The 18th International Conference on Mobility, Sensing and Networking (MSN 2022)
Technical Sessions
Session T1S1
Radio Networks
Conference
4:30 PM
—
6:00 PM HKT
Local
Dec 14 Wed, 3:30 AM
—
5:00 AM EST
Human Occlusion in Ultra-wideband Ranging: What Can the Radio Do for You?
Vu Anh Minh Le, Matteo Trobinger, Davide Vecchia, Gian Pietro Picco
0
Applications of ultra-wideband (UWB) for distance estimation (ranging) and localization often involve users wearing tags. Unfortunately, the human body causes significant signal attenuation, reducing ranging accuracy. This specific case of non-line-of-sight (NLOS) condition has received little attention in the literature. Further, state-of-the-art techniques tackling generic NLOS are often based on machine learning, limiting their exploitation on embedded devices. We pursue an alternative approach and show that the features offered by the UWB transceiver, largely neglected by the literature, can be directly exploited to reliably detect human occlusions and optimize ranging accordingly. We base our findings on an extensive exper?imental campaign exploring many radio, system, and deployment dimensions in two environments, resulting in practical guidelines immediately available to the designers of UWB-based systems.
Deep Reinforcement Learning Based Radio Resource Selection Approach for C-V2X Mode 4 in Cooperative Perception Scenario
Chenhua Wei, Xiaojun Tan, Hui Zhang
0
In recent years, vehicles have been equipped with multiple sensors to enable assisted driving and even autonomous driving. However, due to the physical characteristics of the sensors, there are numerous shortcomings in the perception of the surrounding environment by a single vehicle. The development of vehicle-to-everything technology enables vehicles to extend their sensing range or enhance the reliability of perception by exchanging sensor data via vehicle-to-vehicle communication, which is called cooperative perception. In cellular vehicle-to?everything Mode 4, vehicles use the sensing-based semi-persistent scheduling scheme to select radio resource autonomously before transmission. But this scheme is hardly adaptable to cooperative perception scenario due to the time-sensitive of cooperative perception and the impact caused by the position of the per?ception information. In this paper, we modeled the cooperative perception scenario and the communication between vehicles, and then we formulated the optimization objective considering the characteristics of cooperative perception. Finally, we propose a multi-agent deep reinforcement learning based resource selection algorithm to tackle this problem and demonstrate its effectiveness through simulations.
A Quality-Aware Rendezvous Framework for Cognitive Radio Networks
Hai Liu, Lu Yu, Chung Keung Poon, Zhiyong Lin, Yiu-Wing Leung, Xiaowen Chu
0
In cognitive radio networks, rendezvous is a fundamental operation by which cognitive users establish communication links. Most of existing works were devoted to shortening the time-to-rendezvous (TTR) but paid little attention to qualities of the channels on which rendezvous is achieved. In fact, qualities of channels, such as resistance to primary users?€? activities, have a great effect on the rendezvous operation. If users achieve a rendezvous on a low-quality channel, the communication link is unstable and the communication performance is poor. In this case, re-rendezvous is required which results in considerable communication overhead and a large latency. In this paper, we first show that actual TTRs of existing rendezvous solu?tions increase by 65.40-104.38% if qualities of channels are not perfect. Then we propose a Quality-Aware Rendezvous Framework (QARF) that can be applied to any existing ren?dezvous algorithms to achieve rendezvous on high-quality channels. The basic idea of QARF is to expand the set of available channels by selectively duplicating high-quality channels. We prove that QARF can reduce the expected TTR of any rendezvous algorithm when the expanded ratio ?? is smaller than the threshold (???3 + q 1 + 4( ?? ?? ) 2)/2, where ?? and ??, respectively, are the mean and the standard deviation of qualities of channels. We further prove that QARF can always reduce the expected TTR of Random algorithm by a factor of 1+( ?? ?? ) 2 . Extensive experiments are conducted and the results show that QARF can significantly reduce the TTRs of the existing rendezvous algorithms by 10.50-51.05% when qualities of channels are taken into account.
Rendezvous Delay-Aware Multi-Hop Routing Protocol for Cognitive Radio Networks
Zengqi Zhang, Sheng Sun, Min Liu, Zhongcheng Li, Qiuping Zhang
0
In cognitive radio networks (CRNs), due to the external interference from primary users, secondary users (SUs) cannot reserve a common control channel (CCC). Hence, it is essential to consider the impact of channel rendezvous on the end-to-end delay in multi-hop CRNs. For this reason, we propose a High Probabilistic Transmission Efficiency Multi-hop Routing (HPTEMR) protocol without utilizing a CCC. In HPTEMR, we design an efficient waiting channel hopping sequence to achieve fast channel rendezvous between neighborhood SUs. We then propose a novel link metric, i.e., transmission efficiency, which characterizes the transmission distance and channel-rendezvous delay. Based on the link metric, a sender SU transmits data packets to the receiver SU with the highest probability that data packets can be forwarded to the destination SU with the shortest end-to-end delay. Evaluation results verify the effectiveness of HPTEMR and show its superiority in end-to-end delay and ratio of effective packets.
Session Chair
Pietro Tedeschi, Technology Innovation Institute, UAE
Session T2S1
Federated Learning and Edge Computing
Conference
4:30 PM
—
6:00 PM HKT
Local
Dec 14 Wed, 3:30 AM
—
5:00 AM EST
Fine-grained Cloud Edge Collaborative Dynamic Task Scheduling Based on DNN Layer-Partitioning
Xilong Wang, Xin Li, Ning Wang and Xiaolin Qin
0
Edge computing provides an opportunity to improve the quality of service (QoS) of Artificial Intelligence (AI) apps for the Internet of Things (IoTs) scenarios. It is an important way to improve the QoS of intelligent apps by deploying Deep Neural Network (DNN) models on edge nodes. Though the DNN execution time affects the QoS of apps significantly. Due to the limited and dynamic edge resources, and sudden load to edge nodes, it is hard to guarantee the DNN execution efficiency. In this paper, we conduct fine-grained decomposition of DNN tasks and propose a Cloud Edge Collaborative Dynamic Task Scheduling mechanism based on DNN layer-partitioning tech?nique. The approach can realize the collaborative computing of DNN models between cloud and edge, and improve the execution efficiency of DNN models, which guarantees the QoS of AI apps. Through simulation experiments, compared with the existing task scheduling mechanism and AI app deployment mode, we show that the proposed cloud edge collaborative dynamic task scheduling mechanism can effectively reduce the average service response time in the edge intelligent system, so as to improve the apps?€? overall QoS of the system. Meanwhile, the task scheduling mechanism designed in this paper makes it possible for more complex intelligent models to run in a resource-constrained edge environment.
Edge-assisted Federated Learning in Vehicular Networks
G. La Bruna, C. Risma Carletti, R. Rusca, C. Casetti, C. F. Chiasserini and M. Giordanino, R. Tola
0
Given the plethora of sensors with which vehicles are equipped, today?€?s automated vehicles already generate large amounts of data, and this is expected to increase in the case of autonomous vehicles, to enable data-driven solutions for vehicle control, safety and comfort, as well as to effectively implement convenience applications. It is expected that a crucial role in processing such data will be played by machine learning mod?els, which, however, require substantial computing and energy resources for their training. In this paper, we address the use of cooperative learning solutions to train a Neural Network (NN) model while keeping data local to each vehicle involved in the training process. In particular, we focus on Federated Learning (FL) and explore how this cooperative learning scheme can be applied in an urban scenario where several cars, supported by a server located at the edge of the network, collaborate to train a NN model. To this end, we consider an LSTM model for trajectory prediction ?€? a task that is an essential component of many safety and convenience vehicular applications, and investigate the performance of FL as the number of vehicles contributing to the learning process, and the data set they own, vary. To do so, we leverage realistic mobility traces of a large city and the FLOWER FL platform.
CFedPer: Clustered Federated Learning with Two-Stages Optimization for Personalization
Zhipeng Gao, Yan Yang, Chen Zhao, Zijia Mo
1
Federated learning(FL) is a privacy-preserving dis?tributed learning paradigm in which clients cooperate with each other to train a global model. It is becoming progressively prevalent with the rapid development of edge devices. A critical challenge in federated learning is the data heterogeneity among clients, resulting in the global model generated by standard federated learning being unable to be adapted to all clients. To tackle this problem, we propose the CFedPer for personalized FL, which generates a personalized model for each cluster after clustering to address the deficiency of standard federated learning. Our algorithm is organized into two optimization phases. The pre-start phase clusters clients by our proposed similarity-based clustering model using distribution vector and similarity matrix. In the in-training phase, we represent the neural network as the base layer and personalization layer and propose a novel optimization objective with a regularization term for the personalization layer to achieve a balance between per?sonalization and generalization, preventing over-personalization. Extensive experiments on various datasets and data distributions indicate that the performance of our algorithm is superior to the existing algorithms in terms of average local accuracy and variance among clients.
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
Minghui Li, Wei Wan, Jianrong Lu, Shengshan Hu, Junyu Shi, Leo Yu Zhang, Man Zhou, and Yifeng Zheng
0
Federated learning is a newly emerging distributed learning framework that facilitates the collaborative training of a shared global model among distributed participants with their privacy preserved. However, federated learning systems are vulnerable to Byzantine attacks from malicious partici?pants, who can upload carefully crafted local model updates to degrade the quality of the global model and even leave a backdoor. While this problem has received significant attention recently, current defensive schemes heavily rely on various assumptions, such as a fixed Byzantine model, availability of participants?€? local data, minority attackers, IID data distribu?tion, etc. To relax those constraints, this paper presents Robust-FL, the first prediction-based Byzantine-robust federated learning scheme where none of the assumptions is leveraged. The core idea of the Robust-FL is exploiting historical global model to construct an estimator based on which the local models will be filtered through similarity detection. We then cluster local models to adaptively adjust the acceptable differences between the local models and the estimator such that Byzantine users can be identified. Extensive experiments over different datasets show that our approach achieves the following advantages simultaneously: (i) independence of participants?€? local data, (ii) tolerance of majority attackers, (iii) generalization to variable Byzantine model.
Incremental Unsupervised Adversarial Domain Adaptation for Federated Learning in IoT Networks
Yan Huang, Mengxuan Du, Haifeng Zheng and Xinxin Feng
0
Federated learning, as an effective machine learning paradigm, can collaboratively training an efficient global model by exchanging the network parameters between edge nodes and the cloud server without sacrificing data privacy. Unfortunately, the obtained global model cannot generalize to newly collected unlabeled data since the unlabeled data collected by different edge devices are diverse. Furthermore, the distributions of collected labeled data and unlabeled data are also different for edge devices. In this paper, we propose a method named Incre?mental Unsupervised Adversarial Domain Adaptation (IUADA) for federated learning, which aims to reduce the domain shift between the labeled data and unlabeled data in the edge nodes and enhance the performance of the personalized target domain models based on the local unlabeled data. Finally, we evaluate the performance of the proposed method by using three real?world datasets. Extensive experimental results demonstrate that the proposed method is efficient to solve the problem of domain shift and achieves a better performance for unlabeled data for federated learning.
Session Chair
Anna Maria Vegni, Roma Tre University, Italy
Session T3S1
Privacy
Conference
4:30 PM
—
6:00 PM HKT
Local
Dec 14 Wed, 3:30 AM
—
5:00 AM EST
Approximate Shortest Distance Queries with Advanced Graph Analytics over Large-scale Encrypted Graphs
Yuchuan Luo, Dongsheng Wang, Shaojing Fu, Ming Xu, Yingwen Chen, Kai Huang
0
Understanding graph characteristics is of great importance for graph analytics. Among the many properties, shortest path distance is the fundamental and widely used one. With the advent of cloud computing, it is a natural choice for the data owners to host their massive graphs on the cloud and outsource the shortest distance querying service to it. However, the new paradigm brings serious security concerns as graph data and shortest distance queries may contain sensitive information of data owners and users. In this paper, we propose a novel scheme to support privacy?preserving approximate shortest distance queries with advanced graph analytics over large-scale encrypted graphs, which enables an untrusted cloud to answer shortest distance queries as well as advanced graph metrics (e.g., node centrality) without knowing the content of queries and the sensitive information of outsourced graphs. Compared with the state-of-the-art solutions, our design can support not only efficient and accurate shortest distance approximation, but also advanced graph analytics. We prove that our scheme is secure under the chosen-plaintext model. Experimental results over real-world datasets show that our scheme achieves high approximation accuracy with practical efficiency.
Tangless: Optimizing Cost and Transaction Rate in IOTA by Using Lyapunov Optimization Theory
Yinfeng Chen, Yu Guo, and Rongfang Bie
1
IOTA has emerged as a promising and feeless de?centralized computing paradigm for developing blockchain-based Internet of Things (IoT) applications with high-performance transaction rates and incremental scalability. To support micro?payments of IoT devices, IOTA has abandoned the original blockchain reward mechanism while IOTA nodes voluntarily contribute resources to maintain network stability. However, removing the mining rewards results in the resource cost of generating IOTA ledgers (known as the Tangle) being borne only by IOTA nodes. With the continuous expansion of the IOTA network, cost consumption is increasing. Thus, the inability to effectively reduce the cost of Tangle generation would lead to people being reluctant to dedicate resources to IOTA for maintaining the network robustness. In this paper, for the first time, we present a full-fledged transaction cost optimization scheme for IOTA, called Tangless, which can assist IOTA nodes in effectively reducing the Tangle generation cost while maintaining the strong robustness of the IOTA network. By using our proposed scheme, each IOTA node can effectively formulate the threshold of transaction approval rate in real time, maintaining the stability of the IOTA network with the optimal computational cost. We harness Lyapunov opti?mization theory to design a computational optimization algorithm for minimizing the total cost of nodes in IOTA. Then, we resort to large deviations theory to devise an optimized transaction rate control algorithm to further eliminate orphan Tangles that waste computational costs. Comprehensive theoretical analysis and sim?ulation experiments confirm the effectiveness and practicability of our proposed scheme.
Cloud-assisted Road Condition Monitoring with Privacy Protection in VANETs
Lemei Da, Yujue Wang, Yong Ding, Bo Qin, Xiaochun Zhou4, Hai Liang, Huiyong Wang
0
Vehicular ad hoc network (VANET) is one of the fastest developing technologies in intelligent transportation sys?tems (ITS), which has made great contributions to improving traffic congestion and reducing traffic accidents. As it is deployed in an open environment, security and privacy are threatened to a certain extent. Moreover, there are huge data exchanges in high traffic areas, which require VANET system to improve computing efficiency while ensuring communication security. To solve the above issues, this paper proposes a cloud-assisted road condition monitoring (RCM) system. The trusted authority (TA) monitors the road conditions with the help of the cloud server. The vehicle collects the road condition information of the road section managed by the roadside unit (RU), and only the vehicles authorized by the administrative roadside unit can successfully upload the road condition reports to the cloud server. The cloud server divides the road condition reports into different equivalence classes, in this way to report the emergency to the TA when the reported quantity exceeds the threshold. Security analysis showed that the proposed RCM system can effectively protect the security and privacy of road condition reports in VANETs.
Towards Event-driven Misbehavior Detection Mechanism in Social Internet of Vehicles
Chenchen Lv, Yue Cao, Lexi Xu, Shitao Zou, Yongdong Zhu and Zhili Sun
0
Due to inadequate management of Vehicular Ad hoc Networks (VANETs), malicious nodes could participate in communications along with misbehavior, e.g., dropping packets and spreading fake information. Therefore, it is essential to detect misbehavior of internal attackers that will cause network performance degradation (e.g., taking longer time to receive messages or reaching destinations with detours). Apart from the capture of dynamic network topology of VANETs, the social relationship among nodes can also be applied as a relatively stable metric to qualify nodes. This paper proposes a misbehavior detection mechanism based on social relationships, from which nodes determine trust for the receiver or transmitter. Based on the proposed mechanism, road traffic control applications can avoid the interference from malicious nodes. The construction of social relationships depends on the geographic information reflected by the movement of nodes, including contact frequency and trajectory similarity, since the geographic information can accurately indicate the relevance among nodes. In addition to the social relationship, the proposed mechanism also evaluates the data trust from time and spatial factors to reduce the interference of fake data. Finally, the proposed mechanism integrates data trust and social relationships to enable misbehavior detection decisions. Extensive results of simulations show that the proposed mechanism has outstanding malicious nodes detection rates under various proportions of malicious nodes and movement patterns.
RDP-WGAN: Image Data Privacy Protection based on R??nyi Differential Privacy
Xuebin Ma, Ren Yang and Maobo Zheng
0
In recent years, artificial intelligence technology based on image data has been widely used in various industries. Rational analysis and mining of image data can not only promote the development of the technology field but also become a new engine to drive economic development. However, the privacy leakage problem has become more and more serious. To solve the privacy leakage problem of image data, this paper proposes the RDP-WGAN privacy protection framework, which deploys the R??nyi differential privacy (RDP) protection techniques in the training process of generative adversarial networks to obtain a generative model with differential privacy. This generative model is used to generate an unlimited number of synthetic datasets to complete various data analysis tasks instead of sensitive datasets. Experimental results demonstrate that the RDP-WGAN privacy protection framework provides privacy protection for sensitive image datasets while ensuring the usefulness of the synthetic datasets.
Session Chair
Georgios Kavallieratos, Norwegian University of Science and Technology, Norwegian
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