IEEE Internet of Things Magazine最新文献_第3页

Dynamic Artificial Neural Network-Assisted GPS-Less Navigation for IoT-Enabled Drones 物联网无人机的动态人工神经网络辅助 GPS 导航

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2200276

Murat Simsek, A. Boukerche, B. Kantarci, Rahman Bitirgen, M. Hancer, Ismail Bayezit

{"title":"Dynamic Artificial Neural Network-Assisted GPS-Less Navigation for IoT-Enabled Drones","authors":"Murat Simsek, A. Boukerche, B. Kantarci, Rahman Bitirgen, M. Hancer, Ismail Bayezit","doi":"10.1109/IOTM.001.2200276","DOIUrl":"https://doi.org/10.1109/IOTM.001.2200276","url":null,"abstract":"Uncrewed Aerial Vehicles (UAVs) have enabled key duties in emergency preparedness, traffic monitoring, environmental monitoring, and public safety. Since the presence of GPS-enabled contexts is not always guaranteed, a grand challenge with the UAVs is the lack of accomplishing their tasks without the presence of GPS coordinates (latitude, longitude, and altitude). Hence, the performance of UAVs in GPS-denied environments is expected to degrade dramatically when compared to the UAVs employed in GPS-enabled environments. In this article, an alternative approach to the state-of-the-art, Dynamic Artificial Neural Network (D-ANN)-based solution is proposed to assist UAV navigation without GPS positions during a mission. Besides accelerometer and gyroscope data, Pulse Width Modulation (PWM) signals, which have been traditionally used in the design of UAV flight controllers, are proposed to be a part of the input for D-ANN-assisted UAV navigation without GPS data. Since the latitude, longitude, and altitude values of the UAV are not correlated, each position is obtained through a separate D-ANN system. The proposed D-ANN location of a quadrotor UAV assisted by D-ANN has less than 3m average destination error at the end of the testing trajectory and also less than 0.12 average normalized mean square error during the testing trajectory in terms of the 3D GPS coordinates.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"8 5","pages":"92-99"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141054984","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

Federated Generative Artificial Intelligence Empowered Traffic Flow Prediction Under Vehicular Computing Power Networks 车载计算动力网络下的联合生成人工智能交通流预测

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300259

Yujie Ye, Zitong Zhao, Lei Liu, Jie Feng, Jun Du, Qingqi Pei

{"title":"Federated Generative Artificial Intelligence Empowered Traffic Flow Prediction Under Vehicular Computing Power Networks","authors":"Yujie Ye, Zitong Zhao, Lei Liu, Jie Feng, Jun Du, Qingqi Pei","doi":"10.1109/IOTM.001.2300259","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300259","url":null,"abstract":"Traffic flow prediction holds great promise in prompting the rapid development of intelligent transportation systems. The key challenge for traffic flow prediction lies in effectively modeling the complicated spatiotemporal dependencies of traffic data while considering privacy and cost concerns. Existing methods based on neural networks exhibit limitations, particularly in handling dynamic data and long-distance dependencies. To address these challenges, we have proposed a novel distributed traffic flow prediction architecture that makes the integration of generative artificial intelligence (AI) and hierarchical federated learning. This architecture makes the prediction of traffic flow by incorporating spatial self-attention module and traffic delay-aware feature transformation module, which achieves a better balance between communication and computation costs, enhances training efficiency and guarantees data privacy and security. Next, we have introduced the important characteristics and key technologies used for this devised architecture. Finally, several open issues are given with the aim to attract more attentions for further investigation.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"21 3","pages":"56-61"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141031354","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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Cover 2 封二

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/miot.2024.10517519

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NTN-Aided Quality and Energy-Aware Data Collection in Time-Critical Robotic Wireless Sensor Networks 时间关键型机器人无线传感器网络中的 NTN 辅助质量和能量感知数据采集

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300200

O. Gul, A. Erkmen, B. Kantarci

{"title":"NTN-Aided Quality and Energy-Aware Data Collection in Time-Critical Robotic Wireless Sensor Networks","authors":"O. Gul, A. Erkmen, B. Kantarci","doi":"10.1109/IOTM.001.2300200","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300200","url":null,"abstract":"Through the use of flying objects such as satellites and uncrewed aerial vehicles (UAVs), non-terrestrial networks (NTNs) have recently garnered interest in large-scale and developing wireless communication networks. UAV-assisted networks are quickly becoming part of future communication systems. This article overviews the recent works in which UAV-driven cluster-based data-gathering policies have been proposed for heterogeneous robotics and wireless sensor networks (RWSNs) where a UAV with limited-capacity battery visits a group of cluster head (CH) robots for gathering data by considering their energy consumption and data qualities under data hopping limits and NTN standards. In light of the importance of time-critical communications, an RWSN requires different data collection algorithms from WSN such that it includes no cluster-forming stage unlike the algorithms for WSN. Moreover, in the case of time-sensitive tasks under RWSN and under the quality and energy constraints, maximum latency from a nonvisited CH robot to a visited CH robot needs to be reduced with less number of hops in the RWSN. Further-more, the maximum number of forwarding attempts over a CH robot needs to be reduced, which is presented as an important future research direction.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"268 1","pages":"114-120"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141031877","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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Cover 4 封面 4

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/miot.2024.10517520

引用次数: 0

EdgeGAN: Enhancing Sleep Quality Monitoring in Medical IoT Through Generative AI at the Edge EdgeGAN：通过边缘生成式人工智能加强医疗物联网中的睡眠质量监测

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300276

Kang Peng, Hua He, Jingling Liu, Tao Li, Shenglong Hou, Sibo Qiao

{"title":"EdgeGAN: Enhancing Sleep Quality Monitoring in Medical IoT Through Generative AI at the Edge","authors":"Kang Peng, Hua He, Jingling Liu, Tao Li, Shenglong Hou, Sibo Qiao","doi":"10.1109/IOTM.001.2300276","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300276","url":null,"abstract":"In light of the brisk tempo characterizing contemporary lifestyles and the escalating burden of diverse stressors, the decline in the quality of individuals' sleep has emerged as a consequential issue exerting a notable impact on human physiological health. This article introduces the EdgeGAN system, which proposes a hybrid architecture for medical smart beds aimed at proficiently monitoring sleep quality. The EdgeGAN system seamlessly integrates the Internet of Things (IoT) and edge computing through the incorporation of lightweight Generative Adversarial Networks (GAN) into edge computing devices. The amalgamation of this integration serves to enhance the efficacy of sleep quality monitoring. Relative to conventional sleep monitoring systems, the EdgeGAN system offers reduced computational complexity and streamlined user operation. Furthermore, it adeptly captures long-term temporal dependencies in sleep data, thereby extending the retention time of historical information. It also exhibits exceptional compatibility with sleep monitoring devices. Moreover, the EdgeGAN system possesses the capability to intelligently determine whether to upload pertinent data to the cloud based on user preferences, thereby diminishing reliance on cloud resources. In comparison to traditional cloud platform systems, the EdgeGAN system proposed in this article has the capability to circumvent data blockages arising from increased user requests. This innovation enhances real-time performance and compatibility in sleep monitoring, prioritizing user privacy protection. As a result, it offers an intelligent and convenient solution for the development of future smart medical devices.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"14 10","pages":"16-21"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141029615","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

Generative Edge Intelligence for IoT-Assisted Vehicle Accident Detection: Challenges and Prospects 用于物联网辅助车辆事故检测的生成边缘智能：挑战与前景

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300282

Jiahui Liu, Yang Liu, Kun Gao, Liang Wang

引用次数: 0

UAV-Assisted VLC Using LED-Based Grow Lights in Precision Agriculture Systems 在精准农业系统中使用基于 LED 的生长灯的无人机辅助 VLC

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300122

Hussam Ibraiwish, M. Eltokhey, M. Alouini

引用次数: 0

Traceability and Performance Optimization: Application of Generative AI, Digital Twin, and DRL in the Recycling Process of WEEE 可追溯性和性能优化：生成式人工智能、数字双胞胎和 DRL 在废弃电子电气设备回收过程中的应用

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300261

Jinlong Wang, Yixin Li, Shangzhuo Zhou, Yuanyuan Zhang, Xiaoyun Xiong, Weiwei Zhai

{"title":"Traceability and Performance Optimization: Application of Generative AI, Digital Twin, and DRL in the Recycling Process of WEEE","authors":"Jinlong Wang, Yixin Li, Shangzhuo Zhou, Yuanyuan Zhang, Xiaoyun Xiong, Weiwei Zhai","doi":"10.1109/IOTM.001.2300261","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300261","url":null,"abstract":"The lack of transparency, unified standards, and effective regulation, along with the complexity of the supply chain, make it challenging to achieve reliable traceability throughout the entire process of recycling and reusing waste electronic appliances. This poses a challenge for effectively implementing carbon reduction measures. In response to the above issues, we propose a full process data management solution for WEEE recycling based on blockchain technology. In addition, a method combining digital twin and generative AI technology has been proposed to address the performance bottleneck issue of blockchain. Predicting future data flow through generative AI models and utilizing reinforcement learning algorithms to predictively optimize blockchain parameter configurations effectively improve blockchain performance and scalability. The experimental results demonstrate that the proposed method effectively enhances system adaptability and throughput. It achieves an integration of reliable traceability, accurate prediction, and performance optimization throughout the entire process of WEEE recycling and reuse data management.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"1 10","pages":"22-28"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141055943","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

Machine Learning Explainability for Intrusion Detection in the Industrial Internet of Things 工业物联网入侵检测的机器学习可解释性

IEEE Internet of Things Magazine Pub Date : 2024-05-01 DOI: 10.1109/IOTM.001.2300171

Love Allen Chijioke Ahakonye, C. I. Nwakanma, Jae Min Lee, Dong‐Seong Kim

{"title":"Machine Learning Explainability for Intrusion Detection in the Industrial Internet of Things","authors":"Love Allen Chijioke Ahakonye, C. I. Nwakanma, Jae Min Lee, Dong‐Seong Kim","doi":"10.1109/IOTM.001.2300171","DOIUrl":"https://doi.org/10.1109/IOTM.001.2300171","url":null,"abstract":"Intrusion and attacks have consistently challenged the Industrial Internet of Things (IIoT). Although artificial intelligence (AI) rapidly develops in attack detection and mitigation, building convincing trust is difficult due to its black-box nature. Its unexplained outcome inhibits informed and adequate decision-making of the experts and stakeholders. Explainable AI (XAI) has emerged to help with this problem. However, the ease of comprehensibility of XAI interpretation remains an issue due to the complexity and reliance on statistical theories. This study integrates agnostic post-hoc LIME and SHAP explainability approaches on intrusion detection systems built using representative AI models to explain the model's decisions and provide more insights into interpretability. The decision and confidence impact ratios assessed the significance of features and model dependencies, enhancing cybersecurity experts' trust and informed decisions. The experimental findings highlight the importance of these explainability techniques for understanding and mitigating IIoT intrusions with recourse to significant data features and model decisions.","PeriodicalId":235472,"journal":{"name":"IEEE Internet of Things Magazine","volume":"46 10","pages":"68-74"},"PeriodicalIF":0.0,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141033223","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