Pub Date : 2023-09-01DOI: 10.1109/miot.2023.10255775
{"title":"Comsoc Publications","authors":"","doi":"10.1109/miot.2023.10255775","DOIUrl":"https://doi.org/10.1109/miot.2023.10255775","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"2 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":"135388378","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}
Pub Date : 2023-09-01DOI: 10.1109/iotm.001.2300088
Hosein Nikopour, Wei Mao
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.
{"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}
Pub Date : 2023-09-01DOI: 10.1109/miot.2023.10255786
{"title":"IEEE Foundation","authors":"","doi":"10.1109/miot.2023.10255786","DOIUrl":"https://doi.org/10.1109/miot.2023.10255786","url":null,"abstract":"","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":"135388587","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}
Pub Date : 2023-09-01DOI: 10.1109/iotm.001.2300049
Yuquan Xiao, Qinghe Du, Wenchi Cheng, George K. Karagiannidis, Zixiao Zhao
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.
{"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}
Pub Date : 2023-09-01DOI: 10.1109/IOTM.001.2200232
Balint Bicski, Károly Farkas, Adrian Pekar
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.
{"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}
Pub Date : 2023-09-01DOI: 10.1109/miot.2023.10255779
{"title":"Comsoc Tech Committees","authors":"","doi":"10.1109/miot.2023.10255779","DOIUrl":"https://doi.org/10.1109/miot.2023.10255779","url":null,"abstract":"","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"22 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":"135388379","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}
Pub Date : 2023-09-01DOI: 10.1109/iotm.001.2200264
Fan Zhang, Guangjie Han, Aohan Li, Chuan Lin, Li Liu, Yu Zhang, Yan Peng
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.
{"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}
Pub Date : 2023-09-01DOI: 10.1109/iotm.001.2200164
Christian Miranda Moreira, Georges Kaddoum
Internet Protocol v6 (IPv6) for low-power wireless personal area networks has been developed to facilitate and support IP stack communication over IPv6 networks. In RFC 6550, the Internet Engineering Task Force specifies the IPv6 Routing Protocol for low-power and lossy networks to promote efficient routing in 6LoWPAN. However, this technology is not mature enough to offer secure mechanisms and communications. In this context, Software-Defined Networking has been developed to provide programmability to the resource-constrained 6LoWPAN architecture creating a new paradigm called SD6LoWPAN. Moreover, researchers have proposed machine learning to provide fast reconfigurability and intelligence for SD6LoWPAN. This article aims to provide an overview pertaining to security issues in SD6LoWPAN, considering its resource, topology, and traffic. In addition, a study is presented on the SDN- and ML-based security solutions proposed in the literature. Security research challenges and trends are also put forward.
{"title":"SD6LoWPAN Security: Issues, Solutions, Research Challenges, and Trends","authors":"Christian Miranda Moreira, Georges Kaddoum","doi":"10.1109/iotm.001.2200164","DOIUrl":"https://doi.org/10.1109/iotm.001.2200164","url":null,"abstract":"Internet Protocol v6 (IPv6) for low-power wireless personal area networks has been developed to facilitate and support IP stack communication over IPv6 networks. In RFC 6550, the Internet Engineering Task Force specifies the IPv6 Routing Protocol for low-power and lossy networks to promote efficient routing in 6LoWPAN. However, this technology is not mature enough to offer secure mechanisms and communications. In this context, Software-Defined Networking has been developed to provide programmability to the resource-constrained 6LoWPAN architecture creating a new paradigm called SD6LoWPAN. Moreover, researchers have proposed machine learning to provide fast reconfigurability and intelligence for SD6LoWPAN. This article aims to provide an overview pertaining to security issues in SD6LoWPAN, considering its resource, topology, and traffic. In addition, a study is presented on the SDN- and ML-based security solutions proposed in the literature. Security research challenges and trends are also put forward.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"198 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":"135388600","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}
Pub Date : 2023-09-01DOI: 10.1109/miot.2023.10255768
N. Kishor Narang
Internet of Vehicles (IoV) - a network of vehicles equipped with sensors, software, and the technologies that mediate between them with the aim of connecting & exchanging data over the Internet; is far more complex than it appears to be, or the evangelist and proponents of this ecosystem would like you to believe. Bringing this concept and vision to reality needs a comprehensive System Thinking, Systems Engineering, and Systems of Systems architecture approach. This is because, to realize this vision, we need to ensure that stakeholder of the diverse heterogenous ecosystems understand the confluence and interplay of these systems and their imperatives to work in homogenous manner. And, to add Ubiquitous Intelligence in the IoV is another tall ask, making this approach an imperative.
{"title":"Mentor's musings on systems thinking and systems of systems architecture approach imperative for achieving ubiquitous intelligence in internet of vehicles [iot standards]","authors":"N. Kishor Narang","doi":"10.1109/miot.2023.10255768","DOIUrl":"https://doi.org/10.1109/miot.2023.10255768","url":null,"abstract":"Internet of Vehicles (IoV) - a network of vehicles equipped with sensors, software, and the technologies that mediate between them with the aim of connecting & exchanging data over the Internet; is far more complex than it appears to be, or the evangelist and proponents of this ecosystem would like you to believe. Bringing this concept and vision to reality needs a comprehensive System Thinking, Systems Engineering, and Systems of Systems architecture approach. This is because, to realize this vision, we need to ensure that stakeholder of the diverse heterogenous ecosystems understand the confluence and interplay of these systems and their imperatives to work in homogenous manner. And, to add Ubiquitous Intelligence in the IoV is another tall ask, making this approach an imperative.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"27 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":"135388604","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}
Pub Date : 2023-09-01DOI: 10.1109/IOTM.001.2200218
A. Elzanaty, Anna Guerra, Francesco Guidi, D. Dardari, Mohamed-Slim Alouini
In the last years, we have experienced the evolution of wireless localization from being a simple add-on feature for enabling specific applications to becoming an essential characteristic of wireless cellular networks, as for sixth generation (6G) cellular networks. This article illustrates the importance of radio localization and its role in almost all cellular generations. Also, it speculates on the idea of holographic localization where the characteristics of electromagnetic (EM) waves, including the spherical wavefront in the near-field, are fully controlled and exploited to achieve better wireless localization. Along this line, we briefly overview possible technologies, such as large intelligent surfaces, and challenges to realize holographic localization. To corroborate our vision, we also include a numerical example that confirms the potentialities of holographic localization.
{"title":"Toward 6G Holographic Localization: Enabling Technologies and Perspectives","authors":"A. Elzanaty, Anna Guerra, Francesco Guidi, D. Dardari, Mohamed-Slim Alouini","doi":"10.1109/IOTM.001.2200218","DOIUrl":"https://doi.org/10.1109/IOTM.001.2200218","url":null,"abstract":"In the last years, we have experienced the evolution of wireless localization from being a simple add-on feature for enabling specific applications to becoming an essential characteristic of wireless cellular networks, as for sixth generation (6G) cellular networks. This article illustrates the importance of radio localization and its role in almost all cellular generations. Also, it speculates on the idea of holographic localization where the characteristics of electromagnetic (EM) waves, including the spherical wavefront in the near-field, are fully controlled and exploited to achieve better wireless localization. Along this line, we briefly overview possible technologies, such as large intelligent surfaces, and challenges to realize holographic localization. To corroborate our vision, we also include a numerical example that confirms the potentialities of holographic localization.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"17 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":"132381685","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}
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