IEEE Internet of Things Magazine最新文献_第8页

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IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/miot.2023.10255775

引用次数: 0

Linear Packet Network Coding to Enhance Reliability and Resiliency of Next Generation Wireless Networks with Topological Redundancies 线性分组网络编码提高拓扑冗余下一代无线网络的可靠性和弹性

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/iotm.001.2300088

Hosein Nikopour, Wei Mao

{"title":"Linear Packet Network Coding to Enhance Reliability and Resiliency of Next Generation Wireless Networks with Topological Redundancies","authors":"Hosein Nikopour, Wei Mao","doi":"10.1109/iotm.001.2300088","DOIUrl":"https://doi.org/10.1109/iotm.001.2300088","url":null,"abstract":"The article discusses the use of linear packet network coding (LPNC) to improve reliability and latency in next generation wireless communication networks, particularly for emerging mission critical internet of things (IoT) applications and services. LPNC is a technique for adding redundancy to packets at the upper layers of the radio access network (RAN) protocol stack by partitioning each data packet into equal-sized segments and applying packet-level linear coding, which produces linear combinations of the segments as the output packets of the LPNC layer. It leverages the topological redundancy to send coded segments via multiple diverse routes to a given destination, effectively treating multiple routes as a single data pipe. The receiver can recover the original upper-layer packet if it receives a sufficient number of encoded lower-layer packets. The computation complexity of both encoding and decoding mainly consists of forming the linear combinations of packets which is linear in the packet size. It is shown that with efficient design the contribution of the encoding and decoding process of LPNC to the RAN transmission delay is in microsecond level. The article explores the challenges of implementing LPNC in various wireless network scenarios, such as multi-path and multi-hop integrated access and backhaul (IAB) networks. The proposed solutions include optimizing packet allocation over multiple paths and using adaptive coded-forwarding schemes over multiple hops. The resiliency of LPNC against link blockage in high frequency bands is also evaluated. The article also discusses LPNC for Wi-Fi networks and its benefits in terms of low latency and high reliability in aggregated medium access control protocol data unit (A-MPDU) scenario.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135388382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IEEE Foundation IEEE基金会

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/miot.2023.10255786

引用次数: 0

Model-ML Integrated Intelligence in URLLC Towards End-to-End Delay Fulfillment Over Vehicular Networks 面向车辆网络端到端延迟实现的URLLC模型- ml集成智能

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/iotm.001.2300049

Yuquan Xiao, Qinghe Du, Wenchi Cheng, George K. Karagiannidis, Zixiao Zhao

{"title":"Model-ML Integrated Intelligence in URLLC Towards End-to-End Delay Fulfillment Over Vehicular Networks","authors":"Yuquan Xiao, Qinghe Du, Wenchi Cheng, George K. Karagiannidis, Zixiao Zhao","doi":"10.1109/iotm.001.2300049","DOIUrl":"https://doi.org/10.1109/iotm.001.2300049","url":null,"abstract":"Ultra-Reliable and Low-Latency Communication (URLLC) had been initially proposed as one of the three main application scenarios in the fifth generation of mobile telecommunications systems (5G). While URLLC is expected to support vehicular networking with low latency, current 5G infrastructures still cannot well assure about one-millisecond-level delay for various time-sensitive applications in vehicular networks. To better serve vehicular networks as well as other vertical application scenarios with stringent latency requirement, URLLC remains the hotspot for beyond-5G and 6G and is expected to dig deeper in optimization paradigm together with random-access control technologies. Model-based design principles integrating machine learning (ML) intelligence have been recognized as a competitive candidate to empower URLLC quickly into reality. In this article, we first anatomize the constitution of the end-to-end delay for URLLC towards vehicular networks and concentrate on the ways of how to apply model-ML integrated intelligence to reduce major delay components, including access delay, queuing delay, and transmission delay. Facing the challenging task, we derive an intelligent multi-tier-driven computing framework for access-delay reduction. We then introduce an efficient resource allocation approach driven by multi-deep-reinforcement-learning networks to jointly lower the queuing delay and transmission delay. Finally, we share the discussions about the open issues on latency control for future URLLC.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135388598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Simple Heuristics as a Viable Alternative to Machine Learning-Based Anomaly Detection in Industrial IoT 简单启发式作为工业物联网中基于机器学习的异常检测的可行替代方案

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/IOTM.001.2200232

Balint Bicski, Károly Farkas, Adrian Pekar

{"title":"Simple Heuristics as a Viable Alternative to Machine Learning-Based Anomaly Detection in Industrial IoT","authors":"Balint Bicski, Károly Farkas, Adrian Pekar","doi":"10.1109/IOTM.001.2200232","DOIUrl":"https://doi.org/10.1109/IOTM.001.2200232","url":null,"abstract":"This article evaluates the efficacy of simple heuristic approaches compared to sophisticated machine learning by quantifying the accuracy and timeliness of selected multivariate anomaly detectors on industrial time series. It specifically examines the efficacy of two probabilistic detectors, a statistical detector and a deep learning anomaly detector. The presented work stems from the observation that the application of machine learning methods may be unfounded in a variety of use cases. The findings made in this study imply that there is no reason to over-engineer a solution by applying sophisticated methods without genuine grounds. The conventional autoregressive heuristic model outperforms the autoencoder by up to 7.2 percent. Furthermore, the autoencoder also underperforms in terms of execution time. Compared to the simpler approaches, its computational time complexity is up to 47 percent higher. Simple methods thus emerge as viable alternatives to sophisticated multivariate time-series anomaly detection on the evaluated application domain. Our conclusions remained valid through examining datasets originating from other domains. We infer that the performance of more elaborated methods requires verification to justify their usage.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126074984","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Comsoc Tech Committees 社会委员会技术委员会

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/miot.2023.10255779

引用次数: 0

QoS-Driven Distributed Cooperative Data Offloading and Heterogeneous Resource Scheduling for IIoT 基于qos驱动的工业物联网分布式协同数据卸载与异构资源调度

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/iotm.001.2200264

Fan Zhang, Guangjie Han, Aohan Li, Chuan Lin, Li Liu, Yu Zhang, Yan Peng

{"title":"QoS-Driven Distributed Cooperative Data Offloading and Heterogeneous Resource Scheduling for IIoT","authors":"Fan Zhang, Guangjie Han, Aohan Li, Chuan Lin, Li Liu, Yu Zhang, Yan Peng","doi":"10.1109/iotm.001.2200264","DOIUrl":"https://doi.org/10.1109/iotm.001.2200264","url":null,"abstract":"Edge computing has become a powerful paradigm to fulfill the diversified quality of service (QoS) demands of the Industrial Internet of Things (IIoT) applications. This study examines the cooperative data offloading (DO) and heterogeneous resource scheduling (RS) problem for maximizing long-term system utility. Owing to the dynamics, high connectivity density, and diverse QoS demands of IIoT, a QoS-driven distributed decision-making (QDDM) framework is proposed to address this problem. Specifically, this framework decomposes the primal problem into two subproblems: industrial terminal device (ITD)-side DO and edge server (EDS)-side RS. Then, a modified soft actor-critic (SAC)-based multi-agent deep reinforcement learning (MSMD) algorithm is proposed to address the ITD-side DO subproblem, which can achieve more accurate estimation of the Q-values and solve both the centralized-decentralized mismatch and the multi-agent credit assignment issues. Based on the DO decisions of each ITD, a linear approximation method is proposed to transform the EDS-side RS subproblem into an easily-solved linear programming subproblem. Finally, a real-world IIoT experiment platform is built to evaluate the performance of the QDDM framework. The evaluation results demonstrate that the QDDM framework effectively increases the long-term system utility.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135388399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SD6LoWPAN Security: Issues, Solutions, Research Challenges, and Trends SD6LoWPAN安全:问题、解决方案、研究挑战和趋势

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/iotm.001.2200164

Christian Miranda Moreira, Georges Kaddoum

引用次数: 0

Mentor's musings on systems thinking and systems of systems architecture approach imperative for achieving ubiquitous intelligence in internet of vehicles [iot standards] Mentor对系统思维和系统架构方法的思考是实现车联网无处不在的智能的必要条件[物联网标准]

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/miot.2023.10255768

N. Kishor Narang

引用次数: 0

Toward 6G Holographic Localization: Enabling Technologies and Perspectives 迈向6G全息定位:使能技术和观点

IEEE Internet of Things Magazine Pub Date : 2023-09-01 DOI: 10.1109/IOTM.001.2200218

A. Elzanaty, Anna Guerra, Francesco Guidi, D. Dardari, Mohamed-Slim Alouini

引用次数: 13