ACM Transactions on Sensor Networks最新文献_第7页

FedSuper: A Byzantine-Robust Federated Learning Under Supervision FedSuper:监督下的拜占庭鲁棒联邦学习

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-11-14 DOI: 10.1145/3630099

Ping Zhao, Jin Jiang, Guanglin Zhang

{"title":"FedSuper: A Byzantine-Robust Federated Learning Under Supervision","authors":"Ping Zhao, Jin Jiang, Guanglin Zhang","doi":"10.1145/3630099","DOIUrl":"https://doi.org/10.1145/3630099","url":null,"abstract":"Federated Learning (FL) is a machine learning setting where multiple worker devices collaboratively train a model under the orchestration of a central server, while keeping the training data local. However, owing to the lack of supervision on worker devices, FL is vulnerable to Byzantine attacks where the worker devices controlled by an adversary arbitrarily generate poisoned local models and send to FL server, ultimately degrading the utility (e.g., model accuracy) of the global model. Most of existing Byzantine-robust algorithms, however, cannot well react to the threatening Byzantine attacks when the ratio of compromised worker devices (i.e., Byzantine ratio) is over 0.5 and worker devices’ local training datasets are not independent and identically distributed (non-IID). We propose a novel Byzantine-robust Fed erated Learning under Super vision (FedSuper), which can maintain robustness against Byzantine attacks even in the threatening scenario with a very high Byzantine ratio (0.9 in our experiments) and the largest level of non-IID data (1.0 in our experiments) when the state-of-the-art Byzantine attacks are conducted. The main idea of FedSuper is that the FL server supervises worker devices via injecting a shadow dataset into their local training processes. Moreover, according to the local models’ accuracies or losses on the shadow dataset, we design a Local Model Filter to remove poisoned local models and output an optimal global model. Extensive experimental results on three real-world datasets demonstrate the effectiveness and the superior performance of FedSuper, compared to five latest Byzantine-robust FL algorithms and two baselines, in defending against two state-of-the-art Byzantine attacks with high Byzantine ratios and high levels of non-IID data.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"39 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134954082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LiteWiSys: A Lightweight System for WiFi-based Dual-task Action Perception LiteWiSys:基于wifi的轻量级双任务动作感知系统

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-11-10 DOI: 10.1145/3632177

Biyun Sheng, Jiabin Li, Linqing Gui, Zhengxin Guo, Fu Xiao

{"title":"LiteWiSys: A Lightweight System for WiFi-based Dual-task Action Perception","authors":"Biyun Sheng, Jiabin Li, Linqing Gui, Zhengxin Guo, Fu Xiao","doi":"10.1145/3632177","DOIUrl":"https://doi.org/10.1145/3632177","url":null,"abstract":"As two important contents in WiFi-based action perception, detection and recognition require localizing motion regions from the entire temporal sequences and classifying the corresponding categories. Existing approaches, though yielding reasonably acceptable performances, are suffering from two major drawbacks: heavy empirical dependency and large computational complexity. In order to solve the issues, we develop LiteWiSys in this paper, a lightweight system in an end-to-end deep learning manner to simultaneously detect and recognize WiFi-based human actions. Specifically, we assign different attentions on sub-carriers which are then compressed to reduce noises and information redundancy. Then, LiteWiSys integrates deep separable convolution and channel shuffle mechanism into a multi-scale convolutional backbone structure. By feature channel split, two network branches are obtained and further trained with a joint loss function for dual tasks. We collect different datasets at multi-scenes and conduct experiments to evaluate the performance of LiteWiSys. In comparison to existing WiFi sensing systems, LiteWiSys achieves a promising precision with a lower complexity.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"106 37","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135136522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GSAA: A Novel Graph Spatiotemporal Attention Algorithm for Smart City Traffic Prediction GSAA:一种新的智慧城市交通预测图时空关注算法

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-11-07 DOI: 10.1145/3631608

Jianmin Liu, Xiaoding Wang, Hui Lin, Feng Yu

{"title":"GSAA: A Novel Graph Spatiotemporal Attention Algorithm for Smart City Traffic Prediction","authors":"Jianmin Liu, Xiaoding Wang, Hui Lin, Feng Yu","doi":"10.1145/3631608","DOIUrl":"https://doi.org/10.1145/3631608","url":null,"abstract":"With the development of 5G and Internet of Things technologies, the application process of smart transportation in smart cities continues to advance. Sensors are a key source of information for smart transportation, and their data commonly includes complicated traffic scene information. Urban traffic scheduling and efficiency can be significantly increased by deploying data from smart sensors to forecast traffic flows. Despite the fact that some related works have focused on the prediction task of traffic flows, they have not completely mined the traffic spatiotemporal information present in smart sensor data. We offer a novel graph spatio-temporal attention algorithm (GSAA) for traffic prediction in this paper. To completely exploit the geographical and temporal correlations among complicated roadways for traffic forecast, the algorithm combines a spatiotemporal attention mechanism with a graph neural network.To take full advantage of how much effect various hyperparameters provide, deep reinforcement learning is used to obtain the optimal hyperparameters while the predictive model is trained. Experimental results on real-world public datasets show that the algorithm proposed in this paper achieves performance improvements of about 5.47% and 13.10% over the MAE (mean absolute error) than the best baseline strategies for short-term and long-term traffic forecasting, respectively.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"58 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135479622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Communication-Topology Preserving Motion Planning: Enabling Static Routing in UAV Networks 保持通信拓扑的运动规划:在无人机网络中实现静态路由

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-11-07 DOI: 10.1145/3631530

Ziyao Huang, Weiwei Wu, Chenchen Fu, Xiang Liu, Feng Shan, Jianping Wang, Xueyong Xu

{"title":"Communication-Topology Preserving Motion Planning: Enabling Static Routing in UAV Networks","authors":"Ziyao Huang, Weiwei Wu, Chenchen Fu, Xiang Liu, Feng Shan, Jianping Wang, Xueyong Xu","doi":"10.1145/3631530","DOIUrl":"https://doi.org/10.1145/3631530","url":null,"abstract":"Unmanned Aerial Vehicle ( UAV ) swarm offers extended coverage and is a vital solution for many applications. A key issue in UAV swarm control is to cover all targets while maintaining connectivity among UAVs, referred to as a multi-target coverage problem. With existing dynamic routing protocols, the flying ad hoc network suffers outdated and incorrect route information due to frequent topology changes. This might lead to failures of time-critical tasks. One mitigation solution is to keep the physical topology unchanged, thus maintaining a fixed communication topology and enabling static routing. However, keeping physical topology unchanged may sacrifice the coverage. In this paper, we propose to maintain a fixed communication topology among UAVs, which allows certain changes in physical topology, so that to maximize the coverage. We develop a distributed motion planning algorithm for the online multi-target coverage problem with the constraint of keeping communication topology intact. As the communication topology needs to be timely updated when UAVs leave or arrive at the swarm, we further design a topology-management protocol. Experimental results from the ns-3 simulator show that under our algorithms, UAV swarms of different sizes achieve significantly improved delay and loss ratio, efficient coverage, and rapid topology update.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"279 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135475098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Model Personalization-based Federated Learning Approach for Heterogeneous Participants with Variability in the Dataset 基于模型个性化的数据集可变性异构参与者联邦学习方法

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-11-06 DOI: 10.1145/3629978

Rahul Mishra, Hari Prabhat Gupta

{"title":"A Model Personalization-based Federated Learning Approach for Heterogeneous Participants with Variability in the Dataset","authors":"Rahul Mishra, Hari Prabhat Gupta","doi":"10.1145/3629978","DOIUrl":"https://doi.org/10.1145/3629978","url":null,"abstract":"Federated learning is an emerging paradigm that provides privacy-preserving collaboration among multiple participants for model training without sharing private data. The participants with heterogeneous devices and networking resources decelerate the training and aggregation. The dataset of the participant also possesses a high level of variability, which means the characteristics of the dataset change over time. Moreover, it is a prerequisite to preserve the personalized characteristics of the local dataset on each participant device to achieve better performance. This paper proposes a model personalization-based federated learning approach in the presence of variability in the local datasets. The approach involves participants with heterogeneous devices and networking resources. The central server initiates the approach and constructs a base model that executes on most participants. The approach simultaneously learns the personalized model and handles the variability in the datasets. We propose a knowledge distillation-based early-halting approach for devices where the base model does not fit directly. The early halting speeds up the training of the model. We also propose an aperiodic global update approach that helps participants to share their updated parameters aperiodically with server. Finally, we perform a real-world study to evaluate the performance of the approach and compare with state-of-the-art techniques.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"24 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135634527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep Compressed Sensing based Data Imputation for Urban Environmental Monitoring 基于深度压缩感知的城市环境监测数据输入

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-11-03 DOI: 10.1145/3599236

Qingyi Chang, Dan Tao, Jiangtao Wang, Ruipeng Gao

引用次数: 0

Non-Intrusive Human Vital Sign Detection using mmWave Sensing Technologies: A Review 毫米波传感技术的非侵入式人体生命体征检测综述

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-11-03 DOI: 10.1145/3627161

Yingxiao Wu, Haocheng Ni, Changlin Mao, Jianping Han, Wenyao Xu

{"title":"Non-Intrusive Human Vital Sign Detection using mmWave Sensing Technologies: A Review","authors":"Yingxiao Wu, Haocheng Ni, Changlin Mao, Jianping Han, Wenyao Xu","doi":"10.1145/3627161","DOIUrl":"https://doi.org/10.1145/3627161","url":null,"abstract":"Non-invasive human vital sign detection has gained significant attention in recent years, with its potential for contactless, long-term monitoring. Advances in radar systems have enabled non-contact detection of human vital signs, emerging as a crucial area of research. The movements of key human organs influence radar signal propagation, offering researchers the opportunity to detect vital signs by analyzing received electromagnetic (EM) signals. In this review, we provide a comprehensive overview of the current state-of-the-art in millimeter-wave (mmWave) sensing for vital sign detection. We explore human anatomy and various measurement methods, including contact and non-contact approaches, and summarize the principles of mmWave radar sensing. To demonstrate how EM signals can be harnessed for vital sign detection, we discuss four mmWave-based vital sign sensing (MVSS) signal models and elaborate on the signal processing chain for MVSS. Additionally, we present an extensive review of deep learning-based MVSS and compare existing studies. Finally, we offer insights into specific applications of MVSS (e.g., biometric authentication) and highlight future research trends in this domain.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"176 S423","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135775284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing AdaMEC:面向移动边缘计算的上下文自适应和动态组合DNN部署框架

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-10-30 DOI: 10.1145/3630098

BoWen Pang, Sicong Liu, Hongli Wang, Bin Guo, Yuzhan Wang, Hao Wang, Zhenli Sheng, Zhongyi Wang, Zhiwen Yu

{"title":"AdaMEC: Towards a Context-Adaptive and Dynamically-Combinable DNN Deployment Framework for Mobile Edge Computing","authors":"BoWen Pang, Sicong Liu, Hongli Wang, Bin Guo, Yuzhan Wang, Hao Wang, Zhenli Sheng, Zhongyi Wang, Zhiwen Yu","doi":"10.1145/3630098","DOIUrl":"https://doi.org/10.1145/3630098","url":null,"abstract":"With the rapid development of deep learning, recent research on intelligent and interactive mobile applications ( e.g. , health monitoring, speech recognition) has attracted extensive attention. And these applications necessitate the mobile edge computing scheme, i.e. , offloading partial computation from mobile devices to edge devices for inference acceleration and transmission load reduction. The current practices have relied on collaborative DNN partition and offloading to satisfy the predefined latency requirements, which is intractable to adapt to the dynamic deployment context at runtime. AdaMEC, a context-adaptive and dynamically-combinable DNN deployment framework is proposed to meet these requirements for mobile edge computing, which consists of three novel techniques. First, once-for-all DNN pre-partition divides DNN at the primitive operator level and stores partitioned modules into executable files, defined as pre-partitioned DNN atoms. Second, context-adaptive DNN atom combination and offloading introduces a graph-based decision algorithm to quickly search the suitable combination of atoms and adaptively make the offloading plan under dynamic deployment contexts. Third, runtime latency predictor provides timely latency feedback for DNN deployment considering both DNN configurations and dynamic contexts. Extensive experiments demonstrate that AdaMEC outperforms state-of-the-art baselines in terms of latency reduction by up to 62.14% and average memory saving by 55.21%.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"28 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136103809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ViST: A Ubiquitous Model with Multimodal Fusion for Crop Growth Prediction 基于多模态融合的作物生长预测泛在模型

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-10-28 DOI: 10.1145/3627707

Junsheng Li, Ling Wang, Jie Liu, Jinshan Tang

引用次数: 0

On Neuroevolution of Multi-Input Compositional Pattern Producing Networks: A Case of Entertainment Computing, Edge Devices, and Smart Cities 多输入合成模式产生网络的神经进化:以娱乐计算、边缘设备和智能城市为例

4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-10-23 DOI: 10.1145/3628430

Obaid Ullah, Habib Ullah Khan, Zahid Halim, Sajid Anwar, Muhammad Waqas

{"title":"On Neuroevolution of Multi-Input Compositional Pattern Producing Networks: A Case of Entertainment Computing, Edge Devices, and Smart Cities","authors":"Obaid Ullah, Habib Ullah Khan, Zahid Halim, Sajid Anwar, Muhammad Waqas","doi":"10.1145/3628430","DOIUrl":"https://doi.org/10.1145/3628430","url":null,"abstract":"This work presents a novel approach by utilizing Heterogeneous Activation Neural Networks (HA-NNs) to evolve the weights of Artificial Neural Networks (ANNs) for reinforcement learning in console and arcade computer games like Atari's Breakout and Sonic the Hedgehog. It is the first study to explore the potential of HA-NNs as potent ANNs in solving gaming-related reinforcement learning problems. Additionally, the proposed solution optimizes data transmission over networks for edge devices, marking a novel application of HA-NNs. The study achieved outstanding results, outperforming recent works in benchmark environments like CartPole-v1, Lunar Lander Continuous, and MountainCar-Continuous, with HA-NNs and ANNs evolved using the Neuroevolution of Augmenting Topologies (NEAT) algorithm. Notably, the key advancements include exceptional scores of 500 in CartPole-v1 and 98.2 in Mountain Car Continuous, demonstrating the efficacy of HA-NNs in reinforcement learning tasks. Beyond gaming, the research addresses the challenge of efficient data communication between edge devices, which has the potential to enhance performance in smart cities while reducing the load on edge devices and supporting seamless entertainment experiences with minimal commuting. This work pioneers the application of HA-NNs in reinforcement learning for computer games and introduces a novel approach for optimizing edge device communication, promising significant advancements in the fields of AI, neural networks, and smart city technologies.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"91 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135366631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0