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Distributed Intelligence in Wireless Networks

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arXiv 2022年

作者： Liu, Xiaolan Yu, Jiadong Liu, Yuanwei Gao, Yue Mahmoodi, Toktam Lambotharan, Sangarapillai Tsang, Danny H.K. The Institute for Digital Technologies Loughborough University LondonE20 3BS United Kingdom Guangdong Guangzhou511400 China The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The School of Computer Science Fudan University Shanghai200438 China The Department of Engineering King's College London LondonWC2R 2LS United Kingdom The Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University LoughboroughLE11 3TU United Kingdom The Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

The cloud-based solutions are becoming inefficient due to considerably large time delays, high power consumption, security and privacy concerns caused by billions of connected wireless devices and typically zillions bytes of data they produce at the network edge. A blend of edge computing and Artificial Intelligence (AI) techniques could optimally shift the resourceful computation servers closer to the network edge, which provides the support for advanced AI applications (e.g., video/audio surveillance and personal recommendation system) by enabling intelligent decision making on computing at the point of data generation as and when it is needed, and distributed Machine Learning (ML) with its potential to avoid the transmission of large dataset and possible compromise of privacy that may exist in cloud-based centralized learning. Therefore, AI is envisioned to become native and ubiquitous in future communication and networking systems. In this paper, we conduct a comprehensive overview of recent advances in distributed intelligence in wireless networks under the umbrella of native-AI wireless networks, with a focus on the basic concepts of native-AI wireless networks, on the AI-enabled edge computing, on the design of distributed learning architectures for heterogeneous networks, on the communication-efficient technologies to support distributed learning, and on the AI-empowered end-to-end communications. We highlight the advantages of hybrid distributed learning architectures compared to the state-of-art distributed learning techniques. We summarize the challenges of existing research contributions in distributed intelligence in wireless networks and identify the potential future opportunities. Copyright © 2022, The Authors. All rights reserved.

关键词： Edge computing

A robust controller for stable 3d pinching using tactile sensing

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arXiv 2021年

作者： Psomopoulou, Efi Pestell, Nicholas Papadopoulos, Fotios Lloyd, John Doulgeri, Zoe Lepora, Nathan F. Department of Engineering Mathematics Bristol Robotics Laboratory University of Bristol BristolBS8 1QU United Kingdom Shadow Robotics Company LondonNW5 1LP United Kingdom Department of Electrical and Computer Engineering Aristotle University of Thessaloniki Thessaloniki54124 Greece

This paper proposes a controller for stable grasping of unknown-shaped objects by two robotic fingers with tactile fingertips. The grasp is stabilised by rolling the fingertips on the contact surface and applying a desired grasping force to reach an equilibrium state. The validation is both in simulation and on a fully-actuated robot hand (the Shadow Modular Grasper) fitted with custom-built optical tactile sensors (based on the BRL TacTip). The controller requires the orientations of the contact surfaces, which are estimated by regressing a deep convolutional neural network over the tactile images. Overall, the grasp system is demonstrated to achieve stable equilibrium poses on various objects ranging in shape and softness, with the system being robust to perturbations and measurement errors. This approach also has promise to extend beyond grasping to stable in-hand object manipulation with multiple fingers. © 2021, CC BY.

关键词： Robotic arms

Scalable Authentication and Optimal Flooding in a Quantum Network

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PRX Quantum 2022年第2期3卷 020311-020311页

作者： Naomi R. Solomons Alasdair I. Fletcher Djeylan Aktas Natarajan Venkatachalam Sören Wengerowsky Martin Lončarić Sebastian P. Neumann Bo Liu Željko Samec Mario Stipčević Rupert Ursin Stefano Pirandola John G. Rarity Siddarth Koduru Joshi Quantum Engineering Technology Labs and Quantum Engineering Centre for Doctoral Training Centre for Nanoscience and Quantum Information University of Bristol Bristol BS8 1FD United Kingdom Department of Computer Science University of York York YO10 5GH United Kingdom Quantum Engineering Technology Labs H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering University of Bristol Bristol BS8 1UB United Kingdom Institute for Quantum Optics and Quantum Information—Vienna (IQOQI) and Vienna Center for Quantum Science and Technology (VCQ) 1090 Vienna Austria Institut de Ciencies Fotoniques (ICFO) Barcelona Institute of Science and Technology Castelldefels Barcelona 08860 Spain Photonics and Quantum Optics Research Unit Center of Excellence for Advanced Materials and Sensing Devices Ruđer Bošković Institute 10000 Zagreb Croatia College of Advanced Interdisciplinary Studies NUDT Changsha 410073 China

The global interest in quantum networks stems from the security guaranteed by the laws of physics. The deployment of quantum networks means facing the challenges of scaling up the physical hardware and, more importantly, of scaling up all other network layers and optimally utilizing network resources. Here, we consider two related protocols and their experimental demonstrations on an eight-user quantum network test bed, and discuss their usefulness with the aid of example use cases. First, we consider an authentication-transfer protocol to manage a fundamental limitation of quantum communication—the need for a preshared key between every pair of users linked together on the quantum network. By temporarily trusting some intermediary nodes for a short period of time (<35min in our network), we can generate and distribute these initial authentication keys with a very high level of security. Second, when end users quantify their trust in intermediary nodes, our flooding protocol can be used to improve both end-to-end communication speeds and increase security against malicious nodes.

关键词： Quantum communication Quantum cryptography Quantum networks

A novel fuzzy trust-based secure routing scheme in flying ad hoc networks

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Vehicular Communications 2023年 44卷

作者： Hosseinzadeh, Mehdi Mohammed, Adil Hussein Alenizi, Farhan A. Malik, Mazhar Hussain Yousefpoor, Efat Yousefpoor, Mohammad Sadegh Ahmed, Omed Hassan Rahmani, Amir Masoud Tightiz, Lilia Institute of Research and Development Duy Tan University Da Nang Viet Nam School of Medicine and Pharmacy Duy Tan University Da Nang Viet Nam Department of Communication and Computer Engineering Faculty of Engineering Cihan University-Erbil Kurdistan Region Iraq Electrical Engineering Department College of Engineering Prince Sattam Bin Abdulaziz University Al-Kharj 11942 Saudi Arabia School of Computing and Creative Technologies College of Arts Technology and Environment (CATE) University of the West of England Frenchay Campus Coldharbour Lane Bristol BS16 1QY United Kingdom Department of Computer Engineering Dezful Branch Islamic Azad University Dezful Iran Department of Information Technology University of Human Development Sulaymaniyah Iraq Future Technology Research Center National Yunlin University of Science and Technology Yunlin Taiwan School of Computing Gachon University 1342 Seongnamdaero Seongnam 13120 South Korea

Today, many studies assess vulnerabilities, threats, and attacks in flying ad hoc networks (FANETs) to provide solutions for countermeasures. Protecting FANETs against attackers and coordinating connections are challenging. The purpose of this study is to increase and maintain communication security. In this paper, a fuzzy trust-based secure routing scheme (FTSR) is presented in FANETs. FTSR utilizes two trust assessment mechanisms, namely local trust and path trust. Local trust strategy is a distributed process for finding reliable neighboring nodes and isolating hostile nodes on the network. In this regard, only reliable nodes are allowed to contribute to the path discovery procedure. This lowers the risk of forming fake paths in FANETs. Path trust strategy is responsible for identifying hostile nodes that are not identified in the local trust process. This strategy shows a general view of the trust status of the desired path. To design this mechanism, the source node runs a fuzzy system to select the safest path between source and the destination. Finally, network simulator 2 (NS2) implements FTSR, and the results such as malicious detection rate, packet delivery ratio, packet loss, accuracy, and delay are obtained from the simulation process. These results indicate that FTSR presents better performance compared to TOPCM, MNRiRIP, and MNDA. However, FTSR takes more time to find paths compared to TOPCM. © 2023 Elsevier Inc.

关键词： Artificial intelligence (AI) Cybersecurity Flying ad hoc networks (FANETs) Fuzzy logic Routing

Urban Digital Twins for Intelligent Road Inspection

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Urban Digital Twins for Intelligent Road Inspection

IEEE International Conference on Big Data

作者： Rui Fan Yikang Zhang Sicen Guo Jiahang Li Yi Feng Shuai Su Yanting Zhang Wenshuo Wang Yu Jiang Mohammud Junaid Bocus Xingyi Zhu Qijun Chen the Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous Systems and State Key Laboratory of Intelligent Autonomous Systems Robotics & Artificial Intelligence Laboratory (RAIL) Tongji University Shanghai P. R. China Department of Computer Science & Technology Donghua University Shanghai P. R. China Department of Civil Engineering McGill University Montreal Canada CTO office at ClearMotion Billerica MA USA Department of Electrical & Electronic Engineering the University of Bristol BS8 1UB U.K Key Laboratory of Road and Traffic Engineering of Ministry of Education Tongji University Shanghai P. R. China

ISBN: (纸本)9781665480468

Urban digital twin (UDT) technologies offer new opportunities for intelligent road inspection (IRI). This paper first reviews the state-of-the-art algorithms used in the two key components of UDT-based IRI systems: (1) multi-temporal, multi-dimension, multi-score, and heterogeneous road data acquisition, and (2) road distress detection. This paper then summarizes the UDTIRI competition, organized in conjunction with IEEE Bigdata 2022. More details on our competition are available at ***/view/udtiri-workshop/bigdata-2022.

关键词： Roads Data acquisition Inspection Big Data Rough surfaces Digital twins

Protein structured reservoir computing for spike-based pattern recognition

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arXiv 2020年

作者： Tsakalos, Karolos-Alexandros Sirakoulis, Georgios Ch. Adamatzky, Andrew Smith, Jim Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Unconventional Computing Laboratory University of the West of England BristolBS16 1QY United Kingdom Computer Science Research Centre University of the West of England Xanthi Greece

Nowadays we witness a miniaturisation trend in the semiconductor industry backed up by groundbreaking discoveries and designs in nanoscale characterisation and fabrication. To facilitate the trend and produce ever smaller, faster and cheaper computing devices, the size of nanoelectronic devices is now reaching the scale of atoms or molecules - a technical goal undoubtedly demanding for novel devices. Following the trend, we explore an unconventional route of implementing reservoir computing on a single protein molecule and introduce neuromorphic connectivity with a small-world networking property. We have chosen Izhikevich spiking neurons as elementary processors, corresponding to the atoms of verotoxin protein, and its molecule as a 'hardware' architecture of the communication networks connecting the processors. We apply on a single readout layer, various training methods in a supervised fashion to investigate whether the molecular structured Reservoir Computing (RC) system is capable to deal with machine learning benchmarks. We start with the Remote Supervised Method, based on Spike-Timing-Dependent-Plasticity, and carry on with linear regression and scaled conjugate gradient back-propagation training methods. The RC network is evaluated as a proof-of-concept on the handwritten digit images from the standard MNIST and the extended MNIST datasets and demonstrates acceptable classification accuracies in comparison with other similar approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Proteins

Satellite-Based Quantum Key Distribution in the Presence of Bypass Channels

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arXiv 2022年

作者： Ghalaii, Masoud Bahrani, Sima Liorni, Carlo Grasselli, Federico Kampermann, Hermann Wooltorton, Lewis Kumar, Rupesh Pirandola, Stefano Spiller, Timothy P. Ling, Alexander Huttner, Bruno Razavi, Mohsen School of Electronic and Electrical Engineering University of Leeds LeedsLS2 9JT United Kingdom Department of Computer Science University of York YorkYO10 5GH United Kingdom Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1UB United Kingdom Institute für Theoretische Physik III Heinrich Heine Universität DüsseldorfD-40225 Germany Quantum Engineering Centre for Doctoral Training H. H. Wills Physics Laboratory University of Bristol BristolBS8 1FD United Kingdom Department of Mathematics University of York Heslington YorkYO10 5DD United Kingdom Department of Physics University of York YorkYO10 5DD United Kingdom York Centre for Quantum Technologies University of York York United Kingdom Centre for Quantum Technologies National University of Singapore Singapore ID Quantique Geneva Switzerland

The security of prepare-and-measure satellite-based quantum key distribution (QKD), under restricted eavesdropping scenarios, is addressed. We particularly consider cases where the eavesdropper, Eve, has limited access to the transmitted signal by Alice, and/or Bob’s receiver station. This restriction is modeled by lossy channels between Alice/Bob and Eve, where the transmissivity of such channels can, in principle, be bounded by monitoring techniques. An artefact of such lossy channels is the possibility of having bypass channels, those which are not accessible to Eve, but may not necessarily be characterized by the users either. This creates interesting, unexplored, scenarios for analyzing QKD security. In this paper, we obtain generic bounds on the key rate in the presence of bypass channels and apply them to continuous-variable QKD protocols with Gaussian encoding with direct and reverse reconciliation. We find regimes of operation in which the above restrictions on Eve can considerably improve system performance. We also develop customised bounds for several protocols in the BB84 family and show that, in certain regimes, even the simple protocol of BB84 with weak coherent pulses is able to offer positive key rates at high channel losses, which would otherwise be impossible under an unrestricted Eve. In this case the limitation on Eve would allow Alice to send signals with larger intensities than the optimal value under an ideal Eve, which effectively reduces the effective channel loss. In all these cases, the part of the transmitted signal that does not reach Eve can play a non-trivial role in specifying the achievable key rate. Our work opens up new security frameworks for spaceborne quantum communications systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum cryptography

SleepTransformer: Automatic Sleep Staging with Interpretability and Uncertainty Quantification

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arXiv 2021年

作者： Phan, Huy Mikkelsen, Kaare Chén, Oliver Y. Koch, Philipp Mertins, Alfred de Vos, Maarten The School of Electronic Engineering and Computer Science Queen Mary University of London LondonE1 4NS United Kingdom The Alan Turing Institute LondonNW1 2DB United Kingdom The Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark The School of Economics Finance and Management University of Bristol BristolBS8 1TU United Kingdom The Institute for Signal Processing University of Lübeck Lübeck23562 Germany Lübeck23562 Germany The Department of Electrical Engineering KU Leuven Leuven3001 Belgium The Department of Development and Regeneration KU Leuven Leuven3001 Belgium

Background: Black-box skepticism is one of the main hindrances impeding deep-learning-based automatic sleep scoring from being used in clinical environments. Methods: Towards interpretability, this work proposes a sequence-to-sequence sleep-staging model, namely SleepTransformer. It is based on the transformer backbone and offers interpretability of the model’s decisions at both the epoch and sequence level. We further propose a simple yet efficient method to quantify uncertainty in the model’s decisions. The method, which is based on entropy, can serve as a metric for deferring low-confidence epochs to a human expert for further inspection. Results: Making sense of the transformer’s self-attention scores for interpretability, at the epoch level, the attention scores are encoded as a heat map to highlight sleep-relevant features captured from the input EEG signal. At the sequence level, the attention scores are visualized as the influence of different neighboring epochs in an input sequence (i.e. the context) to recognition of a target epoch, mimicking the way manual scoring is done by human experts. Conclusion: Additionally, we demonstrate that SleepTransformer performs on par with existing methods on two databases of different sizes. Significance: Equipped with interpretability and the ability of uncertainty quantification, SleepTransformer holds promise for being integrated into clinical settings. Copyright © 2021, The Authors. All rights reserved.

关键词： Deep neural networks

Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation

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arXiv 2020年

作者： Fan, Rui Wang, Hengli Cai, Peide Wu, Jin Bocus, Mohammud Junaid Qiao, Lei Liu, Ming Department of Computer Science and Engineering Department of Ophthalmology University of California San Diego San DiegoCA92093 United States Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong Department of Electrical and Electronic Engineering University of Bristol BS8 1UB United Kingdom State Key Laboratory of Ocean Engineering School of Naval Architecture Ocean and Civil Engineering Shanghai Jiao Tong University Shanghai200240 China

Collision-free space detection is a critical component of autonomous vehicle perception. The state-of-the-art algorithms are typically based on supervised deep learning. Their performance is dependent on the quality and amount of labeled training data. It remains an open challenge to train deep convolutional neural networks (DCNNs) using only a small quantity of training samples. Therefore, in this paper, we mainly explore an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available. Due to the fact that the pixels in collision-free space (generally regarded as a planar surface) between two images, captured from different views, can be associated using a homography matrix, the target image can be transformed into the reference view. This provides a simple but effective way to generate training data from additional multi-view images. Extensive experimental results, conducted with six state-of-the-art semantic segmentation DCNNs on three datasets, validate the effectiveness of the proposed method for enhancing collision-free space detection performance. When validated on the KITTI road benchmark, our approach provides the best results, compared with other state-of-the-art stereo vision-based collision-free space detection approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Deep neural networks