ACM Transactions on Internet Technology最新文献

Navigating the Metaverse: A Comprehensive Analysis of Consumer Electronics Prospects and Challenges 领航 Metaverse：全面分析消费电子产品的前景与挑战

IF 3.9 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-07-24 DOI: 10.1145/3680545

Siva Sai, Akshat Garg, Vinay Chamola

{"title":"Navigating the Metaverse: A Comprehensive Analysis of Consumer Electronics Prospects and Challenges","authors":"Siva Sai, Akshat Garg, Vinay Chamola","doi":"10.1145/3680545","DOIUrl":"https://doi.org/10.1145/3680545","url":null,"abstract":"Rapid innovation in consumer electronics has made our lives more comfortable. Consumer electronics serve as the primary platform for the Metaverse (MV), offering users an immersive and interactive medium that connects the digital and real worlds. Consumer electronics play a crucial role in ensuring the accessibility and the user experience within the Metaverse. Despite the vital role of consumer electronics in enabling an immersive metaverse experience, a detailed survey has yet to cover different facets. Addressing this research gap, we present a comprehensive review covering several applications, case studies, and challenges of consumer electronics in Metaverse. We present the role and scope of different consumer electronics devices like VR(Virtual Reality) headsets, AR(Augmented Reality) headsets, haptic feedback devices, and smartphones in Metaverse applications. We present an illustrative case study on how consumer electronics assist education in the Metaverse. Several device-specific challenges include restricted field of view, latency issues, synchronization issues, and non-device-specific challenges like interoperability, scalability, and data privacy. Our survey shall help researchers explore more prospects for making this integration stronger.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141810075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach 迈向可持续区块链：基于点对点联盟学习的方法

IF 3.9 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-07-24 DOI: 10.1145/3680544

Vidushi Agarwal, Shruti Mishra, Sujata Pal

{"title":"Towards a Sustainable Blockchain: A Peer-to-Peer Federated Learning based Approach","authors":"Vidushi Agarwal, Shruti Mishra, Sujata Pal","doi":"10.1145/3680544","DOIUrl":"https://doi.org/10.1145/3680544","url":null,"abstract":"In the rapidly evolving digital world, blockchain technology is becoming the foundation for numerous applications, ranging from financial services to supply chain management. As the usage of blockchain is becoming more prevalent, the energy-intensive nature of this technology has raised concerns about its long-term sustainability and environmental footprint. To address this challenge, we explore the potential of Peer-to-Peer Federated Learning (P2P-FL), a distributed machine learning approach that allows multiple nodes to collaborate without sharing raw data. We present a novel integration of P2P-FL with blockchain technology, aimed at enhancing the sustainability and efficiency of blockchain networks. The basic idea of our approach is the use of distributed learning mechanisms to find the optimal performance parameters of blockchain without relying on centralized control. These parameters are then used by a load-balancing mechanism that prioritizes energy efficiency to distribute loads on different blockchains. Furthermore, we formulate a non-cooperative game theory model to align the individual node strategies with the collective objective of energy optimization, ensuring a balance between self-interest and overall network performance. Our work is exemplified through a case study in the renewable energy sector, demonstrating the application of our model in creating an efficient marketplace for energy trading. The experimentation and results indicate a significant improvement in the execution times and energy consumption of blockchain networks. Therefore, the overall sustainability of the network is enhanced, making our framework practical and applicable in real-world scenarios.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806702","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Point Cloud Registration Method for Multimedia Communication in Automated Driving Metaverse 用于自动驾驶元宇宙多媒体通信的新型点云注册方法

IF 3.9 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-07-12 DOI: 10.1145/3672561

Binbin Yong, Ming Lei, Jun Shen, Peng Zhi, Rui Zhao, Qingguo Zhou

{"title":"A Novel Point Cloud Registration Method for Multimedia Communication in Automated Driving Metaverse","authors":"Binbin Yong, Ming Lei, Jun Shen, Peng Zhi, Rui Zhao, Qingguo Zhou","doi":"10.1145/3672561","DOIUrl":"https://doi.org/10.1145/3672561","url":null,"abstract":"The development of the Metaverse offers more possibilities for autonomous driving. This is mainly reflected in the fact that the scene reconstructed based on multiple sensors can help the autonomous vehicle establish a Metaverse world based on its own real situation. When multiple vehicles build such a Metaverse world in the same scene, they can exchange their information including the perception of the vehicle’s condition and the surrounding environment, which means the creation of a Metaverse world containing all vehicles. Thus, an indirect Human-Human Multimedia Communications based on the Metaverse is realized, in which the autonomous driving system acts as a multimedia to provide a medium for the exchange of information between vehicles. The establishment of the Metaverse is based on stable and high-quality scene reconstruction. Accurate scene information can bring high quality Human-Human Multimedia Communications. Achieving this requires accurate scene reconstruction and vehicle positioning, both of which depend on accurate point cloud registration. In this work, we propose a robust registration method that is based on semantic information and scaling constraints. Our method consists of three steps. Firstly, we filter out points that might mislead the point cloud registration process by leveraging semantic information. Secondly, we obtain a more accurate initial matrix using TEASER++, which is based on semantic information and feature descriptors. Finally, we use semantic information and scaling to constrain the nearest neighbor matching and filter out error matches to obtain a higher quality registration. By following these steps, our method overcomes the memory problem faced by TEASER++ when there are large-scale point clouds, and greatly reduces its running time. Meanwhile, our algorithm achieved superior point cloud registration results compared to two state-of-the-art robust registration techniques: Globally optimal Iterative Closest Point (Go-ICP) and Generalized Iterative Closest Point (GICP).","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141654291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interpersonal Communication Interconnection in Media Convergence Metaverse 媒体融合 Metaverse 中的人际交流互联

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-06-05 DOI: 10.1145/3670998

Xin Wang, Jianhui Lv, Achyut Shankar, Carsten Maple, Keqin Li, Qing Li

{"title":"Interpersonal Communication Interconnection in Media Convergence Metaverse","authors":"Xin Wang, Jianhui Lv, Achyut Shankar, Carsten Maple, Keqin Li, Qing Li","doi":"10.1145/3670998","DOIUrl":"https://doi.org/10.1145/3670998","url":null,"abstract":"The metaverse aims to provide immersive virtual worlds connecting with the physical world. To enable real-time interpersonal communications between users across the globe, the metaverse places high demands on network performance, including low latency, high bandwidth, and fast network speeds. This paper proposes a novel Media Convergence Metaverse Network (MCMN) framework to address these challenges. Specifically, the META controller serves as MCMN's logically centralized control plane, responsible for holistic orchestration across edge sites and end-to-end path computation between metaverse users. We develop a model-free deep reinforcement learning-based metaverse traffic optimization algorithm that learns to route flows while satisfying the Quality of Service (QoS) boundaries. The network slicing engine leverages artificial intelligence and machine learning to create isolated, customized virtual networks tailored for metaverse traffic dynamics on demand. It employs unsupervised and reinforcement learning techniques using network telemetry from the META controller to understand application traffic patterns and train cognitive slicer agents to make quality of service -aware decisions accordingly. Optimized delivery of diverse concurrent media types necessitates routing intelligence to meet distinct requirements while mitigating clashes over a shared infrastructure. Media-aware routing enhances traditional shortest-path approaches by combining topological metrics with workflow sensitivities. We realize an edge-assisted rendering fabric to offload complex processing from bandwidth-constrained endpoints while retaining visual realism. Extensive simulations demonstrate MCMN's superior performance compared to conventional networking paradigms. MCMN shows great promise to enable seamless interconnectivity and ultra-high fidelity communications to unlock the true potential of the metaverse.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141259842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Using Reinforcement Learning and Error Models for Drone Precision Landing 利用强化学习和误差模型实现无人机精确着陆

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-06-04 DOI: 10.1145/3670997

Sepehr Saryazdi, Balsam Alkouz, Athman Bouguettaya, Abdallah Lakhdari

引用次数: 0

Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres 实现人机交互，减轻安全运营中心的警报疲劳

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-05-30 DOI: 10.1145/3670009

Mohan Baruwal Chhetri, Shahroz Tariq, Ronal Singh, Fateneh Jalalvand, Cecile Paris, Surya Nepal

{"title":"Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres","authors":"Mohan Baruwal Chhetri, Shahroz Tariq, Ronal Singh, Fateneh Jalalvand, Cecile Paris, Surya Nepal","doi":"10.1145/3670009","DOIUrl":"https://doi.org/10.1145/3670009","url":null,"abstract":"Security Operations Centres (SOCs) play a pivotal role in defending organisations against evolving cyber threats. They function as central hubs for detecting, analysing, and responding promptly to cyber incidents with the primary objective of ensuring the confidentiality, integrity, and availability of digital assets. However, they struggle against the growing problem of alert fatigue, where the sheer volume of alerts overwhelms SOC analysts and raises the risk of overlooking critical threats. In recent times, there has been a growing call for human-AI teaming, wherein humans and AI collaborate with each other, leveraging their complementary strengths and compensating for their weaknesses. The rapid advances in AI and the growing integration of AI-enabled tools and technologies within SOCs give rise to a compelling argument for the implementation of human-AI teaming within the SOC environment. Therefore, in this position paper, we present our vision for human-AI teaming to address the problem of alert fatigue in SOC. We propose the (mathcal {A}^2mathcal {C} ) Framework, which enables flexible and dynamic decision-making by allowing seamless transitions between automated, augmented, and collaborative modes of operation. Our framework allows AI-powered automation for routine alerts, AI-driven augmentation for expedited expert decision-making, and collaborative exploration for tackling complex, novel threats. By implementing and operationalising (mathcal {A}^2mathcal {C} ), SOCs can significantly reduce alert fatigue while empowering analysts to efficiently and effectively respond to security incidents.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189603","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RESP: A Recursive Clustering Approach for Edge Server Placement in Mobile Edge Computing RESP：移动边缘计算中边缘服务器安置的递归聚类方法

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-05-27 DOI: 10.1145/3666091

Ali Akbar Vali, Sadoon Azizi, Mohammad Shojafar

{"title":"RESP: A Recursive Clustering Approach for Edge Server Placement in Mobile Edge Computing","authors":"Ali Akbar Vali, Sadoon Azizi, Mohammad Shojafar","doi":"10.1145/3666091","DOIUrl":"https://doi.org/10.1145/3666091","url":null,"abstract":"With the rapid advancement of the Internet of Things (IoT) and 5G networks in smart cities, the inevitable generation of massive amounts of data, commonly known as big data, has introduced increased latency within the traditional cloud computing paradigm. In response to this challenge, Mobile Edge Computing (MEC) has emerged as a viable solution, offloading a portion of mobile device workloads to nearby edge servers equipped with ample computational resources. Despite significant research in MEC systems, optimizing the placement of edge servers in smart cities to enhance network performance has received little attention. In this paper, we propose RESP, a novel Recursive clustering technique for Edge Server Placement in MEC environments. RESP operates based on the median of each cluster determined by the number of Base Transceiver Stations (BTSs), strategically placing edge servers to achieve workload balance and minimize network traffic between them. Our proposed clustering approach substantially improves load balancing compared to existing methods and demonstrates superior performance in handling traffic dynamics. Through experimental evaluation with real-world data from Shanghai Telecom’s base station dataset, our approach outperforms several representative techniques in terms of workload balancing and network traffic optimization. By addressing the ESP problem and introducing an advanced recursive clustering technique, this work makes a substantial contribution to optimizing mobile edge computing networks in smart cities. The proposed algorithm outperforms alternative methodologies, demonstrating a 10% average improvement in optimizing network traffic. Moreover, it achieves a 53% more suitable result in terms of computational load.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141165911","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

OTI-IoT: A Blockchain-based Operational Threat Intelligence Framework for Multi-vector DDoS Attacks OTI-IoT：基于区块链的多载体 DDoS 攻击行动威胁情报框架

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-05-11 DOI: 10.1145/3664287

Aswani Aguru, Suresh Erukala

{"title":"OTI-IoT: A Blockchain-based Operational Threat Intelligence Framework for Multi-vector DDoS Attacks","authors":"Aswani Aguru, Suresh Erukala","doi":"10.1145/3664287","DOIUrl":"https://doi.org/10.1145/3664287","url":null,"abstract":"The Internet of Things (IoT) refers to a complex network comprising interconnected devices that transmit their data via the Internet. Due to their open environment, limited computation power, and absence of built-in security, IoT environments are susceptible to various cyberattacks. Denial of service (DDoS) attacks are among the most destructive types of threats. The Multi-vector DDoS attack is a contemporary and formidable form of DDoS wherein the attacker employs a collection of compromised IoT devices as zombies to initiate numerous DDoS attacks against a target server. A Blockchain-based Operational Threat Intelligence framework, OTI-IoT, is proposed in this paper to counter multi-vector DDoS attacks in IoT networks. A ”Prevent-then-Detect” methodology was utilized to deploy the OTI-IoT framework in two distinct stages. During Phase 1, the consortium Blockchain network validators employ the IPS module, composed of a smart contract for attack prevention & access control, and Proof of Voting consensus, to thwart attacks. Validators are outfitted with deep learning-based IDS instances to detect multi-vector DDoS attacks during Phase 2. Alert messages are generated by the IDS module’s alert generation & propagation smart contract in response to identifying malicious IoT sources. The feedback loop from the IDS module to the IPS module prevents incoming traffic from malicious sources. The proposed OTI framework capabilities are realized as an outcome of combining and storing the outcomes of the IDS and IPS modules on the consortium Blockchain. Each validator maintains a shared ledger containing information regarding threat sources to ensure robust security, transparency, and integrity. The operational execution of OTI-IoT occurs on an individual Ethereum Blockchain. The empirical findings indicate that our proposed framework is most suitable for real-time applications due to its ability to lower attack detection time, decreased block validation time, and higher attack prevention rate.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140938725","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Data management for continuous learning in EHR systems 电子病历系统中用于持续学习的数据管理

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-05-07 DOI: 10.1145/3660634

Valerio Bellandi, Paolo Ceravolo, Jonatan Maggesi, Samira Maghool

{"title":"Data management for continuous learning in EHR systems","authors":"Valerio Bellandi, Paolo Ceravolo, Jonatan Maggesi, Samira Maghool","doi":"10.1145/3660634","DOIUrl":"https://doi.org/10.1145/3660634","url":null,"abstract":"To gain a comprehensive understanding of a patient’s health, advanced analytics must be applied to the data collected by electronic health record (EHR) systems. However, managing and curating this data requires carefully designed workflows. While digitalization and standardization enable continuous health monitoring, missing data values and technical issues can compromise the consistency and timeliness of the data. In this paper, we propose a workflow for developing prognostic models that leverages the SMART BEAR infrastructure and the capabilities of the Big Data Analytics (BDA) engine to homogenize and harmonize data points. Our workflow improves the quality of the data by evaluating different imputation algorithms and selecting one that maintains the distribution and correlation of features similar to the raw data. We applied this workflow to a subset of the data stored in the SMART BEAR repository and examined its impact on the prediction of emerging health states such as cardiovascular disease and mild depression. We also discussed the possibility of model validation by clinicians in the SMART BEAR project, the transmission of subsequent actions in the decision support system, and the estimation of the required number of data points.","PeriodicalId":50911,"journal":{"name":"ACM Transactions on Internet Technology","volume":null,"pages":null},"PeriodicalIF":5.3,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140884133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Efficient Vertical Federated Unlearning via Fast Retraining 通过快速再训练实现高效的垂直联合非学习

IF 5.3 3区计算机科学

ACM Transactions on Internet Technology Pub Date : 2024-04-10 DOI: 10.1145/3657290

Zichen Wang, Xiangshan Gao, Cong Wang, Peng Cheng, Jiming Chen

引用次数: 0