2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)最新文献_第2页

IoT Sensor Data Consistency using Deep Learning 使用深度学习的物联网传感器数据一致性

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9975955

I. Zualkernan, Nadeen Ahmed, A. Elmeligy, Adham Abdelnaby, Nouran Sheta

引用次数: 0

Predicting Text-To-Speech Quality using Brain Activity 利用大脑活动预测文本到语音的质量

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9975857

Rhenaldy, Ladysa Stella Karenza, Ivan Halim Parmonangan, F. Kurniadi

引用次数: 0

Video-Based Real-Time Heart Rate Detection for Drivers Inside the Cabin Using a Smartphone 基于视频的实时心率检测，用于驾驶舱内的智能手机

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9975941

Walaa Othman, A. Kashevnik

引用次数: 3

Performance of PHY/MAC Cross-Layer Design for Next-Generation V2X Applications 面向下一代V2X应用的PHY/MAC跨层设计性能研究

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9975999

Andy Triwinarko, S. Cherkaoui, I. Dayoub

{"title":"Performance of PHY/MAC Cross-Layer Design for Next-Generation V2X Applications","authors":"Andy Triwinarko, S. Cherkaoui, I. Dayoub","doi":"10.1109/IoTaIS56727.2022.9975999","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975999","url":null,"abstract":"This paper proposed the use of physical (PHY) and medium access control (MAC) cross-layer approach to obtain two goals outlined by the next-generation V2X (NGV) ’s project authorisation request (PAR) of IEEE 802.11bd group, namely having twice the MAC layer throughput and able to operate in a high mobility scenario of up to 500 km/h. At the PHY layer, we suggested utilising mid-ambles channel estimation (MCE), dual-carrier modulation (DCM), and multiple-input multiple-output space-time block coding (MIMO-STBC). At the MAC layer, we suggested an aggregate MAC protocol data unit (A-MPDU) aggregation technique, choosing an appropriate contention window (CW) value, and setting a limit for re-transmissions. We designed a model utilising a cross-layer approach then we simulated the performance of normalised system throughput for two types of V2X applications, namely safety-related (high reliability) and non-safety (high throughput) V2X applications. To better portray the high-mobility scenario, we used the enhanced highway line of sight (LOS) channel model. Our simulation results showed two times normalized throughput performance improvement for both V2X applications in a high mobility environment, as requested by the NGV standard’s PAR.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125592350","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

Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices 为移动设备上的传输模式检测选择资源高效的ML模型

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9976004

Philipp Matthes, T. Springer

{"title":"Selecting Resource-Efficient ML Models for Transport Mode Detection on Mobile Devices","authors":"Philipp Matthes, T. Springer","doi":"10.1109/IoTaIS56727.2022.9976004","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9976004","url":null,"abstract":"Processing data closer to the source to minimize latency and the amount of data to be transmitted is a major driver for research on the Internet of Things (IoT). Since data processing in many IoT scenarios heavily depends on machine learning (ML), designing ML models for resource constraint devices at the edge of IoT infrastructures is one of the big challenges. Which ML model performs best highly depends on the problem domain but also on the availability of resources. Thus, to find an appropriate ML model in the broad search space of options, the trade-off between accuracy and resource consumption in terms of memory, CPU, and energy needs to be considered. However, there are ML problems where most current research focuses on accuracy, and the resource consumption of applicable models is not well investigated yet. We show that transport mode detection (TMD) is such a problem and present a case study for designing an ML model running on smartphones. To transform the search for the needle in the haystack into a structured design process, we propose an engineering workflow to systematically evolve ML model candidates, considering portability and resource consumption in addition to model accuracy. At the example of the Sussex-Huawei-Locomotion (SHL) dataset, we apply this process to multiple ML architectures and find a suitable model that convinces with high accuracy and low measured resource consumption for smartphone deployment. We discuss lessons learned, enabling engineers and researchers to use our workflow as a blueprint to identify solutions for their ML problems systematically.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114222743","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

A proposal on the control mechanism among distributed MQTT brokers over wide area networks 广域网上分布式MQTT代理间控制机制的一种建议

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9976024

Yuto Noda, K. Ishibashi, T. Yokotani

引用次数: 0

Performance Analysis Forwarding Strategies Based Sdn-Ndn 基于Sdn-Ndn的转发策略性能分析

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9976003

D. Pratama, L. V. Yovita, S. N. Hertiana

引用次数: 1

IoT-based Experimental Aquarium Environment for Observing Crabs 基于物联网的实验水族螃蟹观察环境

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9975886

Sota Nakajima, Daiki Sumiya, M. Morii, Nobuaki Mizutani, Akitsugu Shimano, M. Niswar, Shigeru Kashihara

引用次数: 0

Verification of Feature Detection Through Thermal Imaging: An Extension of PiBase 通过热成像验证特征检测:PiBase的扩展

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9975939

Anthony Atkinson, Venkat Margapuri, Michael L. Neilsen

{"title":"Verification of Feature Detection Through Thermal Imaging: An Extension of PiBase","authors":"Anthony Atkinson, Venkat Margapuri, Michael L. Neilsen","doi":"10.1109/IoTaIS56727.2022.9975939","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975939","url":null,"abstract":"PiBase is a low-cost, Internet-of-Things-capable security systems. It offers users an integrated system of hardware and software in the form of a basic smart camera made from a Raspberry Pi and off-the-shelf parts, an Android app, and a backend built for Google Firebase. Using Haar-feature cascade classifiers and Linear Binary Pattern Histograms, it attempts to provide comprehensive detection and recognition of potential security threats. Being only a prototype there are some vulnerabilities in the initial design. This new design addresses one security threat, namely the possibility of mimicking an authorized user’s appearance. This is achieved through the integration of thermal imaging alongside the original camera used in the system. Some challenges with this approach include maintaining low-cost and part accessibility, working within limitations of the hardware, and choosing an effective method of integration. The proposed solution addresses each of these, in addition to the original issue, by transforming the output of a low-resolution thermal sensor array into a kind of clipping-mask to filter out non-human objects from the input image before performing its other operations.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130169324","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

Towards Adaptive Cybersecurity for Green IoT

2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS) Pub Date : 2022-11-24 DOI: 10.1109/IoTaIS56727.2022.9975990

Talal Halabi, Martine Bellaïche, B. Fung

{"title":"Towards Adaptive Cybersecurity for Green IoT","authors":"Talal Halabi, Martine Bellaïche, B. Fung","doi":"10.1109/IoTaIS56727.2022.9975990","DOIUrl":"https://doi.org/10.1109/IoTaIS56727.2022.9975990","url":null,"abstract":"The Internet of Things (IoT) paradigm has led to an explosion in the number of IoT devices and an exponential rise in carbon footprint incurred by overburdened IoT networks and pervasive cloud/edge communications. Hence, there is a growing interest in industry and academia to enable the efficient use of computing infrastructures by optimizing the management of data center and IoT resources (hardware, software, network, and data) and reducing operational costs to slash greenhouse gas emissions and create healthy environments. Cybersecurity has also been considered in such efforts as a contributor to these environmental issues. Nonetheless, most green security approaches focus on designing low-overhead encryption schemes and do not emphasize energy-efficient security from architectural and deployment viewpoints. This paper sheds light on the emerging paradigm of adaptive cybersecurity as one of the research directions to support sustainable computing in green IoT. It presents three potential research directions and their associated methods for designing and deploying adaptive security in green computing and resource-constrained IoT environments to save on energy consumption. Such efforts will transform the development of data-driven IoT security solutions to be greener and more environment-friendly.","PeriodicalId":138894,"journal":{"name":"2022 IEEE International Conference on Internet of Things and Intelligence Systems (IoTaIS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125844111","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: 2