康和经济造成了副作用。现有的生物安全方法没有同时考虑入侵风险和入侵物种的扩散，这可能导致生物入侵控制不力。此外，由于没有考虑生物入侵控制对航运的影响，其方法也不符合成本效益。为了解决现有方法存在的问题，我们利用自动识别系统（AIS）数据、压载水数据和水温& 盐度数据构建了两个网络：物种入侵网络（SIN）和全球航运网络（GSN）。前者用于分析港口传播海洋入侵物种的潜力，后者用于评估港口航运的重要性。根据对 SIN 和 GSN 的分析，提出了两类生物安全触发机制。第一类既考虑到生物入侵，也考虑到入侵物种的传播；第二类除了考虑入侵风险，还考虑到每个港口的航运价值。通过大量的案例研究，我们发现了在所提出的生物安全触发机制指导下需要优先控制的关键港口。最后，我们的相关性分析表明，近距离与入侵风险的相关性最高。
{"title":"Bioinvasion risk analysis based on automatic identification system and marine ecoregion data","authors":"Hongwei Shi , Chenyu Wang , Hang Zhao , Shengling Wang , Yixian Chen","doi":"10.1016/j.hcc.2024.100210","DOIUrl":"10.1016/j.hcc.2024.100210","url":null,"abstract":"<div><div>The global maritime trade plays a key role in propagating alien aquatic invasive species, which incurs side effects in terms of environment, human health and economy. The existing biosecurity methods did not take into account the invaded risk as well as the diffusion of invasive species at the same time, which may lead to inadequate bioinvasion control. In addition, the lack of considering the impact of bioinvasion control on shipping also makes their methods cost-ineffective. To solve the problems of the existing methods, we employ the automatic identification system (AIS) data, the ballast water data and the water temperature & salinity data to construct two networks: the species invasion network (SIN) and the global shipping network (GSN). The former is used to analyze the potential of a port in propagating marine invasive species while the latter is employed to evaluate the shipping importance of ports. Based on the analysis of SIN and GSN, two categories of biosecurity triggering mechanisms are proposed. The first category takes into consideration both being bioinvaded and spreading invasive species and the second one concerns the shipping value of each port besides its invasion risk. A lot of case studies have been done to discover the key ports needed to be controlled preferentially under the guide of the proposed biosecurity triggering mechanisms. Finally, our correlation analysis shows that closeness is most highly correlated to the invasion risk.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100210"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139816780","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01Epub Date: 2024-05-10DOI: 10.1016/j.hcc.2024.100227
The growing prevalence of AI services on cloud platforms is driving the demand for technologies and tools which enable the integration of multiple AI services to handle intricate tasks. Traditional methods of evaluating intelligent systems focus mainly on the performance of AI components, without providing comprehensive metrics for the system as a whole. Additionally, as these AI components are often sourced from third-party providers, users may face challenges due to inconsistent quality assurance and limitations in further developing AI models, and dealing with third-party service providers’ limitations. These limitations often involve quality assurance and a lack of capability for secondary development and training of services. To address these issues, we have developed a tool based on our previous work. It can autonomously build Intelligent systems from AI services while tackling the issues mentioned above. This tool not only creates service composition solutions that align with user-defined functional requirements and performance metrics but also executes these solutions to verify if the metrics meet user requirements. We have demonstrated the effectiveness of this tool in constructing trustworthy intelligent systems through a series of case studies.
{"title":"AIDCT: An AI service development and composition tool for constructing trustworthy intelligent systems","authors":"","doi":"10.1016/j.hcc.2024.100227","DOIUrl":"10.1016/j.hcc.2024.100227","url":null,"abstract":"<div><div>The growing prevalence of AI services on cloud platforms is driving the demand for technologies and tools which enable the integration of multiple AI services to handle intricate tasks. Traditional methods of evaluating intelligent systems focus mainly on the performance of AI components, without providing comprehensive metrics for the system as a whole. Additionally, as these AI components are often sourced from third-party providers, users may face challenges due to inconsistent quality assurance and limitations in further developing AI models, and dealing with third-party service providers’ limitations. These limitations often involve quality assurance and a lack of capability for secondary development and training of services. To address these issues, we have developed a tool based on our previous work. It can autonomously build Intelligent systems from AI services while tackling the issues mentioned above. This tool not only creates service composition solutions that align with user-defined functional requirements and performance metrics but also executes these solutions to verify if the metrics meet user requirements. We have demonstrated the effectiveness of this tool in constructing trustworthy intelligent systems through a series of case studies.</div></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 4","pages":"Article 100227"},"PeriodicalIF":3.2,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141028150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence development. To solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance (PBFT) in IoT scenarios, a hierarchical consensus protocol called DCBFT is proposed. Above all, we propose an improved k-sums algorithm to build a two-level consensus cluster, achieving an hierarchical management for IoT devices. Next, A scalable two-level consensus protocol is proposed, which uses a multi-primary node mechanism to solve the single-point-of-failure problem. In addition, a data synchronization process is introduced to ensure the consistency of block data after view changes. Finally, A dynamic reputation evaluation model is introduced to update the nodes’ reputation values and complete the rotation of consensus nodes at the end of each consensus round. The experimental results show that DCBFT has a more robust dynamic and higher consensus efficiency. Moreover, After running for some time, the performance of DCBFT shows some improvement.
{"title":"A hierarchical byzantine fault tolerance consensus protocol for the Internet of Things","authors":"Rongxin Guo , Zhenping Guo , Zerui Lin , Wenxian Jiang","doi":"10.1016/j.hcc.2023.100196","DOIUrl":"10.1016/j.hcc.2023.100196","url":null,"abstract":"<div><p>The inefficiency of Consensus protocols is a significant impediment to blockchain and IoT convergence development. To solve the problems like inefficiency and poor dynamics of the Practical Byzantine Fault Tolerance (PBFT) in IoT scenarios, a hierarchical consensus protocol called DCBFT is proposed. Above all, we propose an improved k-sums algorithm to build a two-level consensus cluster, achieving an hierarchical management for IoT devices. Next, A scalable two-level consensus protocol is proposed, which uses a multi-primary node mechanism to solve the single-point-of-failure problem. In addition, a data synchronization process is introduced to ensure the consistency of block data after view changes. Finally, A dynamic reputation evaluation model is introduced to update the nodes’ reputation values and complete the rotation of consensus nodes at the end of each consensus round. The experimental results show that DCBFT has a more robust dynamic and higher consensus efficiency. Moreover, After running for some time, the performance of DCBFT shows some improvement.</p></div>","PeriodicalId":100605,"journal":{"name":"High-Confidence Computing","volume":"4 3","pages":"Article 100196"},"PeriodicalIF":3.2,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2667295223000946/pdfft?md5=319d5156a64f46754b47a19c3c570818&pid=1-s2.0-S2667295223000946-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138992888","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01Epub Date: 2024-05-20DOI: 10.1016/j.hcc.2024.100242
Pervasive Computing has become more personal with the widespread adoption of the Internet of Things (IoT) in our day-to-day lives. The emerging domain that encompasses devices, sensors, storage, and computing of personal use and surroundings leads to Personal IoT (PIoT). PIoT offers users high levels of personalization, automation, and convenience. This proliferation of PIoT technology has extended into society, social engagement, and the interconnectivity of PIoT objects, resulting in the emergence of the Social Internet of Things (SIoT). The combination of PIoT and SIoT has spurred the need for autonomous learning, comprehension, and understanding of both the physical and social worlds. Current research on PIoT is dedicated to enabling seamless communication among devices, striking a balance between observation, sensing, and perceiving the extended physical and social environment, and facilitating information exchange. Furthermore, the virtualization of independent learning from the social environment has given rise to Artificial Social Intelligence (ASI) in PIoT systems. However, autonomous data communication between different nodes within a social setup presents various resource management challenges that require careful consideration. This paper provides a comprehensive review of the evolving domains of PIoT, SIoT, and ASI. Moreover, the paper offers insightful modeling and a case study exploring the role of PIoT in post-COVID scenarios. This study contributes to a deeper understanding of the intricacies of PIoT and its various dimensions, paving the way for further advancements in this transformative field.
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Pub Date : 2024-09-01Epub Date: 2024-01-11DOI: 10.1016/j.hcc.2024.100201
Xiaobing Dai , Jiajing Liu , Xin Liu , Xiaofen Tu , Ruexue Wang
In recent years, with the development of blockchain, electronic bidding auction has received more and more attention. Aiming at the possible problems of privacy leakage in the current electronic bidding and auction, this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries, which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party. The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography. It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants, which can solve the problem of resisting malicious adversary attacks. The security of the protocol is proved by the real/ideal model paradigm, and the efficiency of the protocol is analyzed. The efficiency of the protocol is verified by simulating experiments, and the protocol has practical value.
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