ACM TRANSACTIONS ON SENSOR NETWORKS (TOSN) 是 ACM 在传感器网络跨学科领域的核心出版物，涵盖从信号处理、网络和协议、嵌入式系统、信息管理到分布式算法的广泛学科。它涵盖了引入新概念、技术、分析或架构的研究贡献，以及报告新工具和系统开发或具有高影响力、创新应用的经验和实验的应用贡献。这些交易特别关注对传感器网络系统方法的贡献以及基本贡献。

ACM Transactions on Sensor Networks (TOSN) is a key publication by ACM in the interdisciplinary field of sensor networks, covering a wide range of subjects from signal processing, networking and protocols, embedded systems, information management, to distributed algorithms. It accepts research contributions that introduce new concepts, technologies, analyses, or architectures, as well as reports on the development of new tools and systems, or empirical and experimental contributions with high impact and innovative applications. The journal particularly emphasizes contributions to sensor network system methodologies and foundational research.

《ACM Transactions on Sensor Networks》是ACM在传感器网络跨学科领域的重要出版物，涵盖信号处理、网络与协议、嵌入式系统、信息管理以及分布式算法等多个学科。期刊接受关于新概念、技术、分析或架构的研究贡献，以及新工具和系统开发的报告，或具有高影响力和创新应用的经验和实验贡献。特别关注对传感器网络系统方法的贡献及其基本研究。

ACM Transactions on Sensor Networks

Recently, as fitness has become a popular part of people’s lives, the intention to record fitness processes and assess the standards of fitness movements has grown increasingly keen. However, the existing approaches have some limitations, for example, wearable devices can hinder users’ fitness activities; computer vision–based solutions pose the risk of privacy breach, and so on. Fortunately, we observed that smartspeaker, acoustic-based sensing is a promising method of activity monitoring. In this article, we propose Afitness, an acoustic-based sensing system that enables non-intrusive, passive, and high-precision fitness detection. Afitness has the following three innovations. (i) We utilize pulse compression to generate high-precision motion distance images on commercial devices that can be visually recognized. (ii) We propose a data augmentation algorithm, which also incorporates transfer learning to greatly reduce the pressure of data collection. (iii) We exploit incremental learning techniques that allow Afitness to improve the portability of our system and recognize new actions. Overall, Afitness achieves acoustic signal interpretability and environmental reliability detection.

Penghao Wang

Ruobing Jiang

Zhongwen Guo

Chao Liu

Afitness: Fitness Monitoring on Smart Devices via Acoustic Motion Images

Afitness: 基于声学运动图像的智能设备健身监测

Deep learning–based video analytics demands high network bandwidth to ferry the large volume of data when deployed on the cloud. When incorporated at the edge side, only lightweight deep neural network (DNN) models are affordable due to computational constraint. In this article, a cloud–edge collaborative architecture is proposed combining edge-based inference with cloud-assisted continuous learning. Lightweight DNN models are maintained at the edge servers and continuously retrained with a more comprehensive model on the cloud to achieve high video analytics performance while reducing the amount of data transmitted between edge servers and the cloud. The proposed design faces the challenge of constraints of both computation resources at the edge servers and network bandwidth of the edge–cloud links. An accuracy gradient-based resource allocation algorithm is proposed to allocate the limited computation and network resources across different video streams to achieve the maximum overall performance. A prototype system is implemented and experiment results demonstrate the effectiveness of our system with up to 28.6% absolute mean average precision gain compared with alternative designs.

Ya Nan

Shiqi Jiang

Mo Li

Large-scale Video Analytics with Cloud–Edge Collaborative Continuous Learning

基于云边协同连续学习的大规模视频分析

In recent years, the rapid rise of Mixed Reality (MR) and Augmented Reality (AR) technologies has transformed digital interactions and immersive experiences, as evidenced by their widespread adoption in AR glasses, MR headsets, and mobile AR applications. However, integrating MR and AR into everyday life also presents unique problems, particularly concerning user interactions within limited visual fields and discomfort caused by prolonged arm elevation. To address these challenges, our research introduces an innovative input device utilizing common wearable device magnetometer sensors and a strong magnet as input controls. We employ innovative methods to eliminate environmental magnetic field and soft magnetic offset, achieving trajectory restoration with an error margin of 5.23 mm through machine learning and trajectory smoothing algorithms. This enables our input device to interact with MR and AR devices beyond the user’s visual range, expanding operational space, and enhancing usability.

Yipeng Liu

Feng Hong

Jiguo Yu

Feng Li

Mring: Contactless MR/AR Extended Input Method Based on Magnetic Signal

Mring：基于磁信号的无接触式MR/AR扩展输入法

With the evolution of wireless sensing technology, WiFi-based human identification has demonstrated tremendous potential in human-computer interaction and home security. However, most existing research operates in controlled environments, overlooking the complexities of real-world scenarios, such as signal fading, signal interference and uncertainty, and the diversity of application requirements. This paper presents a comprehensive analysis of a series of representative research articles on WiFi-based human identification and summarizes five major challenges in achieving high-precision identification in complex application scenarios. Non-line-of-sight (NLOS) user sensing, coexistence user sensing, dynamic group user sensing, cross-domain user sensing, and multi-task sensing. Additionally, this paper proposes a series of current solutions, including improving the signal-to-noise ratio (SNR) of NLOS sensing from the perspective of communication signals and sensing models, addressing coexistence sensing issues, designing adaptable tasks for dynamic user groups, leveraging techniques like adversarial learning and transfer learning for cross-domain problems, and employing modular deep learning architectures. By providing a comprehensive overview of WiFi-based human identification, this survey not only offers insights into current research but also charts a roadmap for future investigations. It is anticipated that this survey will stimulate innovative research endeavors and foster the expansion of wireless sensing technology across diverse application domains.

Zhongcheng Wei

Wei Chen

Shuli Ning

Weidong Lin

Nan Li

Bin Lian

Xiang Sun

Jijun Zhao

A Survey on WiFi-based Human Identification: Scenarios, Challenges, and Current Solutions

基于WiFi的人体识别研究综述：场景、挑战与当前解决方案

Face authentication (FA) schemes are widely adopted in smart homes nowadays. However, existing FA systems for smart appliances are commonly camera-based and hence experience performance degradation in poor illumination conditions. Mainstream FA systems based on radio frequency require dedicated hardware that is inaccessible to many appliances. In this paper, we propose an acoustic signals-based FA scheme that extracts acoustic signal features associated with facial 3D geometries to achieve FA named SoundFace . This scheme can be widely deployed on most appliances in home environments. We propose a novel two-stage locating approach based on acoustic sensing to capture the signal variation of the user’s face and separate the face region echoes from multipath interferences in the distance dimension. To obtain distinguishable facial features, we design a Convolutional Neural Network (CNN)-based feature extractor. In addition, the acoustic signal is highly susceptible to different changes in practical authentication. To overcome it, we utilize a transfer learning technique with little training overhead to enable SoundFace resilient to various authentication changes. Extensive evaluations demonstrate that SoundFace achieves an average true authentication rate of over 96.2% and an equal error rate of 4.2%, and it is robust to various real-world settings.

Huimin Chen

Chaojie Gu

Lilin Xu

Rui Tan

Shibo He

Jiming Chen

Listen to Your Face: A Face Authentication Scheme Based on Acoustic Signals

倾听你的脸：基于声信号的面部认证方案

Wireless Power Transfer (WPT) technology, which can charge IoT devices over the air, has become a promising technology for IoT networks. In wireless-powered IoT networks, broadcasting is a fundamental networking service for disseminating messages to the whole network. To seek a fast and collision-free broadcast schedule, the problem of Minimum Latency Broadcast Scheduling (MLBS) has been well studied when nodes are energy-abundant. However, in wireless-powered networks, a node can only receive or transmit packets after it has harvested enough energy. In such networks, it is of great importance to exploit the divergent harvested energy to reduce the broadcast latency. Unfortunately, existing works always assume a predetermined tree and a fixed transmission power for broadcast scheduling, which greatly limits their performance. Thus, in this paper, we investigate the first work for the MLBS problem in wireless-powered networks without relying on predetermined trees. Firstly, the problem is formulated and proved to be NP-hard. Then, two structure-adaptive scheduling algorithms are proposed with a theoretical bound, which can intertwine the construction of broadcast tree with the computation of an energy-aware schedule simultaneously. Furthermore, a power-aware scheduling method is also proposed to take the structure of the broadcast tree, the adjustment of nodes’ transmission powers, and the interference during transmissions into account simultaneously. Additionally, the algorithm for the MLBS problem under the physical interference model is also studied. Finally, the theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency.

Quan Chen

Zhipeng Cai

Jing Li

Ning Li

Lianglun Cheng

Hong Gao

Song Guo

Structure-Adaptive and Power-Aware Broadcast Scheduling for Multihop Wireless-Powered IoT Networks

多跳无线供电物联网网络的结构自适应与能效广播调度

Federated learning (FL) has become a prominent paradigm for collaborative model training while ensuring data privacy. However, in resource-constrained environments, such as the Internet of Things (IoT), FL faces a distinct challenge from Lazybone attackers, who compromise system performance by providing low-quality data or conducting minimal local training to reduce their computational burden. In this paper, we propose Fedeval, a novel multi-dimensional evaluation framework designed to defend against Lazybone attacks. Fedeval leverages a server-side base validation dataset and a base model to assess the quality and relevance of client contributions through gradient inversion, and it compares client-uploaded gradients with an honest baseline to detect training inconsistencies. By assigning adaptive importance scores based on client contributions, Fedeval enhances the robustness of FL by mitigating the impact of non-contributing participants. We also provide a theoretical analysis of Fedeval’s convergence properties and validate its effectiveness through extensive experiments on four datasets and two attack scenarios. Our results demonstrate that Fedeval significantly accelerates convergence and improves accuracy by up to 13% compared to traditional methods.

Hao Wang

Haoran Zhang

Lu Wang

Shichang Xuan

Qian Zhang

Fedeval: Defending Against Lazybone Attack via Multi-dimension Evaluation in Federated Learning

Fedeval：通过联邦学习中的多维度评估防御懒惰攻击

Abbreviation	ACM. Trans. Sens. Netw.
Journal Impact	3.81
Quartiles(Global)	TELECOMMUNICATIONS(Q2)

ISSN	1550-4859, 1550-4867
h-index	74

Publication Information	Publisher: Association for Computing Machinery (ACM)，Publishing cycle: Quarterly，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2005，Proportion of original research papers: 100.00%，Self Citation Rate:10.30%， Gold OA Rate: 0.49%
Average review cycle	网友分享经验：平均6.0个月
Average recruitment ratio	网友分享经验：容易

ACM Transactions on Sensor Networks

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Afitness: Fitness Monitoring on Smart Devices via Acoustic Motion Images

Large-scale Video Analytics with Cloud–Edge Collaborative Continuous Learning

Mring: Contactless MR/AR Extended Input Method Based on Magnetic Signal

A Survey on WiFi-based Human Identification: Scenarios, Challenges, and Current Solutions

Listen to Your Face: A Face Authentication Scheme Based on Acoustic Signals

Listen to Your Face: A Face Authentication Scheme Based on Acoustic Signals

A Survey on WiFi-based Human Identification: Scenarios, Challenges, and Current Solutions

Mring: Contactless MR/AR Extended Input Method Based on Magnetic Signal

Structure-Adaptive and Power-Aware Broadcast Scheduling for Multihop Wireless-Powered IoT Networks

Fedeval: Defending Against Lazybone Attack via Multi-dimension Evaluation in Federated Learning