Network Coding Research Articles

Evaluating Mentorship in the Aquatic Sciences in the Context of a Global Pandemic (COVID‐19)

AbstractThe COVID‐19 pandemic drastically altered human social systems. To better understand ramifications of the pandemic for aquatic scientists, we assessed perceptions of mentorship within the Association for the Sciences of Limnology and Oceanography, focusing on impacts felt during the pandemic. We also evaluated current preferences and practices (e.g., valued traits, network composition, communication tools) related to mentoring, as a way of gauging change within the community and informing ongoing or future resilience and recovery efforts. In surveying this group, we found the largest pandemic related professional development gaps to be lost opportunities for mentoring, the absence of in‐person meetings, and missed collegial/collaborative interactions. We also assessed which mentorship characteristics were highly valued and found that, “communicative” was the most consistently valued characteristic. Finally, we assessed mentor network composition and code of conduct use. Findings show a limited range of disciplines within most mentees' networks, suggesting lack of access to mentors in outside fields or disciplines, and widespread valuation of codes of conduct but limited implementation. We advocate for mentoring practices that foster personal connections, expand networks, and develop clear plans for mentoring relationships, as a path toward general improvement and resilience within mentoring networks in the face of disruption.

A Lightweight Identity-Based Network Coding Scheme for Internet of Medical Things

Network coding is a potent technique extensively utilized in decentralized Internet of Things (IoT) systems, including the Internet of Medical Things (IoMT). Nevertheless, the inherent packet-mixing characteristics of network coding expose data transmission to pollution attacks, potentially compromising the integrity of original files. The homomorphic signature scheme serves as a robust cryptographic tool that can bolster network coding’s resilience against such attacks. However, current schemes are computationally intensive for signature verification, making them impractical for IoMT environments. In this study, we propose a lightweight identity-based network coding scheme (IBNS) that minimizes computational overhead during the signing and verification processes. This scheme has been demonstrated to be secure against adaptive chosen-message attacks and is well-suited for IoMT applications. Furthermore, we assess the performance of our IBNS through both theoretical and experimental analyses. Simulation outcomes confirm that our scheme outperforms previous ones in terms of practicality and efficiency.

Securing the Transmission While Enhancing the Reliability of Communication Using Network Coding in Block-Wise Transfer of CoAP

The practical employment of network coding (NC) has shown major improvements when it comes to the transmission reliability of sender data and bandwidth utilization. Moreover, network coding has been employed recently to secure the transmission of data and prevent unauthorized recovery of sender packets. In this paper, we employ network coding (NC) in a practical way in networks with constrained resources with the goal of improving the reliability and security of data transfer. More specifically, we apply NC on the recent options of block-wise transfer (BWT) of the constrained application protocol (CoAP). The goal is to enhance the reliability of CoAP when used to transfer larger data blocks using BWT. Also, we employ an innovative homomorphic encryption approach to secure the BWT of CoAP.

Private information retrieval from locally repairable databases with colluding servers

We consider information-theoretical private information retrieval (PIR) from a coded database with colluding servers. We target, for the first time, locally repairable storage codes (LRCs). We consider any number of local groups g, locality r, local distance δ and dimension k. Our main contribution is a PIR scheme for maximally recoverable (MR) LRCs based on linearized Reed–Solomon codes, which achieve the smallest field sizes among MR-LRCs for many parameter regimes. In our scheme, nodes are identified with codeword symbols and servers are identified with local groups of nodes. Only locally non-redundant information is downloaded from each server, that is, only r nodes (out of r+δ−1) are downloaded per server. The PIR scheme achieves the (download) rate R=(N−k−rt+1)/N, where N=gr is the length of the MDS code obtained after removing the local parities, and for any t colluding servers such that k+rt≤N. For an unbounded number of stored files, the obtained rate is strictly larger than those of known PIR schemes that work for any MDS code. Finally, the obtained PIR scheme can also be adapted when communication between the user and each server is performed via linear network coding, achieving the same rate as previous PIR schemes for this scenario but with polynomial finite field sizes, instead of exponential. Our rates are equal to those of PIR schemes for Reed–Solomon codes, but Reed–Solomon codes are incompatible with the MR-LRC property or linear network coding, thus our PIR scheme is less restrictive in its applications.

Large optimal cyclic subspace codes

Constant dimension codes, a specialized subclass of subspace codes, have garnered significant attention in recent years due to their applications in random network coding. Among these codes, large cyclic constant dimension codes, endowed with optimal minimum distance, offer efficient solutions for encoding and decoding algorithms. This paper is dedicated to the design of cyclic constant dimension codes that achieve the dual goals: maximizing code size while maintaining optimal minimum distance. We first explore a new form of Sidon spaces and employ this new form to construct several families of cyclic subspace codes. Our codes have optimal minimum distances and have more codewords than previous constructions in the literature.

Gated Attention Coding for Training High-Performance and Efficient Spiking Neural Networks

Spiking neural networks (SNNs) are emerging as an energy-efficient alternative to traditional artificial neural networks (ANNs) due to their unique spike-based event-driven nature. Coding is crucial in SNNs as it converts external input stimuli into spatio-temporal feature sequences. However, most existing deep SNNs rely on direct coding that generates powerless spike representation and lacks the temporal dynamics inherent in human vision. Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that leverages the multi-dimensional gated attention unit to efficiently encode inputs into powerful representations before feeding them into the SNN architecture. GAC functions as a preprocessing layer that does not disrupt the spike-driven nature of the SNN, making it amenable to efficient neuromorphic hardware implementation with minimal modifications. Through an observer model theoretical analysis, we demonstrate GAC's attention mechanism improves temporal dynamics and coding efficiency. Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency. Notably, we improve top-1 accuracy by 3.10% on CIFAR100 with only 6-time steps and 1.07% on ImageNet while reducing energy usage to 66.9% of the previous works. To our best knowledge, it is the first time to explore the attention-based dynamic coding scheme in deep SNNs, with exceptional effectiveness and efficiency on large-scale datasets. Code is available at https://github.com/bollossom/GAC.

Improving Data Delivery in Unreliable Networks Using Network Coding and Ant-Colony Optimization

Wireless Sensor Networks (WSNs) comprise interconnected wireless nodes that receive and transmit data across applications and platforms. This paper addressed the problem of link failures in WSNs that potentially could lead to the loss of data packets while still in transit. This was achieved through the use of network coding which is known to address capacity bottleneck problems in WSNs. In particular, a technique called Ant Agent-Assisted Network Coding (AAANC) is proposed that employs the ant colony optimization technique in addition to network coding operations. The main aim of AAANC is to facilitate the successful delivery and decoding of coded data packets in the presence of link failures. AAANC employs a packet route selection technique that is inspired by the social behavior of natural ants. For natural ants, a strong pheromone trail along a path indicates a promising route to a food source, and this is analogous to a reliable communication link for routing data packets in this paper. Through simulations, AAANC was compared to diagonal pseudorandom network coding (DNC) and triangular pseudorandom network coding (TNC), and it proved to have a superior performance in terms of packet delivery ratio and number of decoded packets. Significant performance gain can be achieved if AAANC algorithm is made to dynamically adapt the ant colony and network coding parameters in response to traffic changes.

Impact of Network Coding on Age of Information

Impact of Network Coding on Age of Information

An Explicit Data-Driven Model for Estimation of Free and Absorbed Gas Volumes in Shale Gas Reservoirs.

Shale gas reservoir fluid flow is characterized by complex interactions between a number of variables, including stress sensitivity, matrix shrinkage, and critical desorption pressure. The behavior and productivity of shale gas reservoirs are significantly influenced by these variables. Therefore, in this study, an in-house finite element poro-elastic simulator was integrated with an in-house Fortran-coded artificial neural network model for accurate estimation of free and absorbed gas volumes in shale gas reservoirs, taking into account the combined effects of stress sensitivity, matrix shrinkage, and critical desorption pressure. The in-house finite element simulator was used to create a database of free and absorbed gas volumes under different reservoir, fluid, and geo-mechanical characteristics of shale gas, while the in-house neural network code was utilized using the outcomes of the poro-elastic simulator in order to propose an explicit data-driven mathematical formula of free and absorbed volumes of shale gas. The study findings indicate that the neural network model achieved high accuracy with an R-squared value exceeding 99% during both the training and blind-testing phases. The newly developed ANN-based correlations can be used as a quick and easy-to-use tool to forecast the volume of shale gas in place both free and adsorbed without any prior experience with utilizing or implementing machine learning models.

Heralded quantum network coding of multi-particle states based on quantum time-bin multiplexing

Quantum network coding can effectively alleviate bottlenecks in quantum networks thus improving the transmission efficiency and the throughput of the quantum network. Although various quantum network coding protocols have been constructed, most of them focus mainly on addressing the communication congestion issue of simultaneously transmitting single-qubit states via the bottleneck channel in quantum networks. In this paper, a protocol for the quantum two-pair unicast transmission of multi-particle states using quantum time-bin multiplexing is proposed. Parallel entanglement is established in a heralded way between two-dimensional quantum memories (QMs) of intermediate nodes by transmitting a high-dimensional encoded single photon via the bottleneck channel. Then the quantum unicast transmission of multi-particle states can be achieved by using quantum teleportation and the resultant entangled pairs. Additionally, a security enhanced protocol is presented when dishonest intermediate nodes or outside eavesdroppers are taken into account. No alternative measurements are needed to check security. This makes the present protocol cost-effective and efficient. Compared with existing methods, no pre-shared entangled states are required. This provides a new perspective for the development of quantum networks.

Visual Communication Design Method in Folk Art Based on Multimedia Data Transmission Technology

With the rapid development of science and technology and China's economy, the internet, big data, computers, and multimedia technology are widely used in all walks of life to promote the application and improvement of visual communication design concepts, the gradual implementation of multimedia technology, and the continuous improvement of innovation. In this paper, from the multimedia transmission technology based on network coding, some key technologies in the network communication of data and video streams are studied in depth and, based on the transmission-quality assessment model, the working effect of the jitter-buffering algorithm and WebRTC jitter-buffering algorithm is compared in different network environments. The experimental results show that the jitter-buffering algorithm proposed in this paper has better working effect. This research is of great significance for the realization of multimedia transmission technology in next-generation networks.

Novel compressed linear network coding vectors for multihop communication networks

Novel compressed linear network coding vectors for multihop communication networks

Quality Enhancement with Frame-wise DLCNN using High Efficiency Video Coding in 5G Networks

In the present situation, applications related to multimedia are discovered to be comfortable with the use of video. The number of end consumers who use video continues to rise every day. People are presently searching for videos with better quality among the ones that are currently available there. This results in the launch and dissemination of HD (high definition) videos. Ultra high-definition (UHD) videos are becoming more and more popular as a result of this advancement and need. However, as video communication keeps expanding, there is an upsurge in network traffic because of the limited bandwidth, especially among smart cities. Different advancement codecs have been suggested to deal with the data stream to overcome this hazardous circumstance. However, the fact that modern UHD videos have huge amounts of data makes the available codecs even more complicated. UHD videos can be processed with the latest improvement codec, H.265/High-efficiency video coding (HEVC). Nevertheless, it is impacted by increased power consumption and intricate calculations. Limitations in the codec's functionality confine its use to specific applications, preventing its application in wireless, mobile, or portable settings. Hence, this research concentrates on implementing frame-level quality enhancement through a deep learning network known as FQE-Net. The deep learning convolutional neural network (DLCNN) is specifically crafted to manage films with resolutions up to 16K. Its primary objectives include reducing complexity, minimizing artifacts, enhancing the efficiency of the HEVC codec, and compacting energy consumption. To achieve superior efficiency, it is imperative to replace the DWT transforms within the HEVC codec with a DLCNN model. Additionally, incorporating the Content Block Search Algorithm for Motion Estimation and Compensation, alongside filtering techniques like Sample Adaptive Filter and Deblocking Filter, becomes essential. The simulation results showed that the suggested FQE-Net performed better than the conventional techniques.

Adaptive Combined Channel Network Coding for Cognitive Radio Networks with Cooperative Relay

Cognitive radio is one of the new technologies for 4G/5G applications, so cooperative relay communication and network coding are considered as certain methods to help improve their re-spective applications. A primary broadcast system for multimedia video streaming applications that broad¬casts data to primary users and an auxiliary cooperative relay secondary cognitive ra-dio system are considered. The secondary cooperative overlay system can use multiple error control coding methods for point-to-point data retransmission, such as channel coding, network coding, and combi¬ned coding methods to improve system performance under variable link con-ditions. A new technique of adaptive coding of AC2NC combined channel network for data re-transmission is proposed. The new AC2NC first analyzes the channel feedback information and then selects the best retransmission coding technique based on the target bandwidth or trans-mission time optimization. This is instead of using a single static channel or a network coding technique with dynamic channel conditions. The proposed AC2NC improves system through-put, reduces retransmission time, and provides more spectrum access opportunities for the sec-ondary sys¬tem's own data transmissions. AC2NC's relative throughput and time saving capabil-ities for cognitive radio users have been shown to exceed ninety percent, under certain channel conditions, compared to some static coding methods.

A Novel Hybrid Communication Policy Using Network Coding Based Post-Quantum Cryptography and Adaptive Neuro Fuzzy Inference System

A Novel Hybrid Communication Policy Using Network Coding Based Post-Quantum Cryptography and Adaptive Neuro Fuzzy Inference System

Optimization of tensor network codes with reinforcement learning

Tensor network codes enable structured construction and manipulation of stabilizer codes out of small seed codes. Here, we apply reinforcement learning (RL) to tensor network code geometries and demonstrate how optimal stabilizer codes can be found. Using the projective simulation framework, our RL agent consistently finds the best possible codes given an environment and set of allowed actions, including for codes with more than one logical qubit. The agent also consistently outperforms a random search, for example finding an optimal code with a 10% frequency after 1000 trials, vs a theoretical 0.16% from random search, an improvement by a factor of 65.

Jointly optimized quasi-cyclic LDPC codes for coded-cooperative wireless networks based on split labeling diversity

Jointly optimized quasi-cyclic LDPC codes for coded-cooperative wireless networks based on split labeling diversity

The effect of Fault Ride-Through requirements on voltage dips and post-fault voltage recovery in a Dutch Distribution Network

In this paper the possibility to use Decentralized Generation (DG) units for voltage support in Distribution Networks during and after a Short Circuit (S/C) event is discussed. Two types of DG units will be examined, Combined Heat-Power (CHP) plants and Doubly-Fed Induction Generators (DFIG). Earlier approaches would oblige the disconnection of these units during grid disturbances, in order to avoid unwanted conditions such as interference with the protection system and islanding [1]. Newer grid codes for Transmission Networks like [2], taking into consideration the increased share of these units in energy production have set specific Fault Ride-Through (FRT) criteria which oblige them to stay connected during SC events when they are combined to large plants directly connected to the transmission grid, such as (off shore) wind farms.. If ever more DG is connected directly to distribution grids, the same reasoning leading to applying rault ride through criteria to DG connected in large groups to transmission grids, may be applied to DG connected to distribution grids, as a loss of a a large amount of DG connected to distribution grids has the same adverse effect as using DG connected directly to the transmission grid. In order to examine the effects of applying FRT criteria to DG connected to the distribution grid, simulations with a detailed modeled network for different types of outages were carried out. In the simulations, the application of FRT criteria currently only used for DG connected to the transmission grid is discussed. From the specific test case, which corresponds to a strong network with extended cable connections and locally high penetration level of DG in distribution system it was shown that application of strict fault ride-through criteria combined with active voltage support is not necessary in case of DG connected to distribution networks.

Retracted: Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles

Retracted: Using a Solution Construction Algorithm for Cyclic Shift Network Coding under Multicast Network to the Transformation of Musical Performance Styles

Adaptive Coding and Modulation-Aided Mobile Relaying for Millimeter-Wave Flying Ad Hoc Networks

Adaptive Coding and Modulation-Aided Mobile Relaying for Millimeter-Wave Flying Ad Hoc Networks