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Federated Learning in the Sky: Aerial-Ground Air Quality Sensing Framework with UAV Swarms [article]

Yi Liu, Jiangtian Nie, Xuandi Li, Syed Hassan Ahmed, Wei Yang Bryan Lim, Chunyan Miao
2020 arXiv   pre-print
To this end, this paper proposes a new federated learning-based aerial-ground air quality sensing framework for fine-grained 3D air quality monitoring and forecasting.  ...  Through extensive case studies on a real-world dataset, numerical results show that the proposed framework can achieve accurate and energy-efficient AQI sensing without compromising the privacy of raw  ...  In the framework we designed, we use an improved lightweight CNN model suitable, i.e., MobileNet model for mobile phones to achieve fine-grained 3D AQI monitoring.  ... 
arXiv:2007.12004v1 fatcat:c2dgwdpncvfbxibjohrquhzhc4

Wireless AI: Enabling an AI-Governed Data Life Cycle [article]

Dinh C. Nguyen, Peng Cheng, Ming Ding, David Lopez-Perez, Pubudu N. Pathirana, Jun Li, Aruna Seneviratne, Yonghui Li, H. Vincent Poor
2020 arXiv   pre-print
Recent years have seen rapid deployment of mobile computing and Internet of Things (IoT) networks, which can be mostly attributed to the increasing communication and sensing capabilities of wireless systems  ...  Big data analysis, pervasive computing , and eventually artificial intelligence (AI) are envisaged to be deployed on top of IoT and create a new world featured by data-driven AI.  ...  [73] Crowd sensing CNN A pattern recognition model for detecting unimportant information and extract useful features for lightweight crowd sensing.  ... 
arXiv:2003.00866v1 fatcat:dofctwtag5ewhhzaseeukhtxbe

2020 Index IEEE Transactions on Information Forensics and Security Vol. 15

2020 IEEE Transactions on Information Forensics and Security  
., +, TIFS 2020 2386-2401 PriRadar: A Privacy-Preserving Framework for Spatial Crowdsourcing.  ...  He, W., +, TIFS 2020 3859-3871 Crowdsourcing PriRadar: A Privacy-Preserving Framework for Spatial Crowdsourcing.  ... 
doi:10.1109/tifs.2021.3053735 fatcat:eforexmnczeqzdj3sc2j4yoige

Deep Learning in Mobile and Wireless Networking: A Survey

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 IEEE Communications Surveys and Tutorials  
In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research.  ...  [500] propose a framework called AR-DEN to preserve users' privacy while reducing communication overhead in mobile-cloud deep learning applications.  ... 
doi:10.1109/comst.2019.2904897 fatcat:xmmrndjbsfdetpa5ef5e3v4xda

Deep Learning in Mobile and Wireless Networking: A Survey [article]

Chaoyun Zhang, Paul Patras, Hamed Haddadi
2019 arXiv   pre-print
In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas.  ...  Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics.  ...  [497] propose a framework called AR-DEN to preserve users' privacy while reducing communication overhead in mobile-cloud deep learning applications.  ... 
arXiv:1803.04311v3 fatcat:awuvyviarvbr5kd5ilqndpfsde

On-Device Object Detection for More Efficient and Privacy-Compliant Visual Perception in Context-Aware Systems

Ivan Rodriguez-Conde, Celso Campos, Florentino Fdez-Riverola
2021 Applied Sciences  
Ambient Intelligence (AmI) encompasses technological infrastructures capable of sensing data from environments and extracting high-level knowledge to detect or recognize users' features and actions, as  ...  Framed in that novel paradigm, this work presents a review of the recent advances made along those lines in object detection, providing a comprehensive study of the most relevant lightweight CNN-based  ...  framework, responsible for extracting from some given input images the different feature maps subsequently exploited by the deeper layers of the detector for predicting the several classes and bounding  ... 
doi:10.3390/app11199173 fatcat:cncucjelmrgv3mdortgxax2qly

Artificial Intelligence for Securing IoT Services in Edge Computing: A Survey

Zhanyang Xu, Wentao Liu, Jingwang Huang, Chenyi Yang, Jiawei Lu, Haozhe Tan
2020 Security and Communication Networks  
Next, the IoT service framework with EC is discussed. The survey of privacy preservation and blockchain for edge-enabled IoT services with AI is then presented.  ...  In the context that security and privacy preservation have become urgent issues for EC, great progress in artificial intelligence (AI) opens many possible windows to address the security challenges.  ...  Privacy partition is a privacy-preservation framework for deep neural networks, and the basic structure of the framework is made up of a bipartite topology network and an interactive adversarial network  ... 
doi:10.1155/2020/8872586 fatcat:wliclkyxufchpfrdw24fckwktq

Deep Learning-Based Security Behaviour Analysis in IoT Environments: A Survey

Yawei Yue, Shancang Li, Phil Legg, Fuzhong Li, Honghao Gao
2021 Security and Communication Networks  
Security and privacy have emerged as significant challenges for managing IoT.  ...  This paper aims to provide a thorough survey related to deep learning applications in IoT for security and privacy concerns. Our primary focus is on deep learning enhanced IoT security.  ...  In [27] , they turn the payload in the traffic packet into a hexadecimal format and visualize it into a 2D image. en, they employ a lightweight CNN framework called MobileNet to extract features from  ... 
doi:10.1155/2021/8873195 fatcat:oh4dcicpsfdcvmkfn5z2lgr2lm

2020 Index IEEE Internet of Things Journal Vol. 7

2020 IEEE Internet of Things Journal  
., Rateless-Code-Based Secure Cooperative Transmission Scheme for Industrial IoT; JIoT July 2020 6550-6565 Jamalipour, A., see Murali, S., JIoT Jan. 2020 379-388 James, L.A., see Wanasinghe, T.R.,  ...  ., +, JIoT April 2020 3602-3613 A Novel OFDM Autoencoder Featuring CNN-Based Channel Estimation for Internet of Vessels.  ...  ., +, JIoT Feb. 2020 1072-1080 A Novel OFDM Autoencoder Featuring CNN-Based Channel Estimation for Internet of Vessels.  ... 
doi:10.1109/jiot.2020.3046055 fatcat:wpyblbhkrbcyxpnajhiz5pj74a

Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing

Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, Junshan Zhang
2019 Proceedings of the IEEE  
We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge.  ...  To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge.  ...  [59] design Arden (privAte infeRence framework based on Deep nEural Networks), a framework that partitions the DNN model with a lightweight privacy-preserving mechanism.  ... 
doi:10.1109/jproc.2019.2918951 fatcat:d53vxmklgfazbmzjhsq3tuoama

Malware threat analysis techniques and approaches for IoT applications: a review

Chimeleze Collins Uchenna, Norziana Jamil, Roslan Ismail, Lam Kwok Yan, Mohamad Afendee Mohamed
2021 Bulletin of Electrical Engineering and Informatics  
This study gives a better understanding of the holistic approaches to malware threats in IoT applications and the way forward for strengthening the protection defense in IoT applications.  ...  Internet of things (IoT) is a concept that has been widely used to improve business efficiency and customer's experience.  ...  [52] proposed for future investigation a lightweight encryption to preserve the privacy of multimedia data within IoT environment.  ... 
doi:10.11591/eei.v10i3.2423 fatcat:tmkgezmv5ngcblgcxr6bmbqd3q

Edge Intelligence for Empowering IoT-based Healthcare Systems [article]

Vahideh Hayyolalam, Moayad Aloqaily, Oznur Ozkasap, Mohsen Guizani
2021 arXiv   pre-print
To meet the increasing demands on this critical infrastructure, there is a need for intelligent methods to cope with the existing obstacles in this area.  ...  Moreover, a novel smart healthcare model is proposed to boost the utilization of AI and edge technology in smart healthcare systems.  ...  They have proposed a novel multimodal data processing method on mobile edge computing environment using convolutional deep learning (CNN) and wavelet transform for extracting high order features and a  ... 
arXiv:2103.12144v1 fatcat:nfhrwejge5hhvhlk4l2fkynvoe

Edge Intelligence: Architectures, Challenges, and Applications [article]

Dianlei Xu, Tong Li, Yong Li, Xiang Su, Sasu Tarkoma, Tao Jiang, Jon Crowcroft, Pan Hui
2020 arXiv   pre-print
We then aim for a systematic classification of the state of the solutions by examining research results and observations for each of the four components and present a taxonomy that includes practical problems  ...  The aim of edge intelligence is to enhance the quality and speed of data processing and protect the privacy and security of the data.  ...  The authors further present a demo of the framework in [96] for continuous vision sensing applications on mobile devices.  ... 
arXiv:2003.12172v2 fatcat:xbrylsvb7bey5idirunacux6pe

Biometrics for Internet-of-Things Security: A Review

Wencheng Yang, Song Wang, Nor Masri Sahri, Nickson M. Karie, Mohiuddin Ahmed, Craig Valli
2021 Sensors  
With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking  ...  Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security  ...  In this scheme, features from raw ECG data are learned directly by the CNN without the need of manual feature extraction.  ... 
doi:10.3390/s21186163 pmid:34577370 fatcat:urk3rlktjbahvdc5evaanqjn3y

Federated Learning for Internet of Things: A Comprehensive Survey [article]

Dinh C. Nguyen, Ming Ding, Pubudu N. Pathirana, Aruna Seneviratne, Jun Li, H. Vincent Poor
2021 arXiv   pre-print
Particularly, we explore and analyze the potential of FL for enabling a wide range of IoT services, including IoT data sharing, data offloading and caching, attack detection, localization, mobile crowdsensing  ...  , and IoT privacy and security.  ...  To protect privacy of customers and improve the test accuracy, the scheme also enforces differential privacy on the extracted features for providing a new level of privacy for FL training.  ... 
arXiv:2104.07914v1 fatcat:b5wsrfcbynel7jqdxpfw4ftwh4
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