2022 IEEE 8th World Forum on Internet of Things (WF-IoT)最新文献_第3页

Uplink Resource Allocation for Video Transmission in Wireless LAN System 无线局域网系统中视频传输的上行链路资源分配

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152172

Ryota Yamada, H. Tomeba, Takuhiro Sato, O. Nakamura, Y. Hamaguchi

{"title":"Uplink Resource Allocation for Video Transmission in Wireless LAN System","authors":"Ryota Yamada, H. Tomeba, Takuhiro Sato, O. Nakamura, Y. Hamaguchi","doi":"10.1109/WF-IoT54382.2022.10152172","DOIUrl":"https://doi.org/10.1109/WF-IoT54382.2022.10152172","url":null,"abstract":"Various applications have been realized by wireless communications. Especially, applications using ultra-high-definition video transmission are attracting attention. There is an increasing demand for ultra-high-definition video transmission over uplink in these applications. Such large-capacity uplink traffic plays an important role not only in mobile phone networks but also in private networks such as wireless local area network (LAN). In wireless LAN systems, uplink transmission access schemes are broadly divided into (a) uplink transmission based on carrier sense multiple access with collision avoidance (UL CSMA/CA) and (b) trigger-based access. From the viewpoint of video transmission, trigger-based access, which can control the transmission timing, is more suitable than UL CSMA/CA, which cannot control the transmission timing. However, even if trigger-based access is performed, highly demanding applications such as ultra-high-definition video transmission cause a problem that increasing the capacity of wireless communication systems does not directly bring to the realization of applications. Therefore, we consider that it is necessary to evaluate video throughput, which is based on the criteria whether requirements for ultra-high-definition video transmission have been satisfied, instead of the bit throughput, which is calculated from bits received correctly. In this paper, we consider the video throughput and propose uplink radio resources allocation using application proportional fairness to utilize radio resources efficiently for video transmission. Computer simulation shows that our proposal can significantly improve the video throughput compared with a typical radio resource allocation scheme, proportional fairness.","PeriodicalId":176605,"journal":{"name":"2022 IEEE 8th World Forum on Internet of Things (WF-IoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129371951","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: 1

Exploiting Physical Layer Vulnerabilities in LoRaWAN-based IoT Networks 利用基于lorawan的物联网网络中的物理层漏洞

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152098

Nuno Torres, Pedro Pinto, S. I. Lopes

{"title":"Exploiting Physical Layer Vulnerabilities in LoRaWAN-based IoT Networks","authors":"Nuno Torres, Pedro Pinto, S. I. Lopes","doi":"10.1109/WF-IoT54382.2022.10152098","DOIUrl":"https://doi.org/10.1109/WF-IoT54382.2022.10152098","url":null,"abstract":"Low Power Wide Area Networks (LPWAN) are used worldwide in several Internet of Things (IoT) applications that rely on large-scale deployments. Despite most of these networks include their own security mechanisms with built-in encryption, they are still vulnerable to a range of attacks that can be performed using low-cost Software Defined Radio (SDR) hardware and low-complexity techniques. This work provides an experimental setup implemented to exploit physical layer vul-nerabilities with SDR techniques. Several attack vectors typically related to LPWAN within the IoT ecosystem are implemented and tested such as Global Positioning (GPS) Spoofing, Replay Attacks, Denial-of-Service (DoS) and Jamming, in environments that rely specifically on LoRaWAN networks. The results show that, in LoRAWAN networks, a set of vulnerabilities can be exploited leading to the incorrect functioning of the executed applications, and possible damage to the systems in which they operate. It was possible to verify that, depending on the type of activation method used between the devices and the LoRaWAN server, the communications and the devices can be compromised.","PeriodicalId":176605,"journal":{"name":"2022 IEEE 8th World Forum on Internet of Things (WF-IoT)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130535286","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

Re-identification Attack based on Few-Hints Dataset Enrichment for Ubiquitous Applications 泛在应用中基于少提示数据集充实的重识别攻击

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152275

Andrea Artioli, L. Bedogni, M. Leoncini

{"title":"Re-identification Attack based on Few-Hints Dataset Enrichment for Ubiquitous Applications","authors":"Andrea Artioli, L. Bedogni, M. Leoncini","doi":"10.1109/WF-IoT54382.2022.10152275","DOIUrl":"https://doi.org/10.1109/WF-IoT54382.2022.10152275","url":null,"abstract":"Ubiquitous and pervasive applications record a large amount of data about users, to provide context-aware and tailored services. Although this enables more personalized applications, it also poses several questions concerning the possible misuse of such data by a malicious entity, which may discover private and sensitive information about the users themselves. In this paper we propose an attack on ubiquitous applications pseudo-anonymized datasets which can be leaked or accessed by the attacker. We enrich the data with true information which the attacker can obtain from a multitude of sources, which will eventually spark a chain reaction on the records of the dataset, possibly re-identifying users. Our results indicate that through this attack, and with few hints added to the dataset, the possibility of re-identification are considerable, achieving more than 70% re-identified users on a public available dataset. We compare our proposal with the state of the art, showing the improved performance figures obtained thanks to the graph-modeling of the dataset records and the novel hint structure.","PeriodicalId":176605,"journal":{"name":"2022 IEEE 8th World Forum on Internet of Things (WF-IoT)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123310572","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

SLA- Gα: The Next Generation of SLAs for IoT SLA- Gα:物联网的下一代SLA

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152099

Kashif Rabbani, A. Moore, J. Rafferty

引用次数: 0

A Robust Deep Learning Architecture for FireFighter PPEs Detection 一种用于消防员ppe检测的鲁棒深度学习架构

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152263

Achilleas Sesis, Ilias Siniosoglou, Yannis Spyridis, G. Efstathopoulos, T. Lagkas, V. Argyriou, P. Sarigiannidis

{"title":"A Robust Deep Learning Architecture for FireFighter PPEs Detection","authors":"Achilleas Sesis, Ilias Siniosoglou, Yannis Spyridis, G. Efstathopoulos, T. Lagkas, V. Argyriou, P. Sarigiannidis","doi":"10.1109/WF-IoT54382.2022.10152263","DOIUrl":"https://doi.org/10.1109/WF-IoT54382.2022.10152263","url":null,"abstract":"Personal Protective Equipment (PPE) is one of the primary defence mechanisms to reduce the exposure of the personnel to hazardous environments. It's significantly important to Fire Fighters as they are constantly exposed to dangerous elements such as fire, gas or chemicals. Unfortunately, in real-time emergencies, such as fires, it is very difficult to identify if a responder using PPE is fully equipped to reduce any accidents in the workplace or even coordinate response actions due to the high pace of the situation. A lack of a unified Fire Fighting PPE image dataset was also observed, which makes the task of training Machine Learning (ML) models to solve this problem a challenge. To that end, we first create a general purpose FireFighter Equipment Detection dataset. We then propose to utilise the widely used YoloV5 Deep Network architecture to detect different PPE components in real-time. This work leverages the pretrained YoloV5 model, using transfer learning to fine-tune the model using the created detection dataset that contains targeted Fire Fighter PPE images. By employing the pre-trained model which requires substantially fewer training samples, we were able to achieve a considerably good performance on the Fire Fighter PPE object detection. The proposed method can distinguish four different PPE components such as a Helmet, Gloves, Mask or Insulated protective cloth, achieving high detection efficiency which is experimentally established.","PeriodicalId":176605,"journal":{"name":"2022 IEEE 8th World Forum on Internet of Things (WF-IoT)","volume":"116 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116619986","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

Topological Twin for Mobility Support Robots 移动支持机器人的拓扑孪生

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152223

Fernando Ardilla, Azhar Aulia Saputra, A. Besari, Naoki Doteguchi, Kohei Oshio, T. Obo, N. Kubota

引用次数: 0

Pedometers for Smartphones: Analysis and Comparison of Real-Time Algorithms 智能手机计步器:实时算法的分析和比较

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152104

Giacomo Neri, Federico Montori, Lorenzo Gigli, L. Bedogni, M. D. Felice, L. Bononi

引用次数: 0

A Trustworthy Blockchain-Enabled System for Indoor Contact Tracing in Epidemic Control 可信赖的区块链疫情防控室内接触者追踪系统

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152071

Mohammad Salimibeni, Zohreh Hajiakhondi-Meybodi, Arash Mohammadi

引用次数: 0

Tutorials 教程

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152175

R. Marin-Lopez, Bala Krishna Maddali

引用次数: 0

Feature-Sniffer: Enabling IoT Forensics in OpenWrt based Wi-Fi Access Points 功能嗅探器:在基于OpenWrt的Wi-Fi接入点中启用物联网取证

2022 IEEE 8th World Forum on Internet of Things (WF-IoT) Pub Date : 2022-10-26 DOI: 10.1109/WF-IoT54382.2022.10152146

Fabio Palmese, A. Redondi, M. Cesana

{"title":"Feature-Sniffer: Enabling IoT Forensics in OpenWrt based Wi-Fi Access Points","authors":"Fabio Palmese, A. Redondi, M. Cesana","doi":"10.1109/WF-IoT54382.2022.10152146","DOIUrl":"https://doi.org/10.1109/WF-IoT54382.2022.10152146","url":null,"abstract":"The Internet of Things is in constant growth, with millions of devices used every day in our homes and workplaces to ease our lives. Such a strict coexistence between humans and smart devices makes the latter digital witnesses of our everyday lives through their sensor systems. This opens up to a new area of digital investigation named IoT Forensics, where digital traces produced by smart devices (network traffic, in primis) are leveraged as evidences for forensic purposes. It is therefore important to create tools able to capture, store and possibly analyse easily such digital traces to ease the job of forensic investigators. This work presents one of such tools, named Feature-Sniffer, which is thought explicitly for Wi-Fi enabled smart devices used in Smart Building/Smart Home scenarios. Feature-Sniffer is an add-on for OpenWrt-based access points and allows to easily perform online traffic feature extraction, avoiding to store large PCAP files. We present Feature-Sniffer with an accurate description of the implementation details, and we show its possible uses with practical examples for device identification and activity classification from encrypted traffic produced by IoT cameras. We release Feature-Sniffer publicly for reproducible research.","PeriodicalId":176605,"journal":{"name":"2022 IEEE 8th World Forum on Internet of Things (WF-IoT)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116074852","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: 3