2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)最新文献

{"title":"Poster: Distracted Driving Management","authors":"Seyhan Uçar, Hao Yang, K. Oguchi","doi":"10.1109/SEC54971.2022.00029","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00029","url":null,"abstract":"Today, modern vehicles can detect other nearby distracted drivers. The next step could be the management of distracted driving, in which a system guides innocent drivers around distracted drivers. The ego vehicle can share detected nearby distracted drivers with the remote server. The remote server tracks them and generates control suggestions (i.e., speed and lane change advisories) to keep innocent drivers away from distracted drivers. In this paper, we focus on that use case. We propose to generate control suggestions (e.g., speed and lane change advisories) for connected vehicles around inattentive drivers. Extensive simulations show that distracted driving management could decrease the collision risk by 78%.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133341672","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}

{"title":"Poster: EdgeShell - A language for composing edge applications","authors":"Kumseok Jung, Julien Gascon-Samson, K. Pattabiraman","doi":"10.1109/SEC54971.2022.00039","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00039","url":null,"abstract":"The edge computing ecosystem is young and diverse - there is a lack of a programming standard such as POSIX in operating systems and ECMAScript in the web. Developers today need to combine a variety of libraries and services to build a distributed edge application. As a result, the application becomes tightly coupled with the implementation choice made, such as the protocol chosen to exchange data or the storage server chosen to store data. This makes applications less reusable. We propose a domain-specific language called EdgeShell that can be used to compose edge applications in a manner similar to writing UNIX pipelines.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"49 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114032439","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}

{"title":"Demo: Human-Computable One-Time Passwords","authors":"Slawomir Matelski","doi":"10.1109/SEC54971.2022.00034","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00034","url":null,"abstract":"This demo shows an enhanced alternative to the Multi-Factor Authentication (MFA) methods. The improvement lies in the elimination of any supplementary gadgets/devices or theft-sensitive biometric data, by substituting it with direct human-computer authentication. This approach remains secure also in untrusted systems and environments. Despite the use of different identification factors by MFA methods, the basic condition for reliable authentication is the use of the intelligence of the human brain, in the form of a static password. For security reasons, it is recommended to use different passwords for each online account. As a result, users often adopt insecure password practices (e.g., reuse or weak password) or they have to frequently reset their passwords. We solved this problem in such a way that the user reconstructs each of his passwords, calculating the response to the public challenge according to his secret by performing simple mathematical operations, i.e. adding modulo 10. For each internet account, such a challenge must be stored on the server with the correct response as a hashed password, but only the user needs to know the secret, only one secret as a universal private key for all these accounts. This secret key is used by our innovative challenge-response protocol for human-generated One-Time Passwords (OTP) based on a hard lattice problem with noise introduced by our new method which we call Learning with Options (LWO). This secret has the form of an outline like a kind of handwritten autograph (Fig. 1), designed in invisible ink. The password generation process requires following such an invisible contour, similar to a manual autograph, and it can also be done offline on paper documents with an acceptable level of security and usability meeting the requirements for post-quantum symmetric cyphers and commercial implementation also in the field of IoT. Many attempts to achieve this goal have been made for over 30 years since Matsumoto's first publication in 1991, but only two protocols have been commercially implemented: strong but very slow HB, presented by Hopper and Blum in 2000 [2], and easy and fast but very weak grIDsure (GS) presented by Brostoff et al. in 2010 [3]. Our iChip scheme has security properties better than HB and usability close to GS, while eliminating their drawbacks.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125086465","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}

{"title":"FLiCR: A Fast and Lightweight LiDAR Point Cloud Compression Based on Lossy RI","authors":"Jin Heo, Christopher Phillips, Ada Gavrilovska","doi":"10.1109/SEC54971.2022.00012","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00012","url":null,"abstract":"Light detection and ranging (LiDAR) sensors are becoming available on modern mobile devices and provide a 3D sensing capability. This new capability is beneficial for perceptions in various use cases, but it is challenging for resource-constrained mobile devices to use the perceptions in real-time because of their high computational complexity. In this context, edge computing can be used to enable LiDAR online perceptions, but offloading the perceptions on the edge server requires a low-latency, lightweight, and efficient compression due to the large volume of LiDAR point clouds data. This paper presents FLiCR, a fast and lightweight LiDAR point cloud compression method for enabling edge-assisted online perceptions. FLiCR is based on range images (RI) as an intermediate representation (IR), and dictionary coding for compressing RIs. FLiCR achieves its benefits by leveraging lossy RIs, and we show the efficiency of bytestream compression is largely improved with quantization and subsampling. In addition, we identify the limitation of current quality metrics for presenting the entropy of a point cloud, and introduce a new metric that reflects both point-wise and entropy-wise qualities for lossy IRs. The evaluation results show FLiCR is more suitable for edge-assisted real-time perceptions than the existing LiDAR compressions, and we demonstrate the effectiveness of our compression and metric with the evaluations on 3D object detection and LiDAR SLAM.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114573251","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}

{"title":"Accelerating Privacy-Preserving Image Retrieval with Multi-Index Hashing","authors":"Jingnan Huang, Yuchuan Luo, Ming Xu, Shaojing Fu, Kai Huang","doi":"10.1109/SEC54971.2022.00075","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00075","url":null,"abstract":"With the explosive growth of data, a large amount of image data is stored on cloud servers. However, cloud servers can easily collect sensitive information about stored images, which brings serious privacy issues. Although uploading encrypted images to cloud servers could solve the privacy problem, most of the existing privacy-preserving schemes inevitably reduce the accuracy and efficiency of image retrieval. To address the above challenging issues, we propose a privacy-preserving content-based image retrieval scheme based on multi-indexed hashing (MIH) in this paper. To improve the retrieval precision, the ViT model is first used to extract feature descriptors of images and ITQ method is utilized to downscale the feature vectors into binary vectors. Subsequently, based on additive secret sharing, we propose a new secure Hamming distance calculation protocol to perform similarity measure, which protects the data privacy of image features. Finally, we design a secure multi-index hash structure to filter the dataset to improve the search efficiency. Experiments on the dataset demonstrate the efficiency and security of the scheme.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130016456","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}

{"title":"Poster: Ensemble Federated Edge Learning for Recommender Systems","authors":"Hui Sun, Yiru Chen, Kewei Sha, Yalong Wu","doi":"10.1109/SEC54971.2022.00035","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00035","url":null,"abstract":"Given the explosion of e-services, it has become critical for recommender systems (RSs) to have expected suggestions. Traditional machine learning-based recommending models provide an interface for platforms to find the most relevant items for users. Nonetheless, those models are often trained with user data from a single domain at centralized cloud, which hinders the performance of RSs, causes significant data transmission overhead, and may harm data privacy. To address these issues, in this poster, we propose an ensemble federated edge learning scheme (eFEEL) on the basis of a semi-distributed architecture design. eFEEL aims to efficiently and effectively improve RSs without breaching user data privacy.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131996796","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}

{"title":"Federated Learning Algorithms with Heterogeneous Data Distributions: An Empirical Evaluation","authors":"Alessio Mora, Davide Fantini, P. Bellavista","doi":"10.1109/SEC54971.2022.00049","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00049","url":null,"abstract":"Federated Learning (FL) is a paradigm that permits to learn a Deep Learning model without centralizing raw data, and has recently received growing interest primarily as a solution to improve privacy guarantees for end users while still distilling knowledge from a population of devices (e.g., edge devices or edge gateways managing a local set of visiting devices). However, the performance of FL algorithms significantly drops in presence of heterogeneous data distributions among the learners in the federation – this setting is very common in real practical applications, with clients holding data related to their habits, preferences, or environment. Several algorithms have been recently proposed to try to deal with data heterogeneity in FL settings under different assumptions and with differentiated pros/cons. In this article, we originally provide a review of the most relevant related solutions in the literature to alleviate the harmfulness of non-identically and independently distributed (IID) data, highlighting the intuition behind these alternative strategies as well as their possible drawbacks. Furthermore, we propose an empirical comparison among a subset of such state-of-the-art solutions under different levels of data hetero-geneity running them in the same operating conditions. We end up identifying the most promising approaches considering both empirical performances and defining characteristics (e.g., assumptions the strategy possibly make). The code is available online at https://github.com/alessiomora/fI_algorithms_non_iid.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130751934","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}

{"title":"Poster: Blockchain-Enabled Federated Edge Learning for Big Data Quality Assessment","authors":"Yalong Wu, Kewei Sha, K. Yue","doi":"10.1109/SEC54971.2022.00032","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00032","url":null,"abstract":"Data quality is essential to pricing big data and deciding its trading profit in digital market. Traditional machine learning-based data quality assessment methods support the valuation of data assets. Nonetheless, these methods require data to be sent over and assessed at centralized cloud, which incurs unprecedented data transmission cost and may jeopardize data privacy. To address these issues, in this poster, we propose a privacy-preserving big data quality assessment scheme (p2 QA) on the basis of blockchain and federated edge learning (FEEL). p2QA aims to notably reduce data transmission cost, accurately measure big data quality, and effectively prevent malicious parties from violating data privacy.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127966109","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}

{"title":"E2EdgeAI: Energy-Efficient Edge Computing for Deployment of Vision-Based DNNs on Autonomous Tiny Drones","authors":"Mozhgan Navardi, E. Humes, T. Mohsenin","doi":"10.1109/SEC54971.2022.00077","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00077","url":null,"abstract":"Artificial Intelligence (AI) and Deep Neural Networks (DNNs) have attracted attention as a solution within autonomous systems fields as they enable applications such as visual perception and navigation. Although cloud-based approaches have already been highly addressed, there is a growing interest in using both AI and DNNs on the edge as this allows for lower latency and avoids the potential security concerns of transmitting data to a remote server. However, deploying DNNs on edge devices is challenging due to the limited computational power available, as well as energy efficiency being of the utmost importance. In this work, we introduce an approach named E2EdgeAI for Energy-Efficient Edge computing that takes advantage of AI for autonomous tiny drones. This approach optimizes the energy efficiency of DNNs by considering the effects of memory access and core utilization on the energy consumption of tiny UAVs. To perform the experiment, we used a tiny drone named Crazyflie with the AI -deck expansion, which includes an octa-core RISC-V processor. The experimental results show the proposed approach reduces the model size by up to 14.4x, improves energy per inference by 78%, and increases energy efficiency by 5.6x. A recorded video for the proposed approach can be found here: Video.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116244430","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}

{"title":"Exact Memory- and Communication-aware Scheduling of DNNs on Pipelined Edge TPUs","authors":"Jiaqi Yin, Zhiru Zhang, Cunxi Yu","doi":"10.1109/SEC54971.2022.00023","DOIUrl":"https://doi.org/10.1109/SEC54971.2022.00023","url":null,"abstract":"Deep neural networks (DNNs) represent the state-of-the-art in many applications but have substantial computational and memory requirements, which greatly limit their training and deployment in real-world systems. In particular, the deployment challenges further increase on edge systems with much more restricted resource-constrained (e.g., computation and memory bounded), which recently attracted significant interest in many application scenarios. Such devices like Edge TPUs usually provide limited on-chip storage and memory bandwidth, where the heuristic-based ahead-of-time compilation techniques are highly limited in optimizing the inference performance due to the lacks of performance guarantees. This work proposes a novel exact pipeline scheduling framework that enables model parameter caching, data dependency, and device-to-device communication-aware multi-objective optimizations. The framework is powered by novel versatile SDC+ILP formulations supporting both propositional logic and non-equality constraints. The experimental results demonstrate that the proposed scheduling frameworks consistently outperform commercial Edge TPU Compiler with up to more than 4 x speedups on eleven ImageNet models in physical pipelined Edge TPU setups. In addition, we have demonstrated consistent real-world energy efficiency improvements measured with high precision power meter. Finally, the proposed framework has also demonstrated the capability in multi-model co-deployment on pipeline Edge TPU system, which is not supported by Edge TPU Compiler.","PeriodicalId":364062,"journal":{"name":"2022 IEEE/ACM 7th Symposium on Edge Computing (SEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129074524","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}