With the rapid growth in demand for indoor positioning, Wi-Fi Fine Time Measurement (FTM)-based positioning technology has gained significant attention due to its low cost and wide applicability. However, the positioning accuracy is significantly affected by the ranging errors in Wi-Fi. This paper proposes a comprehensive Wi-Fi FTM positioning framework to address this issue. Based on a systematic analysis of Wi-Fi error propagation mechanisms, correction and optimization solutions were developed through determining initial biases and modeling systematic ranging errors, predicting multipath interference via regression, and determining weights assisted by quality classification. Static and kinematic positioning experiments were conducted in an underground garage to validate the model’s generalization performance and applicability across various observation conditions. Results demonstrate that the distance model alone improved the ranging accuracy by 24.68%, and the regression model further enhanced it to 64.00%. By integrating correction and classification strategies, the proposed solution achieved a positioning accuracy of 0.80 m in general static environments and 1.20 m in complex environments. In kinematic experiments, the technique maintained optimal positioning accuracy under complex occlusion conditions, highlighting its robustness and practicality.

This paper examines the theoretical performance of distributed diffusion models in environments where computational resources and data availability vary significantly among workers. Traditional models centered on single-worker scenarios fall short in such distributed settings, particularly when some workers are resource-constrained. This discrepancy in resources and data diversity challenges the assumption of accurate score function estimation foundational to single-worker models. We establish the inaugural generation error bound for distributed diffusion models in resource-limited settings, establishing a linear relationship with the data dimension $d$ and consistency with established single-worker results. Our analysis highlights the critical role of hyperparameter selection in influencing the training dynamics, which are key to the performance of model generation. This study provides a streamlined theoretical approach to optimizing distributed diffusion models, paving the way for future research in this area.

This paper investigates decentralized unlearning, aiming to eliminate the impact of a specific client on the whole decentralized system. However, decentralized communication characterizations pose new challenges for effective unlearning: the indirect connections make it difficult to trace the specific client's impact, while the dynamic topology limits the scalability of retraining-based unlearning methods. In this paper, we propose the first **P**rovable **D**ecentralized **U**nlearning algorithm under **D**ynamic **T**opologies called PDUDT. It allows clients to eliminate the influence of a specific client without additional communication or retraining. We provide rigorous theoretical guarantees for PDUDT, showing it is statistically indistinguishable from perturbed retraining. Additionally, it achieves an efficient convergence rate of $\mathcal{O}(\frac{1}{T})$ in subsequent learning, where $T$ is the total communication rounds. This rate matches state-of-the-art results. Experimental results show that compared with the Retrain method, PDUDT saves more than 99\% of unlearning time while achieving comparable unlearning performance.

In this paper, we propose Byzantine-Robust Decentralized Federated Reinforcement Learning (BR-DeFedRL), an innovative framework that effectively combats the harmful influence of Byzantine agents by adaptively adjusting communication weights, thereby significantly enhancing the robustness of the learning system. By leveraging decentralized learning, our approach eliminates the dependence on a central server. Striking a harmonious balance between communication round count and sample complexity, BR-DeFedRL achieves efficient convergence with a rate of $\mathcal{O}\left( {\frac{1}{{TN}}} \right)$, where T denotes the communication rounds and N represents the local steps related to variance reduction. Notably, each agent attains an ϵ-approximation with a state-of-the-art sample complexity of $\mathcal{O}\left( {\frac{1}{{\varepsilon N}} + \frac{1}{\varepsilon }} \right)$. Extensive experimental validations further affirm the efficacy of BR-DeFedRL, making it a promising and practical solution for Byzantine-robust decentralized federated reinforcement learning.

The technology of autonomous vehicle (AV) is critical in nowadays Intelligent Transportation Systems. To achieve the fully automated operation for AVs, one important prerequisite is the accurate and reliable seamless localization covering complex outdoor-indoor scenarios. Although many solutions have been proposed to support AV localization, it is still challenging in achieving reliable drift-free positioning in seamless urban environments. With the current on-board sensors such as GNSS, IMU, LiDAR and cameras, it is difficult to achieve accurate drift-free indoor positioning due to the lack of GNSS indoors. Meanwhile, challenges remain in reliable navigation under obscured conditions. In this paper, we propose a tightly coupled integration algorithm of GNSS RTK, Ultra-Wide Band (UWB) and Visual Inertial Odometry (VIO) to enhance the accuracy and reliability for AVs seamless localization in challenging environments. The UWB technique is innovatively incorporated into the AVs navigation system to extend absolute positioning indoors. The stereo cameras are utilized to improve positioning continuity and enhance GNSS/UWB usability in outdoor-indoor obscured environments. The proposed algorithm is evaluated over real-world datasets in complex seamless environments. The results show that the proposed algorithm achieves 0.411m and 0.077m horizontal positioning accuracy in obscured outdoor and indoor environments, yielding 71.2% and 18.1% improvements compared with the traditional LC integration schemes, respectively.

In this paper, we propose De-RPOTA, a novel algorithm designed for decentralized learning, equipped with mechanisms for resource adaptation and privacy protection through over-the-air computation. We theoretically analyze the combined effects of limited resources and lossy communication on decentralized learning, showing it converges towards a contraction region defined by a scaled errors version. Remarkably, De-RPOTA achieves a convergence rate of$\mathcal {O}\left ({{\frac {1}{\sqrt {nT}}}}\right)$in scenarios devoid of errors, matching the state-of-the-arts. Additionally, we tackle a power control challenge, breaking it down into transmitter and receiver sub-problems to hasten the De-RPOTA algorithm’s convergence. We also offer a quantifiable privacy assurance for our over-the-air computation methodology. Intriguingly, our findings suggest that network noise can actually strengthen the privacy of aggregated information, with over-the-air computation providing extra security for individual updates. Comprehensive experimental validation confirms De-RPOTA’s efficacy in communication resources limited environments. Specifically, the results on the CIFAR-10 dataset reveal nearly 30% reduction in communication costs compared to the state-of-the-arts, all while maintaining similar levels of learning accuracy, even under resource restrictions.

In this paper, we propose a novel decentralized learning algorithm, namely DLLR-OA, for resource-constrained over-the-air computation with formal privacy guarantee. Theoretically, we characterize how the limited resources induced model-components selection error and compound communication errors jointly impact decentralized learning, making the iterates of DLLR-OA converge to a contraction region centered around a scaled version of the errors. In particular, the convergence rate of the DLLR-OA algorithm in the error-free case [EQUATION] achieves the state-of-the-arts. Besides, we formulate a power control problem and decouple it into two sub-problems of transmitter and receiver to accelerate the convergence of the DLLR-OA algorithm. Furthermore, we provide quantitative privacy guarantee for the proposed over-the-air computation approach. Interestingly, we show that network noise can indeed enhance privacy of aggregated updates while over-the-air computation can further protect individual updates. Finally, the extensive experiments demonstrate that DLLR-OA performs well in the communication resources constrained setting. In particular, numerical results on CIFAR-10 dataset shows nearly 30% communication cost reduction over state-of-the-art baselines with comparable learning accuracy even in resource constrained settings.

Edge intelligence is an emerging technology that enables artificial intelligence on connected systems and devices in close proximity to the data sources. Decentralized Collaborative Learning (DCL) is a novel edge intelligence technique that allows distributed clients to cooperatively train a global learning model without revealing their data. DCL has a wide range of applications in various domains, such as smart city and autonomous driving. However, DCL faces significant challenges in ensuring its trustworthiness, as data isolation and privacy issues make DCL systems vulnerable to adversarial attacks that aim to breach system confidentiality, undermine learning reliability or violate data privacy. Therefore, it is crucial to design DCL in a trustworthy manner, with a focus on security, robustness, and privacy. In this survey, we present a comprehensive review of existing efforts for designing trustworthy DCL systems from the three key aformentioned aspects: security, robustness, and privacy. We analyze the threats that affect the trustworthiness of DCL across different scenarios and assess specific technical solutions for achieving each aspect of Trustworthy DCL (TDCL). Finally, we highlight open challenges and future directions for advancing TDCL research and practice.

It is indispensable for a cloud platform to provide flexible elasticity service. However, the cloud users do not know whether elastic resource allocation of cloud platform matches their resource requirement or not, and the inappropriate purchase plan will have an impact on effect of elasticity service. Thus, a fine-grained and suitable purchase plan of cloud resources is inevitably needed. In this paper, we propose a fine-grained elasticity measurement method for cloud resources allocation towards upper-level cloud applications, called FEMCRA. It is a feasible measurement method from the perspective of testing cloud applications in advance to find a fine-grained and suitable elastic resource allocation scheme. We construct an integrated environment for simulating various elasticity level scenarios based on OpenStack, and data analysis application from CloudSuite is deployed to act as applications under testing. By executing that application under different elasticity rule-sets, which is a group of testing strategies that makes the cloud platform to be automatically scaled, we get the optimal elasticity level with least quantity of cloud resource, and the best purchase plan is correspondingly obtained which could save the expense for cloud users.

For IaaS users, how to buy cloud resources with the lowest spend is worth careful considering. Most IaaS providers utilize the elasticity feature in cloud computing to better provide resource allocation, however, IaaS users still know little about actual cloud resource allocation, so they have to apply for cloud resource very roughly to run their applications, which obviously costs more. In this paper, we address this issue from the perspective of cloud elasticity testing, that is, a more practical and better cloud resource purchase plan will be obtained in advance through elasticity testing of applications for IaaS users. We optimize the test generation towards existing elasticity testing by introducing genetic algorithm idea. Then evaluation metrics are designed to measure elasticity level of IaaS platform. After iterative and sufficient elasticity test executions, the best elasticity level for cloud applications is calculated from the test results to indicate better resource purchase plan. Test experiment results show that our method could help IaaS users to buy cloud resources more reasonable, which is much closer to the actual requirements of their applications and costs less.