Tianxing Li, Jin Huang, Erik Risinger, Deepak Ganesan
While multi-modal deep learning is useful in distributed sensing tasks like human tracking, activity recognition, and audio and video analysis, deploying state-of-the-art multi-modal models in a wirelessly networked sensor system poses unique challenges. The data sizes for different modalities can be highly asymmetric (e.g., video vs. audio), and these differences can lead to significant delays between streams in the presence of wireless dynamics. Therefore, a slow stream can significantly slow down a multi-modal inference system in the cloud, leading to either increased latency (when blocked by the slow stream) or degradation in inference accuracy (if inference proceeds without waiting). In this paper, we introduce speculative inference on multi-modal data streams to adapt to these asymmetries across modalities. Rather than blocking inference until all sensor streams have arrived and been temporally aligned, we impute any missing, corrupt, or partially-available sensor data, then generate a speculative inference using the learned models and imputed data. A rollback module looks at the class output of speculative inference and determines whether the class is sufficiently robust to incomplete data to accept the result; if not, we roll back the inference and update the model's output. We implement the system in three multi-modal application scenarios using public datasets. The experimental results show that our system achieves 7 -- 128× latency speedup with the same accuracy as six state-of-the-art methods.
{"title":"Low-latency speculative inference on distributed multi-modal data streams","authors":"Tianxing Li, Jin Huang, Erik Risinger, Deepak Ganesan","doi":"10.1145/3458864.3467884","DOIUrl":"https://doi.org/10.1145/3458864.3467884","url":null,"abstract":"While multi-modal deep learning is useful in distributed sensing tasks like human tracking, activity recognition, and audio and video analysis, deploying state-of-the-art multi-modal models in a wirelessly networked sensor system poses unique challenges. The data sizes for different modalities can be highly asymmetric (e.g., video vs. audio), and these differences can lead to significant delays between streams in the presence of wireless dynamics. Therefore, a slow stream can significantly slow down a multi-modal inference system in the cloud, leading to either increased latency (when blocked by the slow stream) or degradation in inference accuracy (if inference proceeds without waiting). In this paper, we introduce speculative inference on multi-modal data streams to adapt to these asymmetries across modalities. Rather than blocking inference until all sensor streams have arrived and been temporally aligned, we impute any missing, corrupt, or partially-available sensor data, then generate a speculative inference using the learned models and imputed data. A rollback module looks at the class output of speculative inference and determines whether the class is sufficiently robust to incomplete data to accept the result; if not, we roll back the inference and update the model's output. We implement the system in three multi-modal application scenarios using public datasets. The experimental results show that our system achieves 7 -- 128× latency speedup with the same accuracy as six state-of-the-art methods.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"50 S5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132581441","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}
High-precision tracking of a pen-like instrument's movements is desirable in a wide range of fields spanning education, robotics, and art, to name a few. The key challenge in doing so stems from the impracticality of embedding electronics in the tip of such instruments (a pen, marker, scalpel, etc.) as well as the difficulties in instrumenting the surface that it works on. In this paper, we present ITrackU, a movement digitization system that does not require modifications to the surface or the tracked instrument's tip. ITrackU fuses locations obtained using ultra-wideband radios (UWB), with an inertial and magnetic unit (IMU) and a pressure sensor, yielding multidimensional improvements in accuracy, range, cost, and robustness, over existing works. ITrackU embeds a micro-transmitter at the base of a pen which creates a trackable beacon, that is localized from the corners of a writing surface. Fused with inertial motion sensor and a pressure sensor, ITrackU enables accurate tracking. Our prototype of ITrackU covers a large 2.5m × 2m area, while obtaining around 2.9mm median error. We demonstrate the accuracy of our system by drawing numerous shapes and characters on a whiteboard, and compare them against a touchscreen and a camera-based ground-truthing system. Finally, the produced stream of digitized data is minuscule in volume, when compared with a video of the whiteboard, which saves both network bandwidth and storage space.
{"title":"ITrackU","authors":"Yifeng Cao, Ashutosh Dhekne, M. Ammar","doi":"10.1145/3458864.3467885","DOIUrl":"https://doi.org/10.1145/3458864.3467885","url":null,"abstract":"High-precision tracking of a pen-like instrument's movements is desirable in a wide range of fields spanning education, robotics, and art, to name a few. The key challenge in doing so stems from the impracticality of embedding electronics in the tip of such instruments (a pen, marker, scalpel, etc.) as well as the difficulties in instrumenting the surface that it works on. In this paper, we present ITrackU, a movement digitization system that does not require modifications to the surface or the tracked instrument's tip. ITrackU fuses locations obtained using ultra-wideband radios (UWB), with an inertial and magnetic unit (IMU) and a pressure sensor, yielding multidimensional improvements in accuracy, range, cost, and robustness, over existing works. ITrackU embeds a micro-transmitter at the base of a pen which creates a trackable beacon, that is localized from the corners of a writing surface. Fused with inertial motion sensor and a pressure sensor, ITrackU enables accurate tracking. Our prototype of ITrackU covers a large 2.5m × 2m area, while obtaining around 2.9mm median error. We demonstrate the accuracy of our system by drawing numerous shapes and characters on a whiteboard, and compare them against a touchscreen and a camera-based ground-truthing system. Finally, the produced stream of digitized data is minuscule in volume, when compared with a video of the whiteboard, which saves both network bandwidth and storage space.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130174095","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}
Amelia Holcomb, Bill Tong, Megan Penny, Srinivasan Keshav
Tree trunk diameter, currently measured during manual forest inventories, is a key input to tree carbon storage calculations. We designan app running on a smartphone equipped with a time-of-flight sensor that allows efficient, low-cost, and accurate measurement of trunk diameter, even in the face of natural leaf and branch occlusion. The algorithm runs in near real-time on the phone, allowing user interaction to improve the quality of the results. We evaluate the app in realistic settings and find that in a corpus of 55 sample tree images, it estimates trunk diameter with mean error of 7.8%.
{"title":"Measuring forest carbon with mobile phones","authors":"Amelia Holcomb, Bill Tong, Megan Penny, Srinivasan Keshav","doi":"10.1145/3458864.3466916","DOIUrl":"https://doi.org/10.1145/3458864.3466916","url":null,"abstract":"Tree trunk diameter, currently measured during manual forest inventories, is a key input to tree carbon storage calculations. We designan app running on a smartphone equipped with a time-of-flight sensor that allows efficient, low-cost, and accurate measurement of trunk diameter, even in the face of natural leaf and branch occlusion. The algorithm runs in near real-time on the phone, allowing user interaction to improve the quality of the results. We evaluate the app in realistic settings and find that in a corpus of 55 sample tree images, it estimates trunk diameter with mean error of 7.8%.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"306 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115619006","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}
Chang Min Park, Donghwi Kim, D. Sidhwani, Andrew Fuchs, Arnob Paul, Sung-ju Lee, Karthik Dantu, Steven Y. Ko
We present Rushmore, a system that securely displays static or animated images using TrustZone. The core functionality of Rushmore is to securely decrypt and display encrypted images (sent by a trusted party) on a mobile device. Although previous approaches have shown that it is possible to securely display encrypted images using TrustZone, they exhibit a critical limitation that significantly hampers the applicability of using TrustZone for display security. The limitation is that, when the trusted domain of TrustZone (the secure world) takes control of the display, the untrusted domain (the normal world) cannot display anything simultaneously. This limitation comes from the fact that previous approaches give the secure world exclusive access to the display hardware to preserve security. With Rushmore, we overcome this limitation by leveraging a well-known, yet overlooked hardware feature called an IPU (Image Processing Unit) that provides multiple display channels. By partitioning these channels across the normal world and the secure world, we enable the two worlds to simultaneously display pixels on the screen without sacrificing security. Furthermore, we show that with the right type of cryptographic method, we can decrypt and display encrypted animated images at 30 FPS or higher for medium-to-small images and at around 30 FPS for large images. One notable cryptographic method we adapt for Rushmore is visual cryptography, and we demonstrate that it is a light-weight alternative to other cryptographic methods for certain use cases. Our evaluation shows that in addition to providing usable frame rates, Rushmore incurs less than 5% overhead to the applications running in the normal world.
{"title":"Rushmore","authors":"Chang Min Park, Donghwi Kim, D. Sidhwani, Andrew Fuchs, Arnob Paul, Sung-ju Lee, Karthik Dantu, Steven Y. Ko","doi":"10.1145/3458864.3467887","DOIUrl":"https://doi.org/10.1145/3458864.3467887","url":null,"abstract":"We present Rushmore, a system that securely displays static or animated images using TrustZone. The core functionality of Rushmore is to securely decrypt and display encrypted images (sent by a trusted party) on a mobile device. Although previous approaches have shown that it is possible to securely display encrypted images using TrustZone, they exhibit a critical limitation that significantly hampers the applicability of using TrustZone for display security. The limitation is that, when the trusted domain of TrustZone (the secure world) takes control of the display, the untrusted domain (the normal world) cannot display anything simultaneously. This limitation comes from the fact that previous approaches give the secure world exclusive access to the display hardware to preserve security. With Rushmore, we overcome this limitation by leveraging a well-known, yet overlooked hardware feature called an IPU (Image Processing Unit) that provides multiple display channels. By partitioning these channels across the normal world and the secure world, we enable the two worlds to simultaneously display pixels on the screen without sacrificing security. Furthermore, we show that with the right type of cryptographic method, we can decrypt and display encrypted animated images at 30 FPS or higher for medium-to-small images and at around 30 FPS for large images. One notable cryptographic method we adapt for Rushmore is visual cryptography, and we demonstrate that it is a light-weight alternative to other cryptographic methods for certain use cases. Our evaluation shows that in addition to providing usable frame rates, Rushmore incurs less than 5% overhead to the applications running in the normal world.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123221538","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}
By omitting external lighting, OLED display significantly reduces the power draw compared to its predecessor LCD and has gained wide adoption in modern smartphones. The real potential of OLED in saving phone battery drain lies in exploiting app UI color design, i.e., how to design app UI to use pixel colors that result in low OLED display power draw. In this paper, we design and implement an accurate per-frame OLED display power profiler, PFOP, that helps developers to gain insight into the impact of different app UI design on its OLED power draw, and an enhanced Android Battery that helps phone users to understand and manage phone display energy drain, for example, from different app and display configurations such as dark mode and screen brightness. A major challenge in designing both tools is to develop an accurate and robust OLED display power model. We experimentally show that linear-regression-based OLED power models developed in the past decade cannot capture the unique behavior of OLED display hardware in modern smartphones which have a large color space and propose a new piecewise power model that achieves much better modeling accuracy than the prior-art by applying linear regression in each small regions of the vast color space. Using the two tools, we performed to our knowledge the first power saving measurement of the emerging dark mode for a set of popular Google Android apps.
{"title":"How much battery does dark mode save?: an accurate OLED display power profiler for modern smartphones","authors":"Pranab Dash, Y. C. Hu","doi":"10.1145/3458864.3467682","DOIUrl":"https://doi.org/10.1145/3458864.3467682","url":null,"abstract":"By omitting external lighting, OLED display significantly reduces the power draw compared to its predecessor LCD and has gained wide adoption in modern smartphones. The real potential of OLED in saving phone battery drain lies in exploiting app UI color design, i.e., how to design app UI to use pixel colors that result in low OLED display power draw. In this paper, we design and implement an accurate per-frame OLED display power profiler, PFOP, that helps developers to gain insight into the impact of different app UI design on its OLED power draw, and an enhanced Android Battery that helps phone users to understand and manage phone display energy drain, for example, from different app and display configurations such as dark mode and screen brightness. A major challenge in designing both tools is to develop an accurate and robust OLED display power model. We experimentally show that linear-regression-based OLED power models developed in the past decade cannot capture the unique behavior of OLED display hardware in modern smartphones which have a large color space and propose a new piecewise power model that achieves much better modeling accuracy than the prior-art by applying linear regression in each small regions of the vast color space. Using the two tools, we performed to our knowledge the first power saving measurement of the emerging dark mode for a set of popular Google Android apps.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125344192","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}
Latency is a major issue towards practical use of augmented reality (AR) in mobile apps such as navigation and gaming. A string of work has appeared recently, proposing to offload a part of the AR-related processing pipeline to the edge [8]. One pitfall in these studies is the (simplified) assumption about the network delay. As a reality check and to gather insights to realize AR in real time, we seek in this work a better understanding of how a popular AR game, Pokémon Go, delivers its data in situ.
{"title":"AR game traffic characterization: a case of Pokémon Go in a flash crowd event","authors":"Hsi Chen, Ruey-Tzer Hsu, Ying-Chiao Chen, Wei-Chen Hsu, Polly Huang","doi":"10.1145/3458864.3466914","DOIUrl":"https://doi.org/10.1145/3458864.3466914","url":null,"abstract":"Latency is a major issue towards practical use of augmented reality (AR) in mobile apps such as navigation and gaming. A string of work has appeared recently, proposing to offload a part of the AR-related processing pipeline to the edge [8]. One pitfall in these studies is the (simplified) assumption about the network delay. As a reality check and to gather insights to realize AR in real time, we seek in this work a better understanding of how a popular AR game, Pokémon Go, delivers its data in situ.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124331425","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}
Federated Learning (FL) has recently received significant interests thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a similarity-aware federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that maximizes the training accuracy of multiple learned models while automatically capturing the intrinsic clustering relationship among the data of different nodes. Based on the learned cluster relationship, ClusterFL can efficiently drop out the nodes that converge slower or have little correlation with other nodes in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from total 145 users. The results show that, ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy, and save more than 50% communication overhead at the expense of negligible accuracy degradation.
{"title":"ClusterFL","authors":"Xiaomin Ouyang, Zhiyuan Xie, Jiayu Zhou, Jianwei Huang, Guoliang Xing","doi":"10.1145/3458864.3467681","DOIUrl":"https://doi.org/10.1145/3458864.3467681","url":null,"abstract":"Federated Learning (FL) has recently received significant interests thanks to its capability of protecting data privacy. However, existing FL paradigms yield unsatisfactory performance for a wide class of human activity recognition (HAR) applications since they are oblivious to the intrinsic relationship between data of different users. We propose ClusterFL, a similarity-aware federated learning system that can provide high model accuracy and low communication overhead for HAR applications. ClusterFL features a novel clustered multi-task federated learning framework that maximizes the training accuracy of multiple learned models while automatically capturing the intrinsic clustering relationship among the data of different nodes. Based on the learned cluster relationship, ClusterFL can efficiently drop out the nodes that converge slower or have little correlation with other nodes in each cluster, significantly speeding up the convergence while maintaining the accuracy performance. We evaluate the performance of ClusterFL on an NVIDIA edge testbed using four new HAR datasets collected from total 145 users. The results show that, ClusterFL outperforms several state-of-the-art FL paradigms in terms of overall accuracy, and save more than 50% communication overhead at the expense of negligible accuracy degradation.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"24 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128007010","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}
Smart earbuds are recognized as a new wearable platform for personal-scale human motion sensing. However, due to the interference from head movement or background noise, commonly-used modalities (e.g. accelerometer and microphone) fail to reliably detect both intense and light motions. To obviate this, we propose OESense, an acoustic-based in-ear system for general human motion sensing. The core idea behind OESense is the joint use of the occlusion effect (i.e., the enhancement of low-frequency components of bone-conducted sounds in an occluded ear canal) and inward-facing microphone, which naturally boosts the sensing signal and suppresses external interference. We prototype OESense as an earbud and evaluate its performance on three representative applications, i.e., step counting, activity recognition, and hand-to-face gesture interaction. With data collected from 31 subjects, we show that OESense achieves 99.3% step counting recall, 98.3% recognition recall for 5 activities, and 97.0% recall for five tapping gestures on human face, respectively. We also demonstrate that OESense is compatible with earbuds' fundamental functionalities (e.g. music playback and phone calls). In terms of energy, OESense consumes 746 mW during data recording and recognition and it has a response latency of 40.85 ms for gesture recognition. Our analysis indicates such overhead is acceptable and OESense is potential to be integrated into future earbuds.
{"title":"OESense: employing occlusion effect for in-ear human sensing","authors":"Dong Ma, Andrea Ferlini, C. Mascolo","doi":"10.1145/3458864.3467680","DOIUrl":"https://doi.org/10.1145/3458864.3467680","url":null,"abstract":"Smart earbuds are recognized as a new wearable platform for personal-scale human motion sensing. However, due to the interference from head movement or background noise, commonly-used modalities (e.g. accelerometer and microphone) fail to reliably detect both intense and light motions. To obviate this, we propose OESense, an acoustic-based in-ear system for general human motion sensing. The core idea behind OESense is the joint use of the occlusion effect (i.e., the enhancement of low-frequency components of bone-conducted sounds in an occluded ear canal) and inward-facing microphone, which naturally boosts the sensing signal and suppresses external interference. We prototype OESense as an earbud and evaluate its performance on three representative applications, i.e., step counting, activity recognition, and hand-to-face gesture interaction. With data collected from 31 subjects, we show that OESense achieves 99.3% step counting recall, 98.3% recognition recall for 5 activities, and 97.0% recall for five tapping gestures on human face, respectively. We also demonstrate that OESense is compatible with earbuds' fundamental functionalities (e.g. music playback and phone calls). In terms of energy, OESense consumes 746 mW during data recording and recognition and it has a response latency of 40.85 ms for gesture recognition. Our analysis indicates such overhead is acceptable and OESense is potential to be integrated into future earbuds.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132370823","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}
Mikhail Fomichev, Julia Hesse, Lars Almon, Timm Lippert, Jun Han, M. Hollick
With the advent of the Internet of Things (IoT), establishing a secure channel between smart devices becomes crucial. Recent research proposes zero-interaction pairing (ZIP), which enables pairing without user assistance by utilizing devices' physical context (e.g., ambient audio) to obtain a shared secret key. The state-of-the-art ZIP schemes suffer from three limitations: (1) prolonged pairing time (i.e., minutes or hours), (2) vulnerability to brute-force offline attacks on a shared key, and (3) susceptibility to attacks caused by predictable context (e.g., replay attack) because they rely on limited entropy of physical context to protect a shared key. We address these limitations, proposing FastZIP, a novel ZIP scheme that significantly reduces pairing time while preventing offline and predictable context attacks. In particular, we adapt a recently introduced Fuzzy Password-Authenticated Key Exchange (fPAKE) protocol and utilize sensor fusion, maximizing their advantages. We instantiate FastZIP for intra-car device pairing to demonstrate its feasibility and show how the design of FastZIP can be adapted to other ZIP use cases. We implement FastZIP and evaluate it by driving four cars for a total of 800 km. We achieve up to three times shorter pairing time compared to the state-of-the-art ZIP schemes while assuring robust security with adversarial error rates below 0.5%.
{"title":"FastZIP: faster and more secure zero-interaction pairing","authors":"Mikhail Fomichev, Julia Hesse, Lars Almon, Timm Lippert, Jun Han, M. Hollick","doi":"10.1145/3458864.3467883","DOIUrl":"https://doi.org/10.1145/3458864.3467883","url":null,"abstract":"With the advent of the Internet of Things (IoT), establishing a secure channel between smart devices becomes crucial. Recent research proposes zero-interaction pairing (ZIP), which enables pairing without user assistance by utilizing devices' physical context (e.g., ambient audio) to obtain a shared secret key. The state-of-the-art ZIP schemes suffer from three limitations: (1) prolonged pairing time (i.e., minutes or hours), (2) vulnerability to brute-force offline attacks on a shared key, and (3) susceptibility to attacks caused by predictable context (e.g., replay attack) because they rely on limited entropy of physical context to protect a shared key. We address these limitations, proposing FastZIP, a novel ZIP scheme that significantly reduces pairing time while preventing offline and predictable context attacks. In particular, we adapt a recently introduced Fuzzy Password-Authenticated Key Exchange (fPAKE) protocol and utilize sensor fusion, maximizing their advantages. We instantiate FastZIP for intra-car device pairing to demonstrate its feasibility and show how the design of FastZIP can be adapted to other ZIP use cases. We implement FastZIP and evaluate it by driving four cars for a total of 800 km. We achieve up to three times shorter pairing time compared to the state-of-the-art ZIP schemes while assuring robust security with adversarial error rates below 0.5%.","PeriodicalId":153361,"journal":{"name":"Proceedings of the 19th Annual International Conference on Mobile Systems, Applications, and Services","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127012740","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：