101 research outputs found
Adaptive and Robust Fault-Tolerant Tracking Control of Contact force of Pantograph-Catenary for High-Speed Trains
Abstract This paper presents a modified multi-body dynamic model and a linear time-invariant model with actuator faults (loss of effectiveness faults, bias faults) and matched and unmatched uncertainties. Based on the fault model, a class of adaptive and robust tracking controllers are proposed which are adjusted online to tolerate the time-varying loss of effectiveness faults and bias faults, and compensate matched disturbances without the knowledge of bounds. For unmatched uncertainties, optimal control theory is added to the controller design processes. Simulations on a pantograph are shown to verify the efficiency of the proposed fault-tolerant design approach
Towards Secure Blockchain-enabled Internet of Vehicles: Optimizing Consensus Management Using Reputation and Contract Theory
In Internet of Vehicles (IoV), data sharing among vehicles is essential to
improve driving safety and enhance vehicular services. To ensure data sharing
security and traceability, highefficiency Delegated Proof-of-Stake consensus
scheme as a hard security solution is utilized to establish blockchain-enabled
IoV (BIoV). However, as miners are selected from miner candidates by
stake-based voting, it is difficult to defend against voting collusion between
the candidates and compromised high-stake vehicles, which introduces serious
security challenges to the BIoV. To address such challenges, we propose a soft
security enhancement solution including two stages: (i) miner selection and
(ii) block verification. In the first stage, a reputation-based voting scheme
for the blockchain is proposed to ensure secure miner selection. This scheme
evaluates candidates' reputation by using both historical interactions and
recommended opinions from other vehicles. The candidates with high reputation
are selected to be active miners and standby miners. In the second stage, to
prevent internal collusion among the active miners, a newly generated block is
further verified and audited by the standby miners. To incentivize the standby
miners to participate in block verification, we formulate interactions between
the active miners and the standby miners by using contract theory, which takes
block verification security and delay into consideration. Numerical results
based on a real-world dataset indicate that our schemes are secure and
efficient for data sharing in BIoV.Comment: 12 pages, submitted for possible journal publicatio
Reliable Federated Learning for Mobile Networks
Federated learning, as a promising machine learning approach, has emerged to
leverage a distributed personalized dataset from a number of nodes, e.g.,
mobile devices, to improve performance while simultaneously providing privacy
preservation for mobile users. In the federated learning, training data is
widely distributed and maintained on the mobile devices as workers. A central
aggregator updates a global model by collecting local updates from mobile
devices using their local training data to train the global model in each
iteration. However, unreliable data may be uploaded by the mobile devices
(i.e., workers), leading to frauds in tasks of federated learning. The workers
may perform unreliable updates intentionally, e.g., the data poisoning attack,
or unintentionally, e.g., low-quality data caused by energy constraints or
high-speed mobility. Therefore, finding out trusted and reliable workers in
federated learning tasks becomes critical. In this article, the concept of
reputation is introduced as a metric. Based on this metric, a reliable worker
selection scheme is proposed for federated learning tasks. Consortium
blockchain is leveraged as a decentralized approach for achieving efficient
reputation management of the workers without repudiation and tampering. By
numerical analysis, the proposed approach is demonstrated to improve the
reliability of federated learning tasks in mobile networks
AI-Generated Incentive Mechanism and Full-Duplex Semantic Communications for Information Sharing
The next generation of Internet services, such as Metaverse, rely on mixed
reality (MR) technology to provide immersive user experiences. However, the
limited computation power of MR headset-mounted devices (HMDs) hinders the
deployment of such services. Therefore, we propose an efficient information
sharing scheme based on full-duplex device-to-device (D2D) semantic
communications to address this issue. Our approach enables users to avoid heavy
and repetitive computational tasks, such as artificial intelligence-generated
content (AIGC) in the view images of all MR users. Specifically, a user can
transmit the generated content and semantic information extracted from their
view image to nearby users, who can then use this information to obtain the
spatial matching of computation results under their view images. We analyze the
performance of full-duplex D2D communications, including the achievable rate
and bit error probability, by using generalized small-scale fading models. To
facilitate semantic information sharing among users, we design a contract
theoretic AI-generated incentive mechanism. The proposed diffusion model
generates the optimal contract design, outperforming two deep reinforcement
learning algorithms, i.e., proximal policy optimization and soft actor-critic
algorithms. Our numerical analysis experiment proves the effectiveness of our
proposed methods. The code for this paper is available at
https://github.com/HongyangDu/SemSharingComment: Accepted by IEEE JSA
Vision-based Semantic Communications for Metaverse Services: A Contest Theoretic Approach
The popularity of Metaverse as an entertainment, social, and work platform
has led to a great need for seamless avatar integration in the virtual world.
In Metaverse, avatars must be updated and rendered to reflect users' behaviour.
Achieving real-time synchronization between the virtual bilocation and the user
is complex, placing high demands on the Metaverse Service Provider (MSP)'s
rendering resource allocation scheme. To tackle this issue, we propose a
semantic communication framework that leverages contest theory to model the
interactions between users and MSPs and determine optimal resource allocation
for each user. To reduce the consumption of network resources in wireless
transmission, we use the semantic communication technique to reduce the amount
of data to be transmitted. Under our simulation settings, the encoded semantic
data only contains 51 bytes of skeleton coordinates instead of the image size
of 8.243 megabytes. Moreover, we implement Deep Q-Network to optimize reward
settings for maximum performance and efficient resource allocation. With the
optimal reward setting, users are incentivized to select their respective
suitable uploading frequency, reducing down-sampling loss due to rendering
resource constraints by 66.076\% compared with the traditional average
distribution method. The framework provides a novel solution to resource
allocation for avatar association in VR environments, ensuring a smooth and
immersive experience for all users.Comment: 6 pages,7figure
Semantic Communications for Wireless Sensing: RIS-aided Encoding and Self-supervised Decoding
Semantic communications can reduce the resource consumption by transmitting
task-related semantic information extracted from source messages. However, when
the source messages are utilized for various tasks, e.g., wireless sensing data
for localization and activities detection, semantic communication technique is
difficult to be implemented because of the increased processing complexity. In
this paper, we propose the inverse semantic communications as a new paradigm.
Instead of extracting semantic information from messages, we aim to encode the
task-related source messages into a hyper-source message for data transmission
or storage. Following this paradigm, we design an inverse semantic-aware
wireless sensing framework with three algorithms for data sampling,
reconfigurable intelligent surface (RIS)-aided encoding, and self-supervised
decoding, respectively. Specifically, on the one hand, we propose a novel RIS
hardware design for encoding several signal spectrums into one MetaSpectrum. To
select the task-related signal spectrums for achieving efficient encoding, a
semantic hash sampling method is introduced. On the other hand, we propose a
self-supervised learning method for decoding the MetaSpectrums to obtain the
original signal spectrums. Using the sensing data collected from real-world, we
show that our framework can reduce the data volume by 95% compared to that
before encoding, without affecting the accomplishment of sensing tasks.
Moreover, compared with the typically used uniform sampling scheme, the
proposed semantic hash sampling scheme can achieve 67% lower mean squared error
in recovering the sensing parameters. In addition, experiment results
demonstrate that the amplitude response matrix of the RIS enables the
encryption of the sensing data
Adversarial Attacks and Defenses for Semantic Communication in Vehicular Metaverses
For vehicular metaverses, one of the ultimate user-centric goals is to
optimize the immersive experience and Quality of Service (QoS) for users on
board. Semantic Communication (SemCom) has been introduced as a revolutionary
paradigm that significantly eases communication resource pressure for vehicular
metaverse applications to achieve this goal. SemCom enables high-quality and
ultra-efficient vehicular communication, even with explosively increasing data
traffic among vehicles. In this article, we propose a hierarchical
SemCom-enabled vehicular metaverses framework consisting of the global
metaverse, local metaverses, SemCom module, and resource pool. The global and
local metaverses are brand-new concepts from the metaverse's distribution
standpoint. Considering the QoS of users, this article explores the potential
security vulnerabilities of the proposed framework. To that purpose, this study
highlights a specific security risk to the framework's SemCom module and offers
a viable defense solution, so encouraging community researchers to focus more
on vehicular metaverse security. Finally, we provide an overview of the open
issues of secure SemCom in the vehicular metaverses, notably pointing out
potential future research directions
Generative AI-aided Optimization for AI-Generated Content (AIGC) Services in Edge Networks
As Metaverse emerges as the next-generation Internet paradigm, the ability to
efficiently generate content is paramount. AI-Generated Content (AIGC) offers a
promising solution to this challenge. However, the training and deployment of
large AI models necessitate significant resources. To address this issue, we
introduce an AIGC-as-a-Service (AaaS) architecture, which deploys AIGC models
in wireless edge networks, ensuring ubiquitous access to AIGC services for
Metaverse users. Nonetheless, a key aspect of providing personalized user
experiences requires the careful selection of AIGC service providers (ASPs)
capable of effectively executing user tasks. This selection process is
complicated by environmental uncertainty and variability, a challenge not yet
addressed well in existing literature. Therefore, we first propose a diffusion
model-based AI-generated optimal decision (AGOD) algorithm, which can generate
the optimal ASP selection decisions. We then apply AGOD to deep reinforcement
learning (DRL), resulting in the Deep Diffusion Soft Actor-Critic (D2SAC)
algorithm, which achieves efficient and effective ASP selection. Our
comprehensive experiments demonstrate that D2SAC outperforms seven leading DRL
algorithms. Furthermore, the proposed AGOD algorithm has the potential for
extension to various optimization problems in wireless networks, positioning it
a promising approach for the future research on AIGC-driven services in
Metaverse. The implementation of our proposed method is available at:
https://github.com/Lizonghang/AGOD
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