Subjects -> COMPUTER SCIENCE (Total: 2313 journals)
    - ANIMATION AND SIMULATION (33 journals)
    - ARTIFICIAL INTELLIGENCE (133 journals)
    - AUTOMATION AND ROBOTICS (116 journals)
    - CLOUD COMPUTING AND NETWORKS (75 journals)
    - COMPUTER ARCHITECTURE (11 journals)
    - COMPUTER ENGINEERING (12 journals)
    - COMPUTER GAMES (23 journals)
    - COMPUTER PROGRAMMING (25 journals)
    - COMPUTER SCIENCE (1305 journals)
    - COMPUTER SECURITY (59 journals)
    - DATA BASE MANAGEMENT (21 journals)
    - DATA MINING (50 journals)
    - E-BUSINESS (21 journals)
    - E-LEARNING (30 journals)
    - ELECTRONIC DATA PROCESSING (23 journals)
    - IMAGE AND VIDEO PROCESSING (42 journals)
    - INFORMATION SYSTEMS (109 journals)
    - INTERNET (111 journals)
    - SOCIAL WEB (61 journals)
    - SOFTWARE (43 journals)
    - THEORY OF COMPUTING (10 journals)

SOCIAL WEB (61 journals)

Showing 1 - 58 of 58 Journals sorted alphabetically
ACM Transactions on Social Computing     Hybrid Journal  
ACM Transactions on the Web (TWEB)     Hybrid Journal   (Followers: 3)
American Journal of Information Systems     Open Access   (Followers: 4)
Asiascape : Digital Asia     Hybrid Journal   (Followers: 1)
CCF Transactions on Networking     Hybrid Journal  
Communications in Mobile Computing     Open Access   (Followers: 14)
Computational Social Networks     Open Access   (Followers: 4)
Cyberpolitik Journal     Open Access  
Cyberpsychology, Behavior, and Social Networking     Hybrid Journal   (Followers: 16)
Data Science     Open Access   (Followers: 6)
Digital Library Perspectives     Hybrid Journal   (Followers: 42)
Discover Internet of Things     Open Access   (Followers: 2)
Informação & Informação     Open Access   (Followers: 2)
Information Technology and Libraries     Open Access   (Followers: 342)
Infrastructure Complexity     Open Access   (Followers: 5)
International Journal of Art, Culture and Design Technologies     Full-text available via subscription   (Followers: 10)
International Journal of Bullying Prevention     Hybrid Journal   (Followers: 1)
International Journal of Digital Humanities     Hybrid Journal   (Followers: 3)
International Journal of e-Collaboration     Full-text available via subscription  
International Journal of E-Entrepreneurship and Innovation     Full-text available via subscription   (Followers: 6)
International Journal of Entertainment Technology and Management     Hybrid Journal   (Followers: 1)
International Journal of Information Privacy, Security and Integrity     Hybrid Journal   (Followers: 25)
International Journal of Information Technology and Web Engineering     Hybrid Journal   (Followers: 2)
International Journal of Interactive Communication Systems and Technologies     Full-text available via subscription   (Followers: 2)
International Journal of Interactive Mobile Technologies     Open Access   (Followers: 8)
International Journal of Internet and Distributed Systems     Open Access   (Followers: 2)
International Journal of Knowledge Society Research     Full-text available via subscription  
International Journal of Networking and Virtual Organisations     Hybrid Journal   (Followers: 11)
International Journal of Social and Humanistic Computing     Hybrid Journal  
International Journal of Social Computing and Cyber-Physical Systems     Hybrid Journal  
International Journal of Social Media and Interactive Learning Environments     Hybrid Journal   (Followers: 14)
International Journal of Social Network Mining     Hybrid Journal   (Followers: 3)
International Journal of Virtual Communities and Social Networking     Full-text available via subscription   (Followers: 1)
International Journal of Web Based Communities     Hybrid Journal  
International Journal of Web-Based Learning and Teaching Technologies     Hybrid Journal   (Followers: 20)
International Journal on Semantic Web and Information Systems     Hybrid Journal   (Followers: 4)
Internet Technology Letters     Hybrid Journal  
JLIS.it     Open Access   (Followers: 7)
Journal of Cyber Policy     Hybrid Journal   (Followers: 1)
Journal of Digital & Social Media Marketing     Full-text available via subscription   (Followers: 18)
Journal of Social Structure     Open Access   (Followers: 1)
Medicine 2.0     Open Access   (Followers: 2)
Observatorio (OBS*)     Open Access  
Online Social Networks and Media     Hybrid Journal   (Followers: 9)
Policy & Internet     Hybrid Journal   (Followers: 11)
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies     Hybrid Journal  
Redes. Revista Hispana para el Análisis de Redes Sociales     Open Access  
RESET     Open Access  
Scientific Phone Apps and Mobile Devices     Open Access  
Social Media + Society     Open Access   (Followers: 25)
Social Network Analysis and Mining     Hybrid Journal   (Followers: 4)
Social Networking     Open Access   (Followers: 3)
Social Networks     Hybrid Journal   (Followers: 20)
Social Science Computer Review     Hybrid Journal   (Followers: 13)
Synthesis Lectures on the Semantic Web: Theory and Technology     Full-text available via subscription  
Teknokultura. Revista de Cultura Digital y Movimientos Sociales     Open Access  
Terminal     Open Access  
Texto Digital     Open Access  
Similar Journals
Journal Cover
CCF Transactions on Networking
Number of Followers: 0  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 2520-8462 - ISSN (Online) 2520-8470
Published by Springer-Verlag Homepage  [2469 journals]
  • Energy efficient broadcast protocol for Underwater Wireless Sensor
           Networks

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      Abstract: Abstract An Underwater Wireless Sensor Network (UWSN) is a collection of sensors which are deployed at different depths. The 3D deployment model enables sensing data from surrounding area and relay information to sink nodes at the surface level. This paper, proposes an energy efficient, high reachability 3D broadcast protocol for UWSN (EE-BUWSN). EE-BUWSN protocol takes into accounts the acoustic channel characteristics and the 3D nature of undersea area to reduce packet collisions and hop count in order to achieve high delivery ratio, with less energy consumption. Simulation results show that EE-BUWSN protocol outperforms Vector-Based Forwarding broadcast protocol (VBFB) and probability-based protocols in terms of reachability, latency, and energy consumption.
      PubDate: 2021-08-02
      DOI: 10.1007/s42045-021-00049-y
       
  • Editorial of CCF transactions on networking: special issue on
           intelligence-enabled end-edge-cloud orchestrated computing

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      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00048-5
       
  • Blockchain-driven anomaly detection framework on edge intelligence

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      Abstract: Abstract There are a large number of end devices in an IoT system, which may malfunction due to various reasons, such as being attacked. Anomaly detection of the devices and the whole IoT system normally rely on the analysis of the huge amount of log records generated by the end devices. How to protect the log records from being tampered with and realize the real-time anomaly detection is a challenging task which is still not addressed. Existing works on anomaly detection by the emerging and effective deep learning algorithms require the transfer of log data to cloud servers which incurs high communication overhead and long detection latency, and is subject to the risk of being tampered. In this paper, we propose a novel and efficient hierarchical framework for online anomaly detection in IoT systems atop Blockchain and smart contracts. At the device layer of the hierarchical framework, an efficient feature extractor is developed to preprocess the raw log data which greatly reduces the size of data to be transferred while keeps sufficient information for the anomaly detection model to use. At the cloud layer of the framework, deep learning models use the processed data from the device layer to build the detection model and output normal workflow patterns. In the edge layer of the framework, a permissioned blockchain is built and a series of smart contracts are developed which can guarantee data integrity and achieve automatic anomaly detection based on the model output from the cloud layer. Extensive experiments demonstrate that our framework can reduce the ledger size by 7.1% without detection accuracy reduction compared with traditional centralized solutions and the detection latency is only 0.47ms in our prototype. Our feature extractor can speed up by 3.6x–7.3x times on the execution time with almost the same CPU usage rate compared with state-of-the-art log parsers and encryption solutions, such as AES and RSA.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00044-9
       
  • Cooperative abnormal sound event detection in end-edge-cloud orchestrated
           systems

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      Abstract: Abstract In this paper, we propose a novel cooperative abnormal sound event detection framework for city surveillance in end-edge-cloud orchestrated systems. A novel offloading decision-making scheme that leverages hierarchical computational capabilities is proposed to speed up the detection process. The audio pre-processing (feature extraction) and post-processing (sound source localization and sound event classification) can be locally executed or offloaded to the edge or cloud based on the calculation of the so-called communication-to-computation ratio. Furthermore, considering the biased audio information due to source-sensor geometries, a cooperative decision-making algorithm is proposed to aggregate the sound event classification results with adaptive control and ensemble learning. In the audio pre-processing, the log-mel spectrogram and time of arrival information are first extracted from the audio waveform captured by the distributed acoustic sensors and then sent to the computation entity assigned by the offloading scheme. In the audio post-processing, the sound source is localized through least-square minimization. Guided by the localized sound source, the spectrograms are fed into the pre-trained neural networks and then the result aggregation algorithm for further classification. Extensive experiments regarding latency and detection accuracy show the superiority and robustness of the proposed scheme, avoiding the cumulative latency caused by the increased number of sensors while maintaining high detection accuracy.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00042-x
       
  • QoE evaluation of dynamic adaptive streaming over HTTP (DASH) with
           promising transport layer protocols

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      Abstract: Abstract Recently, dynamic adaptive streaming over HTTP (DASH) has become an increasingly popular way to view video over the Internet. In particular compared to other video streaming services these DASH approaches deliver superior QoE to viewers. This is due to improved video segment selection. Generally, Transmission Control Protocol (TCP) CUBIC is the defacto transport layer protocol use by DASH. To improve the robustness of the transport layer to network congestion many other TCP variants were implemented such as Compound TCP and BBR. Nevertheless some were made to work specifically in LAN environments for example Agile-SD. However, recently another transport layer protocol User Datagram Protocol (UDP) has been used in Google’s QUIC implementation. To date no work has be found giving the performance of these transport layer protocols with DASH. In this paper we test the performance of Agile-SD, CUBIC, Compound TCP, BBR and QUIC using the BBA, MPC, Pensieve and Oboe DASH approaches. Experiments simulate congested bottleneck link conditions common at household routers where families view multiple videos at the same time. We observe Oboe and Agile-SD is the most promising combination with CUBIC and Pensieve next. However, even though QUIC was touted to have superior transport layer performance by Google it was the worst performing.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00047-6
       
  • An efficient flow-based multi-level hybrid intrusion detection system for
           software-defined networks

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      Abstract: Abstract Software-defined networking (SDN) is a novel networking paradigm that provides enhanced programming abilities, which can be used to solve traditional security challenges on the basis of more efficient approaches. The most important element in the SDN paradigm is the controller, which manages the flows of each correspondence forwarding element (switch or router). Flow statistics provided by the controller are considered to be useful information that can be used to develop a network-based intrusion detection system. Therefore, in this paper, we propose a 5-level hybrid classification system based on flow statistics in order to attain an improvement in the overall accuracy of the system. For the first level, we employ the k-nearest neighbor approach (kNN); for the second level, we use the extreme learning machine (ELM); and for the remaining levels, we utilize the hierarchical extreme learning machine (HELM) approach. In comparison with conventional supervised machine learning algorithms and other state-of-the-art methodologies based on the NSL-KDD benchmark dataset, the experimental study showed that our system achieves a good accuracy (84.29%), with an ability to detect new attacks that reaches 77.18%. Therefore, our approach presents an efficient approach for intrusion detection in SDNs.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00040-z
       
  • A trusted recommendation scheme for privacy protection based on federated
           learning

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      Abstract: Abstract With the convergence of the era of global news and the era of big data, the daily amount of news sent to the world is exploding. Users also face the problem of information overloads when they get massive information, which leads to how cloud servers push personalized data to users among massive data have become the focus of news companies. In order to obtain the push accuracy, the traditional recommendation system often makes deep mining of users’ privacy data, which makes users’ privacy cannot be guaranteed. In order to solve the above problems, this paper proposes a collaborative filtering algorithm recommendation system based on federated learning on end-edge-cloud. The exposure of data privacy was further prevented by adding Laplace noise to the training model through differential privacy technology. Finally, the training model and recommendation information is stored to the blockchain network to provide permanent storage, evidence chain and real-time traceability services.On the premise of protecting data privacy, this system provides cloud server with solutions to alleviate computing pressure, bandwidth pressure and improve news push accuracy through end-edge-cloud distributed learning.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00045-8
       
  • Towards fair and efficient task allocation in blockchain-based
           crowdsourcing

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      Abstract: Abstract Crowdsourcing has been a popular paradigm leveraging the power of the crowd to accomplish a common goal. Traditional crowdsourcing systems rely on a centralized platform to allocate tasks and rewards to users, facing severe problems of single node failure and malicious behaviors of the platform. Recently, some works take advantage of blockchain to solve the drawback of centralization. However, the key issue, fair and efficient task allocation, has not been explored in existing blockchain-based crowdsourcing systems. In this paper, we design a novel blockchain-based framework for crowdsourcing, in which a distributed reverse and blind auction-based task allocation mechanism (RbatAlloc) is proposed utilizing user profile and bidding price to realize fair and efficient task allocation in transparent blockchain environment. Finally, we implement a prototype of our system and deploy it to a locally developed network. The experimental results demonstrate the effectiveness of the proposed framework and mechanism.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00043-w
       
  • Performance analysis of hybrid SAC-OCDMA-OFDM model over free space
           optical communication

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      Abstract: Abstract Recently, free space optical (FSO) communication network has gained popularity because of its unlicensed spectrum, high data rate, low power consumption and ease of installation. Spectral amplitude coded optical code division multiple access (SAC-OCDMA) technique has become significant because of enhanced network capacity, privacy, and simultaneous use of multiple asynchronous users. However, the climatic turbulence causes signal fluctuations and degrades the quality of the received signal. Therefore, to improve the quality of the received signal in this work, SAC-OCDMA technique is integrated with orthogonal frequency division multiplexing (OFDM) technique and performance of such hybrid model is investigated. Two SAC-OCDMA codes are proposed in this work referred as triangular matrix-zero cross correlation (TM-ZCC) and pulse shifting substitution-zero cross correlation (PSS-ZCC) codes. The hybrid model uses the novel one dimensional (1D) SAC-OCDMA codes with two different types of receivers such as modified AND (M-AND), and single photodiode detection (SPD) receivers over Gamma-Gamma fading channel to evaluate the bit error rate (BER) performance of the proposed hybrid model. It is observed from the simulation results that the hybrid SAC-OCDMA-OFDM model with SPD detection receiver performs better in terms of bit error rate (BER) than that of M-AND detection receiver. Furthermore, it is also observed from the simulation results that the hybrid model based on the proposed TM-ZCC code performs better than the proposed PSS-ZCC code in terms of BER and higher FSO distance coverage. The performance of the proposed model is estimated for 20 and 30 active users simultaneously at various data rates. The TM-ZCC and PSS-ZCC codes are generated using MATLAB platform and the proposed hybrid SAC-OCDMA-OFDM model is validated using optisystem version 15 over the Gamma-Gamma fading channel.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00039-6
       
  • ECAS: an efficient and conditional privacy preserving collision warning
           system in fog-based vehicular ad hoc networks

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      Abstract: Abstract VANET is a prominent way to provide road security and prevent vehicles from collision by using various methods, such as message dissemination, traffic management, etc. However, the traditional vehicular network faces some problems related to high latency, low bandwidth, and communication in open wireless environment. Thus, some researchers have attempted to combine fog computing with vehicular ad hoc networks to overcome these problems. In this paper, we design an efficient and conditional privacy preserving collision warning system for fog-based vehicular ad hoc networks without using bilinear pairing. The fog nodes collect the speed violation reports from the speed sensor of vehicles. This protocol achieves privacy protection, message authentication, and revocate malicious vehicles. We also provide strict security proof and illustrate how to reach the security requirements in the proposed protocol. Moreover, the experiment demonstrates that the proposed protocol provides better efficiency in computation overhead and communication overhead, and makes it more applicable for adoption in the VANET collision warning systems.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00041-y
       
  • Proof of outsourced encryption: cross verification of security service
           level agreement

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      Abstract: Abstract With the popularity of cloud and edge computing, user data is often stored at third party service providers. Restricted by the available resources, end users may need to outsource the data encryption operations. However, the security service level agreement (SSLA) are usually hard to verify since it is fairly hard for end users to learn the data status at the service providers. In this paper, we investigate the proof of outsourced encryption problem. We first define the expected properties of the proof of encryption (PoE) mechanisms. Depending on the negotiated encryption algorithm in SSLA, we design two verification mechanisms so that end users can query encryption results at service providers to verify the enforcement of SSLA even when they are not aware of the keys. We formally analyze the protocols with BAN logic. Simulation and experiments show that our approaches can detect a dishonest service provider with high probability.
      PubDate: 2020-12-01
      DOI: 10.1007/s42045-020-00046-7
       
  • Security of cellular networks position paper

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      Abstract: Abstract The paper discusses current research efforts focusing on security and privacy for 5G cellular networks. It then outlines research directions.
      PubDate: 2020-10-01
      DOI: 10.1007/s42045-020-00037-8
       
  • Sum of squares: a new metric for NFV service chain placement in edge
           computing environments and efficient heuristic algorithms

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      Abstract: Abstract Both network function virtualization (NFV) and edge computing (EC), especially the latter, are attracting more and more attention in recent years. A growing number of network service providers are migrating their services from the cloud to the edge for better QoS services, while the recent researches on NFV also concentrate on deploying NFV services in edge computing networks. However, NFV deployment in edge networks is a troublesome challenge and is fairly alien from conventional NFV deployment problems in data centres. Edge network differs from the data center network in the following two aspects: firstly, edge nodes are constrained in computing capacity, and secondly, the network connections between edge nodes are unstable and dynamic, which may show large variance over time. This means edge computing should be designed for high-efficient use of physical edge nodes’ resources. To address the challenges above, we investigate a new NFV Service Chain Placement problem in edge computing environments (NSCP-EC) in this paper. We first prove that the NSCP-EC problem is NP-complete. Then we propose a new metric which can better measure the capacity utilization rate of physical resources, and analyze its advantages with details. Based on the new metric, we propose two heuristic but efficient algorithms called MINI and MINI-tree. To confirm the performance of the two algorithms, we conduct simulations. The result demonstrates that MINI gains an advantage over genetic algorithm (GA) and MINI-tree orevails over MINI in tree topology conditions in the aspects of physical resource utilization, acceptance rate and running time. Both theoretical analysis and simulation results confirm the feasibility of the algorithms.
      PubDate: 2020-10-01
      DOI: 10.1007/s42045-020-00030-1
       
  • A multi-level proactive security auditing framework for clouds through
           automated dependency building

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      Abstract: Abstract A cloud is very often a subject to diverse security threats directing to its multiple levels (e.g., user, virtual, and physical). Even though there exist several security solutions for a specific cloud level, none of them provides a comprehensive solution that can protect a cloud tenant against the threats rendered from those multiple levels; which is mainly due to the operational complexity and unique nature of each level of cloud (e.g., authentication and access control models in the user level vs. VM migration rules in the virtual level) in a cloud. Furthermore, a simple integration of those existing tools will not be sufficient as all of them are suffering from different practical issues. For instance, most of the existing solutions suffer from slow response time and require significant manual efforts from the cloud tenants. In this paper, we propose a multi-level proactive security auditing framework, which provides a unified platform to plug-in existing security auditing tools for those levels and overcomes their major practical issues. To this end, our main idea is to design a framework to integrate existing auditing solutions and protect the multiple levels of a cloud. Also, we convert those tools (regardless of their original nature, e.g., retroactive and runtime) into a proactive auditing solution by leveraging a predictive model, which captures the dependency relationships between cloud events and helps to predict future events. We integrate our framework with OpenStack, a popular cloud management platform and outline a concrete guideline to adapt our framework to other major cloud platforms, Google GCP, Amazon EC2, and Microsoft Azure. Our experiments using both synthetic and real data show the practicality and effectiveness of this solution (e.g., responding in a few milliseconds to verify each level of the cloud).
      PubDate: 2020-10-01
      DOI: 10.1007/s42045-020-00028-9
       
  • A stimulus-response based EEG biometric using mallows distance

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      Abstract: Abstract Electroencephalogram (EEG) activity from the brain is a promising biological marker that can serve as personal identification. Despite substantial efforts, it remains an unsolved problem to quantify EEG feature distribution for brain biometrics due to the complexity of the brain. In this study, we attempt to tackle EEG-based identification challenges by exploiting a novel distribution model. The distribution dissimilarity is measured by Mallows distance, a cluster similarity sensitive distance that is robust to signal noises. Specifically, EEG signals are decomposed through several statistical feature extraction methods, autoregressive model, discrete wavelet transform, and fast Fourier transform. With the dataset obtained from the real-world application, our proposed system achieves the f score accuracy of \(96.18\%\) and half total error rate of \(2.223\%\) , which demonstrates the feasibility and effectiveness of utilizing EEG biometrics for personal identification applications.
      PubDate: 2020-10-01
      DOI: 10.1007/s42045-020-00033-y
       
  • A scalable ledger-assisted architecture for secure query processing over
           distributed IoT data

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      Abstract: Abstract Massive amount of IoT data poses unique challenges in centralized data management systems. Specifically, IoT data can originate from heterogeneous and distributed sources, and commonly regulations forbid data from different IoT stakeholders to be managed via central governance. To serve for IoT applications, recent proposals leverage distributed ledgers (e.g., blockchains) to function on top of distributed data storage with improved data interoperability. Unfortunately, most of them do not consider data security and privacy in the first place. Along with this transformative paradigm, in this paper, we propose a ledger-assisted architecture for secure distributed IoT data management. This architecture adapts searchable encryption to decentralized storage network to enable encrypted query processing. Meanwhile, it is designed to leverage the distributed ledger to harden both data and query integrity. To deal with continuously generated IoT data, we further devise an efficient secure data insertion protocol, and employ a recent variant of blockchain for users to validate updated query results in a scalable manner. Evaluations on Azure blockchain service confirm the practicality of our proposed architecture.
      PubDate: 2020-10-01
      DOI: 10.1007/s42045-020-00038-7
       
  • Editorial of special issue on time-sensitive networking

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      PubDate: 2020-09-01
      DOI: 10.1007/s42045-020-00035-w
       
  • Packet-size aware scheduling algorithms in guard band for time sensitive
           networking

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      Abstract: As an emerging and promising technology, Time Sensitive Networking (TSN) can be widely used in many real-time systems such as Industrial Internet of Things (IIoT) and Cyber Physical System (CPS). TSN, while ensuring the bounded latency and jitter, exhibits the disadvantage of not being able to efficiently use the bandwidth resources in the guard band. In this paper, we propose an algorithm family named Packet-size Aware Shaping (PAS), which is inspired by abstracting the problem of utilizing the guard band to a classic Precedence-Constrained Knapsack Problem (PCKP). PAS works with the existing TSN standards, having achieved the goal of guaranteeing the end-to-end latency for scheduled time-sensitive applications while fully utilizing the available bandwidth in the guard band for others. Furthermore, we have proposed and implemented several hardware designs for both the current standard TSN scheduler and the programmable one. The simulation results show that the PAS family can achieve satisfying performance in maximizing the resource utilization in the guard band. The synthesis results on Xilinx Vivado show that our proposed Multi-group Push-In-First-Out (MPIFO) scheduler can achieve 100 Mpps scheduling rate for 1024 scheduling items, which is fast enough to support the high-speed TSN.
      PubDate: 2020-09-01
      DOI: 10.1007/s42045-020-00031-0
       
  • OpenTSN: an open-source project for time-sensitive networking system
           development

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      Abstract: Abstract Time-sensitive networking (TSN) is a promising technique in many fields such as industrial automation and autonomous driving. The standardization of TSN has been rapidly improved by the IEEE 802.1 TSN working group. Currently, it has formed a comprehensive standard system with a wide range of choices. However, there is a large gap between TSN standards and application specific TSN systems. Designers need to determine the required TSN standards and standard implementation methods based on the application’s transmission performance and reliability requirements. Therefore, an easy-to-use developing platform for rapid TSN system prototyping and evaluation plays a vital role in the application of TSN technologies. This article mainly introduces OpenTSN, an open source project that supports rapid TSN system customization. This project has three features, which are SDN-based TSN network control mechanism, time-sensitive management protocol and time-sensitive switching model, for building an efficient TSN system. OpenTSN opens all the hardware and software source codes so that designers can quickly and flexibly customize the TSN system according to their own needs, maximizing the reuse of existing code and reducing the customization complexity. With this project, two FPGA-based prototyping examples with star and ring topology are presented at the experimental section. The experiment results show that the synchronization precision of the entire testing network is under 32 ns and the transmission performance matches the theory analysis of the testing Cyclic Queue and Forwarding based TSN network.
      PubDate: 2020-09-01
      DOI: 10.1007/s42045-020-00029-8
       
  • A weakly secure multiple description coding scheme in lossy multipath
           channels for fine-grained SVC streaming

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      Abstract: Abstract Video streaming service is growing rapidly these years. The quality of video streaming may suffer from issues like packet loss and solutions like multiple description coding (MDC) has been shown to provide flexible and distortion-rate optimal video streaming transmission over packet-loss links. On the other hand, the contents of stream are prone to attacks like eavesdropping while MDC is not specifically designed for security purpose. In this paper, fine grain scalably coded video data is channel-coded through multiple description-forward error correction (MD-FEC) coding. The resulting descriptions (packets) are then sent to a variety of channels, which may be peeked by an eavesdropper with certain probability. We adopt the weakly secure design here to reduce the quality of video obtained by the eavesdropper. Two weak security criteria for this problem are proposed, namely the deterministic overhearing peak-signal-to-noise-ratio (PSNR) constraint and the average overhearing PSNR constraint. The corresponding optimization algorithms are provided. Numerical results show that by applying weakly secure design, the PSNR value on the eavesdropper’s side will be reduced to the level at which useful details of the clip can hardly be exposed at the cost of reducing around 1–2 dB at the authorized receiver’s side.
      PubDate: 2020-09-01
      DOI: 10.1007/s42045-020-00036-9
       
 
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