Pub Date : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00042
N. Suryadevara, Subham Saha
Implementing Machine Learning algorithms with low-power devices and limited computational resources is challenging. Research on temporal data of Ambient Assisted Living (AAL) environment sensors to handle many states of the deep learning models that are used to train unsupervised data is limited. Computational aspects of the data training and inferring the insights from the sensor data of the AAL environment are essential aspects of a fog computing framework. The AAL environment embodies a fog computing structure with limited computing capabilities. This paper studies how to train and infer meaning information from the AAL sensor data using a hybrid algorithm of Self Organizing Map (SOM) and Hidden Markov Model (HMM) on a resource constraint computing device such as Raspberry Pi was explored. The research investigations reveal that the execution of the hybrid method on the fog computing gateway could cluster the anomalous instances accurately.
{"title":"A hybrid SOM and HMM classifier in a Fog Computing gateway for Ambient Assisted Living Environment","authors":"N. Suryadevara, Subham Saha","doi":"10.1109/SmartIoT55134.2022.00042","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00042","url":null,"abstract":"Implementing Machine Learning algorithms with low-power devices and limited computational resources is challenging. Research on temporal data of Ambient Assisted Living (AAL) environment sensors to handle many states of the deep learning models that are used to train unsupervised data is limited. Computational aspects of the data training and inferring the insights from the sensor data of the AAL environment are essential aspects of a fog computing framework. The AAL environment embodies a fog computing structure with limited computing capabilities. This paper studies how to train and infer meaning information from the AAL sensor data using a hybrid algorithm of Self Organizing Map (SOM) and Hidden Markov Model (HMM) on a resource constraint computing device such as Raspberry Pi was explored. The research investigations reveal that the execution of the hybrid method on the fog computing gateway could cluster the anomalous instances accurately.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114400473","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00032
Peng Li, Yunfeng Zhao, Liandong Chen, Kai Cheng, Chuyue Xie, Xiaofei Wang, Qinghua Hu
Federated learning is a hot machine learning research direction, its goal is to train a high quality central model while protecting the privacy of all parties, and it has a broad application prospect in smart grid and other fields. However, in federated learning with massive client participation, it is impossible to have all clients participate in training and model aggregation every time due to the limitation of communication and computing resources. Usually the method of selecting clients for federated learning is random, some studies have studied this problem from aspects of client data quality, model training effect, communication and computing resources, etc. In this paper, we propose an active client selection algorithm from the perspective of model uncertainty, this algorithm is called uncertainty measured active client selection in FL (UCS-FL). The server actively selects a subset of clients to participate in the FL training, and the unselected clients do not need to train in this round, saving computing and communication resources. Perform a thorough empirical analysis of the image classification task to demonstrate the excellent performance of UCS-FL against baseline in the context of monitored FL settings. Finally, we describes the real-world application of the proposed architecture, especially in smart grid scenarios.
{"title":"Uncertainty Measured Active Client Selection for Federated Learning in Smart Grid","authors":"Peng Li, Yunfeng Zhao, Liandong Chen, Kai Cheng, Chuyue Xie, Xiaofei Wang, Qinghua Hu","doi":"10.1109/SmartIoT55134.2022.00032","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00032","url":null,"abstract":"Federated learning is a hot machine learning research direction, its goal is to train a high quality central model while protecting the privacy of all parties, and it has a broad application prospect in smart grid and other fields. However, in federated learning with massive client participation, it is impossible to have all clients participate in training and model aggregation every time due to the limitation of communication and computing resources. Usually the method of selecting clients for federated learning is random, some studies have studied this problem from aspects of client data quality, model training effect, communication and computing resources, etc. In this paper, we propose an active client selection algorithm from the perspective of model uncertainty, this algorithm is called uncertainty measured active client selection in FL (UCS-FL). The server actively selects a subset of clients to participate in the FL training, and the unselected clients do not need to train in this round, saving computing and communication resources. Perform a thorough empirical analysis of the image classification task to demonstrate the excellent performance of UCS-FL against baseline in the context of monitored FL settings. Finally, we describes the real-world application of the proposed architecture, especially in smart grid scenarios.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127194453","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00015
Yuntao Wu, Mingyi Chen
In this article, a low-voltage low-power voltage reference used for smart IoT devices is presented. The proposed volt-age reference is supported by subthreshold MOSFETs with compensation of proportional-to-absolute-temperature (PTAT) voltage and complementary-to-absolute-temperature (CTAT) gate-source voltage. A voltage doubler boosts the input voltage so that the reference can operate at an extra low voltage. Implemented with 180 nm CMOS technology, simulation results show that the voltage reference obtains a TC of 12 ppm $/^{circ}mathrm{C}$ under 0.32 V supply within $text{-}20 ^{circ}mathrm{C}$ to 120 $^{circ}mathbf{C}$. The voltage reference has a power consumption of 151 nW, with a total noise of 0.9 $mu mathbf{V}$ rms(0.5–10M Hz) and PSRR of −50 dB.
{"title":"A 0.32-V 151-nW Voltage Reference for Smart IoT Applications","authors":"Yuntao Wu, Mingyi Chen","doi":"10.1109/SmartIoT55134.2022.00015","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00015","url":null,"abstract":"In this article, a low-voltage low-power voltage reference used for smart IoT devices is presented. The proposed volt-age reference is supported by subthreshold MOSFETs with compensation of proportional-to-absolute-temperature (PTAT) voltage and complementary-to-absolute-temperature (CTAT) gate-source voltage. A voltage doubler boosts the input voltage so that the reference can operate at an extra low voltage. Implemented with 180 nm CMOS technology, simulation results show that the voltage reference obtains a TC of 12 ppm $/^{circ}mathrm{C}$ under 0.32 V supply within $text{-}20 ^{circ}mathrm{C}$ to 120 $^{circ}mathbf{C}$. The voltage reference has a power consumption of 151 nW, with a total noise of 0.9 $mu mathbf{V}$ rms(0.5–10M Hz) and PSRR of −50 dB.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121234921","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00049
Bin Zhang, Yi Hao, Jing Zhou, Xiaoming Li, Huichao Li, Shuai Wang, Ximin Sun
In response to the phenomenon of weak warehouse security and low defensiveness, we have built an intelligent risk identification security management and control system to help the company's warehouse security comprehensively improve. By combining multi-dimensional video, infrared monitoring, image recognition, trajectory motion mathematical model, multi-dimensional information fusion and other related technologies, the system recognizes and monitors the movement trajectory of warehouse personnel, work clothes, and safety helmets, and conducts multi-dimensional safety management and control. Realize the identification of unsafe clothing and real-time warning and prompt of illegal intrusion of staff in the warehouse, minimize the occurrence and prevention of safety accidents, and improve the company's intelligent warehouse safety management and control level.
{"title":"Visualized Intelligent Warehouse Safety Control System Using Target Detection","authors":"Bin Zhang, Yi Hao, Jing Zhou, Xiaoming Li, Huichao Li, Shuai Wang, Ximin Sun","doi":"10.1109/SmartIoT55134.2022.00049","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00049","url":null,"abstract":"In response to the phenomenon of weak warehouse security and low defensiveness, we have built an intelligent risk identification security management and control system to help the company's warehouse security comprehensively improve. By combining multi-dimensional video, infrared monitoring, image recognition, trajectory motion mathematical model, multi-dimensional information fusion and other related technologies, the system recognizes and monitors the movement trajectory of warehouse personnel, work clothes, and safety helmets, and conducts multi-dimensional safety management and control. Realize the identification of unsafe clothing and real-time warning and prompt of illegal intrusion of staff in the warehouse, minimize the occurrence and prevention of safety accidents, and improve the company's intelligent warehouse safety management and control level.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125556258","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00047
Yong Li, Xiaoyun Tian, Jiangkai Jia, Bin Zheng, Hairu Li, Mingda Wang, Ximin Sun
The warehousing and logistics industry is a basic, strategic and leading industry that supports the development of the national economy. Efforts must be made to improve the intelligent level of warehousing and logistics in the Turnover Time. Warehousing and logistics in the power field are large in scale and wide in scope. In this paper, we use exponential smoothing algorithm to compress large amounts of data while eliminating extreme data. K-means and DBSCAN algorithms are used to deal with data factors related to the turnover time of warehousing materials.
{"title":"Optimization of Warehousing Material Turnover Time Based on Clustering","authors":"Yong Li, Xiaoyun Tian, Jiangkai Jia, Bin Zheng, Hairu Li, Mingda Wang, Ximin Sun","doi":"10.1109/SmartIoT55134.2022.00047","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00047","url":null,"abstract":"The warehousing and logistics industry is a basic, strategic and leading industry that supports the development of the national economy. Efforts must be made to improve the intelligent level of warehousing and logistics in the Turnover Time. Warehousing and logistics in the power field are large in scale and wide in scope. In this paper, we use exponential smoothing algorithm to compress large amounts of data while eliminating extreme data. K-means and DBSCAN algorithms are used to deal with data factors related to the turnover time of warehousing materials.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128081689","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00012
Lawrence He, Mark Eastburn
Nanosensors synthesize the most recent advantages of nanomaterials and biosensing technologies. Injury detection is one of the important areas of nanosensor applications in healthcare. It is especially useful to the injuries that are more difficult to diagnose at the early stage with the traditional medical methods. This paper focuses on the nanosensor networks for human body injury detection. After reviewing recent progress in the biomedical nanosensor development, an architecture is proposed to employ nanosensors to collect the bio-parameters of the injury part in a human body. Key elements of the architecture are nanosensors, data collectors, medical servers, as well as healthcare providers. Major functions on the biomedical data processing are analyzed in a structure with three layers: sensing layer, networking layer, and application layer. Each layer conducts different data processing functionalities to facilitate sensing, collecting, and analyzing of the vitals. Based on the IEEE nano-scale communication framework, a mathematical model is further derived. This model represents the trade-off between the nanosensor network resource and its injury detection performance. The problem constraints describe the characteristics of the patient body and the injury part. Simulations are conducted in several sets of typical cases to evaluate the model performance. Results demonstrate that the nanosensor amount selection is determined by multiple bio-factors of the human body and the injury part.
{"title":"Smart Nanosensor Networks for Body Injury Detection","authors":"Lawrence He, Mark Eastburn","doi":"10.1109/SmartIoT55134.2022.00012","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00012","url":null,"abstract":"Nanosensors synthesize the most recent advantages of nanomaterials and biosensing technologies. Injury detection is one of the important areas of nanosensor applications in healthcare. It is especially useful to the injuries that are more difficult to diagnose at the early stage with the traditional medical methods. This paper focuses on the nanosensor networks for human body injury detection. After reviewing recent progress in the biomedical nanosensor development, an architecture is proposed to employ nanosensors to collect the bio-parameters of the injury part in a human body. Key elements of the architecture are nanosensors, data collectors, medical servers, as well as healthcare providers. Major functions on the biomedical data processing are analyzed in a structure with three layers: sensing layer, networking layer, and application layer. Each layer conducts different data processing functionalities to facilitate sensing, collecting, and analyzing of the vitals. Based on the IEEE nano-scale communication framework, a mathematical model is further derived. This model represents the trade-off between the nanosensor network resource and its injury detection performance. The problem constraints describe the characteristics of the patient body and the injury part. Simulations are conducted in several sets of typical cases to evaluate the model performance. Results demonstrate that the nanosensor amount selection is determined by multiple bio-factors of the human body and the injury part.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130315665","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00048
Ximin Sun, Jiangkai Jia, Zan Liu, Yong Li, Bo Sun, Dan Liu
Current edge cloud resource management approaches generally target clusters with specific purposes and can only be optimized for one load variation at a time. However, large general industrial IoT cloud platforms have multiple system architectures, which provide a wide range of resources and service characteristics. Meanwhile, there is a huge difference between the application type and the resource demand of the application, which leads to drastic energy consumption fluctuations and resource heterogeneity. The existing edge computing architecture and scheduling algorithm do not consider the impact of dynamic factors on the computational load. In this paper, a scheduling mechanism based on game theory and queuing networks is proposed for resource allocation and load balancing in IIoT.
{"title":"Resource Allocation and Load Balancing Based on Edge Computing in Industrial Networks","authors":"Ximin Sun, Jiangkai Jia, Zan Liu, Yong Li, Bo Sun, Dan Liu","doi":"10.1109/SmartIoT55134.2022.00048","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00048","url":null,"abstract":"Current edge cloud resource management approaches generally target clusters with specific purposes and can only be optimized for one load variation at a time. However, large general industrial IoT cloud platforms have multiple system architectures, which provide a wide range of resources and service characteristics. Meanwhile, there is a huge difference between the application type and the resource demand of the application, which leads to drastic energy consumption fluctuations and resource heterogeneity. The existing edge computing architecture and scheduling algorithm do not consider the impact of dynamic factors on the computational load. In this paper, a scheduling mechanism based on game theory and queuing networks is proposed for resource allocation and load balancing in IIoT.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133415982","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00017
M. Usman, M. Alryan, M. Qaiser, M. Omar, S. Khan, S. Larkin, B. Raw
This manuscript presents a smart, IoT-enabled cold-storage monitoring system for human blood. The communication between the nodes and gateway is achieved by Low Power Wide Area Network (LoRaWAN). The sensing node and gateway are fabricated, and measurements have been conducted to evaluate the sensitivity, battery life, and coverage area. The results demonstrate that a 6 km radius of the dense urban area is covered without a line of sight. The main purpose is to monitor the putrefiable blood samples that expire if the optimal temperature is hampered unnoticed and the storage duration exceeds its shelf life. Likewise, an AI-based solution has been provided to monitor the availability of blood samples. Moreover, the system will also inform the authoritative personnel about the inventory and the availability of potential donors.
本文介绍了一种智能的、支持物联网的人体血液冷藏监测系统。节点与网关之间的通信通过低功率广域网LoRaWAN (Low Power Wide Area Network)实现。制作了传感节点和网关，并进行了测量以评估灵敏度、电池寿命和覆盖面积。结果表明，密集城区半径6 km范围内无视线覆盖。主要目的是监测在最佳温度被忽视和储存时间超过保质期的情况下过期的可腐烂血液样本。同样，已经提供了一种基于人工智能的解决方案来监测血液样本的可用性。此外，该系统还将向主管人员通报潜在捐助者的清单和可用情况。
{"title":"IoT-Based Blood Quality Monitoring: Using LoRaWAN","authors":"M. Usman, M. Alryan, M. Qaiser, M. Omar, S. Khan, S. Larkin, B. Raw","doi":"10.1109/SmartIoT55134.2022.00017","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00017","url":null,"abstract":"This manuscript presents a smart, IoT-enabled cold-storage monitoring system for human blood. The communication between the nodes and gateway is achieved by Low Power Wide Area Network (LoRaWAN). The sensing node and gateway are fabricated, and measurements have been conducted to evaluate the sensitivity, battery life, and coverage area. The results demonstrate that a 6 km radius of the dense urban area is covered without a line of sight. The main purpose is to monitor the putrefiable blood samples that expire if the optimal temperature is hampered unnoticed and the storage duration exceeds its shelf life. Likewise, an AI-based solution has been provided to monitor the availability of blood samples. Moreover, the system will also inform the authoritative personnel about the inventory and the availability of potential donors.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129149999","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00022
E. Kadusic, N. Zivic, Narcisa Hadzajlic, C. Ruland
With the global transition to the IPv6 (Internet Protocol version 6), IP (Internet Protocol) validation efficiency and IPv6 support from the aspect of network programming are gaining more importance. As global computer networks grow in the era of IoT (Internet of Things), IP address validation is an inevitable process for assuring strong network privacy and security. The complexity of IP validation has been increased due to the rather drastic change in the memory architecture needed for storing IPv6 addresses. Low-level programming languages like C/C++ are a great choice for handling memory spaces and working with simple devices connected in an IoT (Internet of Things) network. This paper analyzes some user-defined and open-source implementations of IP validation codes in Boost. Asio and POCO C++ networking libraries, as well as the IP security support provided for general networking purposes and IoT. Considering a couple of sample codes, the paper gives a conclusion on whether these C++ implementations answer the needs for flexibility and security of the upcoming era of IPv6 addressed computers.
随着全球向IPv6 (Internet Protocol version 6)的过渡，IP (Internet Protocol)的验证效率和网络编程方面对IPv6的支持变得越来越重要。随着全球计算机网络在物联网(IoT)时代的发展，IP地址验证是确保强大网络隐私和安全性的必然过程。由于存储IPv6地址所需的内存架构发生了相当大的变化，IP验证的复杂性增加了。像C/ c++这样的低级编程语言是处理内存空间和处理IoT(物联网)网络中连接的简单设备的绝佳选择。本文分析了Boost中IP验证码的一些用户定义实现和开源实现。Asio和POCO c++网络库，以及为一般网络目的和物联网提供的IP安全支持。考虑到几个示例代码，本文给出了这些c++实现是否满足即将到来的IPv6寻址计算机时代对灵活性和安全性的需求的结论。
{"title":"The transitional phase of Boost.Asio and POCO C++ networking libraries towards IPv6 and IoT networking security","authors":"E. Kadusic, N. Zivic, Narcisa Hadzajlic, C. Ruland","doi":"10.1109/SmartIoT55134.2022.00022","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00022","url":null,"abstract":"With the global transition to the IPv6 (Internet Protocol version 6), IP (Internet Protocol) validation efficiency and IPv6 support from the aspect of network programming are gaining more importance. As global computer networks grow in the era of IoT (Internet of Things), IP address validation is an inevitable process for assuring strong network privacy and security. The complexity of IP validation has been increased due to the rather drastic change in the memory architecture needed for storing IPv6 addresses. Low-level programming languages like C/C++ are a great choice for handling memory spaces and working with simple devices connected in an IoT (Internet of Things) network. This paper analyzes some user-defined and open-source implementations of IP validation codes in Boost. Asio and POCO C++ networking libraries, as well as the IP security support provided for general networking purposes and IoT. Considering a couple of sample codes, the paper gives a conclusion on whether these C++ implementations answer the needs for flexibility and security of the upcoming era of IPv6 addressed computers.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115057609","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 : 2022-08-01DOI: 10.1109/SmartIoT55134.2022.00016
Tiancong Wang, Bin Wang
In recent years, RFID technologies have been widely used in real-life applications, including supply chain manage-ment, warehouse tracking, etc. One of the concerns is the estimation of the number of interested tags. The existence of unknown tags will affect normal operation and management for RFID systems. Existing protocols for estimating the number of unknown tags generally assume ideal communication channels. In practice, there may exist environment interference that affects the transmission from tags to a RFID reader. In this paper, we study the problem of estimating the number of unknown tags under unreliable channels, and propose a cardinality estimation scheme CEUT(Cardinality Estimation for Unknown Tags under unreliable channels). The reader collects responses from all tags after running the Aloha protocol. The number of non-empty slots in the response frame increases due to the presence of unknown tags is designed based on the number of empty slots in the predicted frame converted into non-empty slots in the response frame and the channel noise parameter. The simulation results show that, under the unreliable channel, the estimation result scheme yielded by CEUT is more robust than other existing schemes, and can achieve therequired estimation accuracy.
近年来，RFID技术在现实生活中得到了广泛的应用，包括供应链管理、仓库跟踪等。其中一个问题是对感兴趣的标签数量的估计。未知标签的存在会影响RFID系统的正常运行和管理。现有用于估计未知标签数量的协议通常假设理想的通信信道。在实际应用中，可能存在环境干扰，影响从标签到RFID阅读器的传输。本文研究了不可靠信道下未知标签数量的估计问题，提出了一种不可靠信道下未知标签的基数估计方案CEUT(cardinality estimation for unknown tags under不可靠信道)。阅读器在运行Aloha协议后收集来自所有标签的响应。响应帧中由于未知标签的存在而增加的非空槽数是根据预测帧中转换为响应帧中的非空槽数和信道噪声参数来设计的。仿真结果表明，在不可靠信道下，CEUT估计结果方案比其他方案具有更强的鲁棒性，能够达到要求的估计精度。
{"title":"A cardinality estimation scheme for the number of unknown RFID tags under unreliable channels","authors":"Tiancong Wang, Bin Wang","doi":"10.1109/SmartIoT55134.2022.00016","DOIUrl":"https://doi.org/10.1109/SmartIoT55134.2022.00016","url":null,"abstract":"In recent years, RFID technologies have been widely used in real-life applications, including supply chain manage-ment, warehouse tracking, etc. One of the concerns is the estimation of the number of interested tags. The existence of unknown tags will affect normal operation and management for RFID systems. Existing protocols for estimating the number of unknown tags generally assume ideal communication channels. In practice, there may exist environment interference that affects the transmission from tags to a RFID reader. In this paper, we study the problem of estimating the number of unknown tags under unreliable channels, and propose a cardinality estimation scheme CEUT(Cardinality Estimation for Unknown Tags under unreliable channels). The reader collects responses from all tags after running the Aloha protocol. The number of non-empty slots in the response frame increases due to the presence of unknown tags is designed based on the number of empty slots in the predicted frame converted into non-empty slots in the response frame and the channel noise parameter. The simulation results show that, under the unreliable channel, the estimation result scheme yielded by CEUT is more robust than other existing schemes, and can achieve therequired estimation accuracy.","PeriodicalId":422269,"journal":{"name":"2022 IEEE International Conference on Smart Internet of Things (SmartIoT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130858633","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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