2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)最新文献_第9页

Virtual Inertial Sensors with Fine Time Measurements 具有精细时间测量的虚拟惯性传感器

2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) Pub Date : 2020-12-01 DOI: 10.1109/MASS50613.2020.00085

Maurizio Rea, D. Giustiniano, Joerg Widmer

{"title":"Virtual Inertial Sensors with Fine Time Measurements","authors":"Maurizio Rea, D. Giustiniano, Joerg Widmer","doi":"10.1109/MASS50613.2020.00085","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00085","url":null,"abstract":"Inertial sensors embedded in mobile devices, such as accelerometers and gyroscopes, have shown great potential to study human motion. In this paper, we propose to estimate the device movement without any access to physical inertial sensors in the mobile. Our idea is to infer the movements of the mobile through radio measurements, a concept we call “virtual inertial sensors”. We propose a method for estimating the rotation of a user that uses only WiFi Fine Time Measurements (FTM) to infer the rotation speed. We evaluate and demonstrate the proposed approach with experiments, using commodity 802.11ac Access Point (AP)s for Channel State Information (CSI) and FTMs measurements, and a Google Pixel 3 smartphone as mobile terminal. While FTM works with only one single antenna, it achieves better performance than a CSI-based estimator that exploits four antennas and multiple sub-carriers at the AP, but is limited by the typical one single WiFi antenna at the smartphone side. Together with walking speed estimation of a user, we envision that virtual inertial sensors can be leveraged by location systems and sensing mechanisms, including 5G, to improve localization accuracy, infer user behavior, and design better and more secure communication.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123368098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Centralized active tracking of a Markov chain with unknown dynamics 未知动态马尔可夫链的集中主动跟踪

2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) Pub Date : 2020-10-30 DOI: 10.1109/MASS50613.2020.00021

Mrigank Raman, Ojal Kumar, Arpan Chattopadhyay

{"title":"Centralized active tracking of a Markov chain with unknown dynamics","authors":"Mrigank Raman, Ojal Kumar, Arpan Chattopadhyay","doi":"10.1109/MASS50613.2020.00021","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00021","url":null,"abstract":"In this paper, selection of an active sensor subset for tracking a discrete time, finite state Markov chain having an unknown transition probability matrix (TPM) is considered. A total of N sensors are available for making observations of the Markov chain, out of which a subset of sensors are activated each time in order to perform reliable estimation of the process. The trade-off is between activating more sensors to gather more observations for the remote estimation, and restricting sensor usage in order to save energy and bandwidth consumption. The problem is formulated as a constrained minimization problem, where the objective is the long-run averaged mean-squared error (MSE) in estimation, and the constraint is on sensor activation rate. A Lagrangian relaxation of the problem is solved by an artful blending of two tools: Gibbs sampling for MSE minimization and an on-line version of expectation maximization (EM) to estimate the unknown TPM. Finally, the Lagrange multiplier is updated using slower timescale stochastic approximation in order to satisfy the sensor activation rate constraint. The on-line EM algorithm, though adapted from literature, can estimate vector-valued parameters even under time-varying dimension of the sensor observations. Numerical results demonstrate approximately 1 dB better error performance than uniform sensor sampling and comparable error performance (within 2 dB bound) against complete sensor observation. This makes the proposed algorithm amenable to practical implementation.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"2021 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121863407","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Jointly Optimizing Sensing Pipelines for Multimodal Mixed Reality Interaction 面向多模态混合现实交互的传感管道联合优化

2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) Pub Date : 2020-10-13 DOI: 10.1109/MASS50613.2020.00046

Darshana Rathnayake, Ashen de Silva, Dasun Puwakdandawa, L. Meegahapola, Archan Misra, I. Perera

{"title":"Jointly Optimizing Sensing Pipelines for Multimodal Mixed Reality Interaction","authors":"Darshana Rathnayake, Ashen de Silva, Dasun Puwakdandawa, L. Meegahapola, Archan Misra, I. Perera","doi":"10.1109/MASS50613.2020.00046","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00046","url":null,"abstract":"Natural human interactions for Mixed Reality Applications are overwhelmingly multimodal: humans communicate intent and instructions via a combination of visual, aural and gestural cues. However, supporting low-latency and accurate comprehension of such multimodal instructions (MMI), on resource-constrained wearable devices, remains an open challenge, especially as the state-of-the-art comprehension techniques for each individual modality increasingly utilize complex Deep Neural Network models. We demonstrate the possibility of overcoming the core limitation of latency–vs.–accuracy tradeoff by exploiting cross-modal dependencies–i.e., by compensating for the inferior performance of one model with an increased accuracy of more complex model of a different modality. We present a sensor fusion architecture that performs MMI comprehension in a quasi-synchronous fashion, by fusing visual, speech and gestural input. The architecture is reconfigurable and supports dynamic modification of the complexity of the data processing pipeline for each individual modality in response to contextual changes. Using a representative “classroom” context and a set of four common interaction primitives, we then demonstrate how the choices between low and high complexity models for each individual modality are coupled. In particular, we show that (a) a judicious combination of low and high complexity models across modalities can offer a dramatic 3-fold decrease in comprehension latency together with an increase $sim$10-15% in accuracy, and (b) the right collective choice of models is context dependent, with the performance of some model combinations being significantly more sensitive to changes in scene context or choice of interaction.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121687673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

FLaPS: Federated Learning and Privately Scaling FLaPS:联邦学习和私有扩展

2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) Pub Date : 2020-09-13 DOI: 10.1109/MASS50613.2020.00011

Sudipta Paul, Poushali Sengupta, Subhankar Mishra

{"title":"FLaPS: Federated Learning and Privately Scaling","authors":"Sudipta Paul, Poushali Sengupta, Subhankar Mishra","doi":"10.1109/MASS50613.2020.00011","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00011","url":null,"abstract":"Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of transferring and aggregating the data centrally. In this way, sensitive data does not leave the user devices. FL uses the FedAvg algorithm, which is trained in the iterative model averaging way, on the non-iid and unbalanced distributed data, without depending on the data quantity. Some issues with the FL are, 1) no scalability, as the model is iteratively trained over all the devices, which amplifies with device drops; 2) security and privacy trade-off of the learning process still not robust enough and 3) overall communication efficiency and the cost are higher. To mitigate these challenges we present Federated Learning and Privately Scaling (FLaPS) architecture, which improves scalability as well as the security and privacy of the system. The devices are grouped into clusters which further gives better privacy scaled turn around time to finish a round of training. Therefore, even if a device gets dropped in the middle of training, the whole process can be started again after a definite amount of time. The data and model both are communicated using differentially private reports with iterative shuffling which provides a better privacy-utility trade-off. We evaluated FLaPS on MNIST, CIFAR10, and TINY-IMAGENET-200 dataset using various CNN models. Experimental results prove FLaPS to be an improved, time and privacy scaled environment having better and comparable after-learning-parameters with respect to the central and FL models.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"1996 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130960522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Dynamic Multi-level Privilege Control in Behavior-based Implicit Authentication Systems Leveraging Mobile Devices 利用移动设备的基于行为的隐式认证系统中的动态多级权限控制

2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS) Pub Date : 2018-08-02 DOI: 10.1109/MASS50613.2020.00037

Yingyuan Yang, Jinyuan Sun

{"title":"Dynamic Multi-level Privilege Control in Behavior-based Implicit Authentication Systems Leveraging Mobile Devices","authors":"Yingyuan Yang, Jinyuan Sun","doi":"10.1109/MASS50613.2020.00037","DOIUrl":"https://doi.org/10.1109/MASS50613.2020.00037","url":null,"abstract":"Implicit authentication (IA) is gaining popularity over recent years due to its use of user behavior as the main input, relieving users from explicit actions such as remembering and entering passwords. However, such convenience comes with a cost of authentication accuracy and delay which we propose to improve in this paper. Authentication accuracy deteriorates as users’ behaviors change as a result of mood, age, a change of routine, etc. Current authentication systems handle failed authentication attempts by locking the users out of their mobile devices. It is unsuitable for IA whose accuracy deterioration induces a high false reject rate, rendering the IA system unusable. Furthermore, existing IA systems leverage computationally expensive machine learning, which can introduce a large authentication delay. It is challenging to improve the authentication accuracy of these systems without sacrificing authentication delay. In this paper, we propose a multi-level privilege control (MPC) scheme that dynamically adjusts users’ access privilege based on their behavior change. MPC increases the system’s confidence in users’ legitimacy even when their behaviors deviate from historical data, thus improving authentication accuracy. It is a lightweight feature added to the existing IA schemes that helps avoid frequent and expensive retraining of machine learning models, thus improving authentication delay. We demonstrate that MPC increases authentication accuracy by 18.63% and reduces authentication delay by 7.02 minutes on average, using a public dataset that contains comprehensive user behavior data.","PeriodicalId":105795,"journal":{"name":"2020 IEEE 17th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130575951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3