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Experimental demonstration of an adaptive orthogonal frequency division multiplexing visible light communication system

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Chinese Optics Letters 2016年第11期14卷 23-27页

作者：郭心悦李鑫 Shanghai Key Lab of Modern Optical Systems School of Optical-Electrical and Computing EngineeringUniversity of Shanghai for Science and Technology

We propose and experimentally demonstrate a simple visible light communication system by combining an adaptive transmission technique with orthogonal frequency division *** the adaptive transmission scheme,power allocation is performed in such a way as to maximize the channel capacity under electric power constraints,and adaptive modulation is then introduced to achieve the maximum data rate given a target bit error *** results show that spectrum efficiency can be greatly improved through this adaptive transmission technique.

关键词： Modulation Free-space optical communication

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Efficient and available in-memory KV-store with hybrid erasure coding and replication 16

Efficient and available in-memory KV-store with hybrid erasu...

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Proceedings of the 14th Usenix Conference on File and Storage Technologies

作者： Heng Zhang Mingkai Dong Haibo Chen Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University

ISBN: (纸本)9781931971287

In-memory key/value store (KV-store) is a key building block for many systems like databases and large websites. Two key requirements for such systems are efficiency and availability, which demand a KV-store to continuously handle millions of requests per second. A common approach to availability is using replication such as primary-backup (PBR), which, however, requires M + 1 times memory to tolerate M failures. This renders scarce memory unable to handle useful user *** paper makes the first case of building highly available in-memory KV-store by integrating erasure coding to achieve memory efficiency, while not notably degrading performance. A main challenge is that an in-memory KV-store has much scattered metadata. A single KV put may cause excessive coding operations and parity updates due to numerous small updates to metadata. Our approach, namely Cocytus, addresses this challenge by using a hybrid scheme that leverages PBR for small-sized and scattered data (e.g., metadata and key), while only applying erasure coding to relatively large data (e.g., value). To mitigate well-known issues like lengthy recovery of erasure coding, Cocytus uses an online recovery scheme by leveraging the replicated metadata information to continuously serving KV requests. We have applied Cocytus to Memcached. Evaluation using YCSB with different KV configurations shows that Cocytus incurs low overhead for latency and throughput, can tolerate node failures with fast online recovery, yet saves 33% to 46% memory compared to PBR when tolerating two failures.

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When remote sensing data meet ubiquitous urban data: Fine-grained air quality inference

When remote sensing data meet ubiquitous urban data: Fine-gr...

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IEEE International Conference on Big Data

作者： Yanan Xu Yanmin Zhu Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9781467390064

With the growth of the economy, the air quality is becoming a serious issue, especially for those developing countries, such as China. Therefore, it is very important for the public and the government to access real-time air quality information. Unfortunately, the limited number of air quality monitoring stations is unable to provide fine-grained air quality information in a huge city, such as Beijing. One cost-effective approach for obtaining fine-grained air quality information is to infer air quality with those measured data at the monitoring stations. However, existing inference techniques have poor performance because of the extreme data sparsity problem (e.g., only 0.2% data are known). We observe that remote sensing has been a high-quality data source about urban dynamics. In this paper, we propose to integrate remote sensing data and ubiquitous urban data for air quality inference. There are two main challenges, i.e., data heterogeneity and incomplete remote sensing data. In response to the challenges, we propose a two-stage inference approach. In the first stage, we use the AOT remote sensing data and the meteorological data to infer the air quality values with an Artificial Neural Network (ANN). After this stage, we significantly reduce the percentage of empty cells in the tensor representing the spatio-temporal air quality values. In the second stage, we propose a tensor decomposition method to infer the complete set of air quality values. We use the spatial features (i.e., road features and POI features) and the temporal features (i.e., meteorological features) as the constraints in the tensor decomposition process. Experiments with real data sets show that our approach has profound performance advantage over the state-of-the-art methods, such as U-Air.

关键词： Air quality Remote sensing Monitoring Tensile stress Sensors Urban areas Pollution measurement

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A Hybrid Approach Based on Collaborative Filtering to Recommending Mobile Apps

A Hybrid Approach Based on Collaborative Filtering to Recomm...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Xia Wu Yanmin Zhu Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Shanghai Key Lab of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9781509053827

With the rapid emergence of mobile devices, smart phones have penetrated into every aspect of people's daily life. The explosive growth of mobile applications makes it difficult for mobile users to find suitable and interesting applications. Mobile app recommendation has been explored by many researchers and some industry solutions are proposed for mobile users. Collaborative filter (CF) is a popular technique in recommendation system, but it requires explicit feedback data like ratings, which are usually difficult to be collected. Many application markets provide keywords search functions and recommend applications with high download counts. However, downloading an application is a vague indicator of whether the user truly likes that application, as the user may probably uninstall that application immediately after it has been installed. Some other industry solutions involve users' personal data such as social networking, which may result in privacy leaks. In this paper, we propose two hybrid models based on collaborative filtering to make mobile app recommendations. We leverage RFD (Recency, Frequency, Duration) model to label the preference for users over applications from users' usage data. The first model, namely, improved item-oriented collaborative filtering (IIOCF), improves the performance of the item-oriented approach by leveraging the latent factor model to discover latent factors among applications. The second model (HLF) is a hybrid model of the latent factor and item-oriented approach. This model sums the predictions of the latent factor model and item-oriented approach, thereby capturing the advantages of both approaches. Our experiment results over 6,568 applications and 25,302 users clearly show that HLF model has better performance than both the item-oriented and latent factor approach.

关键词： Mobile communication Collaboration Sparse matrices Computational modeling Industries Frequency measurement Filtering

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A strategy-proof combinatorial heterogeneous channel auction framework in noncooperative wireless networks

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IEEE Transactions on Mobile computing 2015年第6期14卷 1123-1137页

作者： Zheng, Zhenzhe Wu, Fan Chen, Guihai Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China

Auction is believed to be an effective way to solve or relieve the problem of radio spectrum shortage, by dynamically redistributing idle wireless channels of primary users to secondary users. However, to design a practical channel auction mechanism, we have to consider five challenges, including strategy-proofness, channel spatial reusability, channel heterogeneity, bid diversity, and social welfare maximization. Unfortunately, none of the existing works fully considered the five design challenges. In this paper, we present the first in-depth study on the problem of dynamic channel redistribution jointly considering the five design challenges, and present SMASHER, which is a family of Strategy-proof coMbinatorial A uction mechaniSms for HEterogeneous channel R edistribution. SMASHER contains two strategy-proof auction mechanisms, namely SMASHER-AP and SMASHER-GR. SMASHER-AP is a strategy-proof, approximately efficient combinatorial auction mechanism for indivisible channel redistribution. We further consider the case, in which channels can be shared by the users in a paradigm of time-division multiplexing and propose SMASHER-GR, which is a strategy-proof channel allocation and scheduling mechanism. We have extensively evaluated our designs. The evaluation results show that our designs achieve much better performance than existing works. © 2014 IEEE.

关键词： Wireless networks

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STAR: Strategy-Proof Double Auctions for Multi-Cloud, Multi-Tenant Bandwidth Reservation

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IEEE Transactions on Computers 2015年第7期64卷 2071-2083页

作者： Zheng, Zhenzhe Gui, Yang Wu, Fan Chen, Guihai Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China

Bandwidth reservation has been recognized as a value-added service to the cloud provider in recent years. We consider an open market of cloud bandwidth reservation, in which cloud providers offer bandwidth reservation services to cloud tenants, especially online streaming service providers, who have strict requirements on the amount of bandwidth to guarantee their quality of services. In this paper, we model the open market as a double-sided auction, and propose the first family of STrategy-proof double Auctions for multi-cloud, multi-tenant bandwidth Reservation (STAR). STAR contains two auction mechanisms. The first one, STAR-Grouping, divides the tenants into groups by a bid-independent way, and carefully matches the cloud providers with the tenant groups to form good trades. The second one, STAR-Padding, greedily matches the cloud providers with the tenants, and fills the partially reserved cloud provider(s) with a novel virtual padding tenant who can be a component of the auctioneer. Our analysis shows that both of the two auction mechanisms achieve strategy-proofness and ex-post budget balance. Our evaluation results show that they achieve good performance in terms of social welfare, cloud bandwidth utilization, and tenant satisfaction ratio. © 1968-2012 IEEE.

关键词： Bandwidth

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Mitigating sync amplification for copy-on-write virtual disk 16

Mitigating sync amplification for copy-on-write virtual disk

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Proceedings of the 14th Usenix Conference on File and Storage Technologies

作者： Qingshu Chen Liang Liang Yubin Xia Haibo Chen Hyunsoo Kim Shanghai Key Laboratory of Scalable Computing and Systems Institute of Parallel and Distributed Systems Shanghai Jiao Tong University Samsung Electronics Co. Ltd.

ISBN: (纸本)9781931971287

Copy-on-write virtual disks (e.g., qcow2 images) provide many useful features like snapshot, de-duplication, and full-disk encryption. However, our study uncovers that they introduce additional metadata for block organization and notably more disk sync operations (e.g., more than 3X for qcow2 and 4X for VMDK images). To mitigate such sync amplification, we propose three optimizations, namely per virtual disk internal journaling, dual-mode journaling, and adaptive-preallocation, which eliminate the extra sync operations while preserving those features in a consistent way. Our evaluation shows that the three optimizations result in up to 110% performance speedup for varmail and 50% for TPCC.

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Strategy-Proof Data Auctions with Negative Externalities 16

Strategy-Proof Data Auctions with Negative Externalities

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International Conference on Autonomous Agents and Multiagent systems

作者： Xiang Wang Zhenzhe Zheng Fan Wu Xiaoju Dong Shaojie Tang Guihai Chen Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Department of Information Systems University of Texas at Dallas

ISBN: (纸本)9781510855083

Data has appeared to be a new kind of commodity with distinctive characteristics, which make it fundamentally different from physical goods as well as traditional digital goods. Therefore, new trading mechanisms for data need to be designed. In this paper, we model the data market as an auction with negative externalities, and design practical mechanisms for data trading. Specifically, we propose a family of Data auctIons in CompetiTive mArkets, namely DICTA. DICTA contains two mechanisms, including DICTA-FUL and DICTA-PAR. DICTA-FUL is a direct revelation auction mechanism in full competition markets, achieving strategy-proofness and optimal social welfare. In the partial competition markets, we show that the allocation problem is NP-hard. Therefore, we present an approximation algorithm for winner determination. By carefully integrating this approximation allocation algorithm and a charging scheme, DICTA-PAR achieves both strategy-proofness and d-approximation, where d is the maximum degree of the underlying undirected graph of the competition graph.

关键词： Algorithms Theory Economics Data Market Auction Externality externality economics algorithms Approximation algorithms Markets undirected graph cargo theories

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Privacy and quality preserving multimedia data aggregation for participatory sensing systems

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IEEE Transactions on Mobile computing 2015年第6期14卷 1287-1300页

作者： Qiu, Fudong Wu, Fan Chen, Guihai Department of Computer Science and Engineering Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai200240 China

With the popularity of mobile wireless devices equipped with various kinds of sensing abilities, a new service paradigm named participatory sensing has emerged to provide users with brand new life experience. However, the wide application of participatory sensing has its own challenges, among which privacy and multimedia data quality preservations are two critical problems. Unfortunately, none of the existing work has fully solved the problem of privacy and quality preserving participatory sensing with multimedia data. In this paper, we propose SLICER , which is the first k -anonymous privacy preserving scheme for participatory sensing with multimedia data. SLICER integrates a data coding technique and message transfer strategies, to achieve strong protection of participants' privacy, while maintaining high data quality. Specifically, we study two kinds of data transfer strategies, namely transfer on meet up (TMU) and minimal cost transfer (MCT). For MCT, we propose two different but complimentary algorithms, including an approximation algorithm and a heuristic algorithm, subject to different strengths of the requirement. Furthermore, we have implemented SLICER and evaluated its performance using publicly released taxi traces. Our evaluation results show that SLICER achieves high data quality, with low computation and communication overhead. © 2014 IEEE.

关键词： Data transfer

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Launching the VASCO Citizen Science Project

arXiv

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arXiv 2020年

作者： Villarroel, Beatriz Pelckmans, Kristiaan Solano, Enrique Laaksoharju, Mikael Souza, Abel Dom, Onyeuwaoma Nnaemeka Laggoune, Khaoula Mimouni, Jamal Guergouri, Hichem Mattsson, Lars García, Aurora Lago Soodla, Johan Castillo, Diego Shultz, Matthew E. Aworka, Rubby Comerón, Sébastien Geier, Stefan Marcy, Geoffrey W. Gupta, Alok C. Bergstedt, Josefine Bär, Rudolf E. Buelens, Bart Enriquez, Emilio Mellon, Christopher K. Prieto, Almudena Wamalwa, Dismas Simiyu de Souza, Rafael S. Ward, Martin J. Nordita KTH Royal Institute of Technology Stockholm University Roslagstullsbacken 23 StockholmSE-106 91 Sweden Instituto de Astrofísica de Canarias Avda Vía Láctea s/n Tenerife La LagunaE-38205 Spain Department of Information Technology Uppsala University Box 337 UppsalaSE-751 05 Sweden P.O. Box 78 Villanueva de la CañadaE-28691 Spain Autonomous Distributed Systems Lab Department of Computing Science Umeå Universitet Umeå901 87 Sweden Center for Basic Space Science National Space Research and Development Agency P.O. Box 2022 Nsukka Enugu410002 Nigeria Sirius Astronomy Association Sirius Astronomy Association BP 18 Cité du 20 Aout Constantine25000 Algeria Department of Physics University of Constantine 1 LPMPS CERIST Constantine25000 Algeria Science of Matter Division Research Unit in Scientific Mediation CERIST Constantine25016 Algeria IES Tartessos Barriada Hiconsa s/n Camas Sevilla41900 Spain Department of Physics & Astronomy University of Delaware 217 Sharp Lab NewarkDE19716 United States Ghana Space Science and Technology Institute Atomic-Haatso Road Kwabenya Box LG 80 Accra233 Ghana University of Ghana Summerhill Estates Ltd Rd Santeo P.O. Box LG 25 Legon Accra Ghana Departamento de Astrofísica Universidad de La Laguna Tenerife La LagunaE-38206 Spain Cuesta de San José s/n La Palma Breña Baja38712 Spain Space Laser Awareness 3883 Petaluma Hill Rd Santa RosaCA95404 United States Manora Peak Nainital263 001 India Freelance Uppsala752 17 Sweden Institute for Particle Physics and Astrophysics ETH Zürich Wolfgang-Pauli-Strasse 27 ZürichCH-8093 Switzerland Mol2400 Belgium Department of Astrophysics IMAPP Radboud University Nijmegen Nijmegen6525 AJ Netherlands Galileo project affiliate Meru Physics Department University of Science and Technology Meru P.O. Box Kenya972-60200 Kenya Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 80

The Vanishing & Appearing Sources during a Century of Observations (VASCO) project investigates astronomical surveys spanning a time interval of 70 years, searching for unusual and exotic transients. We present herein the VASCO Citizen Science Project, which can identify unusual candidates driven by three different approaches: hypothesis, exploratory, and machine learning, which is particularly useful for SETI searches. To address the big data challenge, VASCO combines three methods: the Virtual Observatory, user-aided machine learning, and visual inspection through citizen science. Here we demonstrate the citizen science project and its improved candidate selection process, and we give a progress report. We also present the VASCO citizen science network led by amateur astronomy associations mainly located in Algeria, Cameroon, and Nigeria. At the moment of writing, the citizen science project has carefully examined 15,593 candidate image pairs in the data (ca. 10% of the candidates), and has so far identified 798 objects classified as "vanished". The most interesting candidates will be followed up with optical and infrared imaging, together with the observations by the most potent radio telescopes. Copyright © 2020, The Authors. All rights reserved.

关键词： Machine learning

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