Survey of Error Correction Mechanisms for Video Streaming over the Internet

Ababneh, Jafar; Almomani, Omar

doi:10.14569/ijacsa.2014.050322

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…In communication theory, majorly, two methods exist to deal with frame losses: Automatic Repeat reQuest (ARQ) and Forward Error Correction (FEC) [18]. The former employs error detection schemes in communication but relies on frame retransmission to correct errors, which is inefficient because of data overhead and more collisions.…”

Section: Erasure Codingmentioning

confidence: 99%

DaRe:DataRecovery Through Application Layer Coding for LoRaWAN

Marcelis

Kouvelas

Rao

et al. 2022

IEEE Trans. on Mobile Comput.

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Long-range wide-area network (LoRaWAN) is an energy-efficient and inexpensive networking technology that is rapidly being adopted for many Internet-of-Things applications. In this study, we perform extensive measurements on a new LoRaWAN deployment to characterise the spatio-temporal properties of the LoRaWAN channel. Our experiments reveal that LoRaWAN frames are mostly lost due to the channel effects, which are adverse when the end-devices are mobile. The frame losses are up to 70 percent, which can be bursty for both mobile and stationary scenarios. Frame losses result in data losses since the frames are transmitted only once in the basic configuration. To reduce data losses in LoRaWAN, we design a novel coding scheme for data recovery called DaRe that works on the application layer. DaRe combines techniques from convolutional and fountain codes. By implementing DaRe, we show that 99 percent of the data can be recovered with a code rate of 1/2 when the frame loss is up to 40 percent. Compared to the repetition coding scheme, DaRe provides 21 percent higher data recovery and can save up to 42 percent of the energy consumed on a transmission for 10-byte data units. We also show that DaRe provides better resilience to bursty frame losses.

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Section: Erasure Codingmentioning

confidence: 99%

DaRe:DataRecovery Through Application Layer Coding for LoRaWAN

Marcelis

Kouvelas

Rao

et al. 2022

IEEE Trans. on Mobile Comput.

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“…STARTGAS: represents the max number of computational steps allowed for the transaction to execute. The data field is the key field that the contracts use to read whenever a smart contract is addressed [ 42 , 43 , 44 , 45 , 46 ]. IIoT Security Threats IIoT solutions consist of industrial systems that connect to cloud for data collection and analysis purposes.…”

Section: Background and Related Studiesmentioning

confidence: 99%

“…The second level of FN’s latency is reduced because of IoMT device communication. As a result, users’ requirements are encountered in the proposed fog computing (FC) model [ 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ].…”

Section: Proposed Frameworkmentioning

confidence: 99%

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Almaiah

Ali

Hajjej

et al. 2022

Sensors

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The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

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“…(iii) Extensive experiments with hundreds of receivers: Our experiments demonstrate that MuDRA swiftly converges to the target-rate, while meeting the Service Level Agreement (SLA) requirements (e.g., ensuring PDR above 85% to at least 95% of the nodes). Losses can be recovered by using appropriate application-level FEC methods [39], [41], [52], [55], [56].…”

Section: B Our Contributionsmentioning

confidence: 99%

Experimental Evaluation of Large Scale WiFi Multicast Rate Control

Gupta

Gutterman

Bejerano

et al. 2018

IEEE Trans. Wireless Commun.

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WiFi multicast to very large groups has gained attention as a solution for multimedia delivery in crowded areas. Yet, most recently proposed schemes do not provide performance guarantees and none have been tested at scale. To address the issue of providing high multicast throughput with performance guarantees, we present the design and experimental evaluation of the Multicast Dynamic Rate Adaptation (MuDRA) algorithm. MuDRA balances fast adaptation to channel conditions and stability, which is essential for multimedia applications. MuDRA relies on feedback from some nodes collected via a light-weight protocol and dynamically adjusts the rate adaptation response time. Our experimental evaluation of MuDRA on the ORBIT testbed with over 150 nodes shows that MuDRA outperforms other schemes and supports high throughput multicast flows to hundreds of receivers while meeting quality requirements. MuDRA can support multiple high quality video streams, where 90% of the nodes report excellent or very good video quality.

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Survey of Error Correction Mechanisms for Video Streaming over the Internet

Cited by 9 publications

References 31 publications

DaRe:DataRecovery Through Application Layer Coding for LoRaWAN

DaRe:DataRecovery Through Application Layer Coding for LoRaWAN

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things

Experimental Evaluation of Large Scale WiFi Multicast Rate Control

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