Tunable sparse network coding

The network coding capacity of a single multicast traffic is characterized by the min-cut/max-flow (mcMF) theorem, which can be achieved by random linear network coding (RLNC). Nonetheless, the graph-theoretic characterization for multiple unicast/multicast traffic remains an open problem. This paper proposes and studies a new class of intersession-network-coding schemes: RLNC with selective cancelling (SC), which inherits the complexity advantage of RLNC once the set of selective cancelling edges is decided. A graph-theoretic characterization is provided for the achievable rates of RLNC with SC for the general multiple multicast setting. The findings contain most existing achievability results as special cases, including the mcMF theorem of the single multicast traffic and the existing characterization of pairwise intersession network coding. One prominent feature of the proposed approach is its focus on the achievability analysis with arbitrary network topology, arbitrary inter-session packet-mixing capability, and arbitrary traffic demands, which distinguishes the results from the special case analysis, capacity outer bound constructions, and the pattern-based (butterfly-based) superposition arguments.

Passive network tomography uses end-to-end observations of network communication to characterize the network, for instance to estimate the network topology and to localize random or adversarial glitches. Under the setting of linear network coding this work provides a comprehensive study of passive network tomography in the presence of network (random or adversarial) glitches. To be concrete, this work is developed along two directions: 1. Tomographic upper and lower bounds (i.e., the most adverse conditions in each problem setting under which network tomography is possible, and corresponding schemes (computationally efficient, if possible) that achieve this performance) are presented for random linear network coding (RLNC). We consider RLNC designed with common randomness, i.e., the receiver knows the random code-books all nodes. (To justify this, we show an upper bound for the problem of topology estimation in networks using RLNC without common randomness.) In this setting we present the first set of algorithms that characterize the network topology exactly. Our algorithm for topology estimation with random network errors has time complexity that is polynomial in network parameters. For the problem of network error localization given the topology information, we present the first computationally tractable algorithm to localize random errors, and prove it is computationally intractable to localize adversarial errors. 2. New network coding schemes are designed that improve the tomographic performance of RLNC while maintaining the desirable low-complexity, throughput-optimal, distributed linear network coding properties of RLNC. In particular, we design network codes based on Reed-Solomon codes so that a maximal number of adversarial errors can be localized in a computationally efficient manner even without the information of network topology.

he usage of random linear network coding in wireless sensor network is consistent with sociality, for this feature, proposed a network coding scheme for sociality for wireless sensor networks. Analyzed the process of coding and decoding of random linear network coding and summarized the advantages and disadvantages of the scheme in practical application, at last through NS2 to compare the scheme of using random linear network coding and traditional coding, the random linear network is better at network performance.

Random linear network coding (RLNC) has the potential to improve the performance of current and future Internet of Things (IoT) communication systems, but is computationally demanding due to matrix multiplications and inversions. Some single-core RLNC implementations achieve already sufficient coding speeds for contemporary multimedia streaming formats. However, advances in multimedia streaming formats and IoT applications will require the exploitation of heterogeneous multicore architectures, which are becoming common for a wide range of IoT nodes, including smartphones. In this paper, we introduce and evaluate efficient RLNC computing strategies for IoT node architectures, including the emerging heterogeneous big.LITTLE multicore architectures with multiple big (fast) cores and multiple LITTLE (slow) cores. In contrast to existing RLNC implementation strategies, we build on and adapt highly optimized dense matrix operations from the high performance computing field to RLNC on heterogeneous multicore IoT nodes. Our approach includes the optimization of RLNC matrix operations through optimized operations on matrix blocks with single instruction multiple data instructions. We schedule block operations on the heterogeneous cores through a directed acyclic graph that avoids artificial synchronization points while ensuring the data dependencies. We examine priority scheduling according to the number of outgoing dependencies of a task and data locality of cached blocks. Our extensive measurements with several heterogeneous big.LITTLE multicore IoT node and smartphone processor boards demonstrate higher RLNC encoding and decoding throughputs than existing approaches. Moreover, our measurements indicate that the utilization of more cores decreases energy consumption, which is an important goal for IoT nodes.

Random linear network coding with ladder-shaped global coding matrix for robust video transmission

Network error localizations uses end-to-end observations to localize random or adversarial glitches. For error localization under random linear network codes (RLNCs) the schemes proposed in previous literatures require the priori knowledge of the network topology. Moreover, adversarial error localization is proved computationally intractable under RLNCs. The goal of current paper is designing new network coding schemes to improve the error localization performance of RLNCs while maintaining the key advantages of RLNCs. To be concrete, we introduce Network Reed-Solomon codes (NRSCs), which have the following features: 1. NRSCs are low-complexity distributed linear network codes. 2. NRSCs achieve the network multicast capacity with high probability. 3. In adversarially faulty networks NRSCs enable the receiver to locate a maximum number of adversarial errors in a computationally efficient manner. Moreover, the error localization schemes under NRSCs do not need the priori knowledge of the network topology and thus are robust against dynamic network updating.

This technical summary provides an exploration of go-layer community Coding (CLNC) algorithms in wireless systems. CLNC is a coding technique whereby impartial packet transmissions from distinctive layers of the protocol stack are combined to create an unmarried packet. This is conceptually extraordinary from conventional community coding (NC), which handiest combines distinctive packets within the identical layer of the stack. CLNC aims to improve the efficiency of Wi-Fi transmission with the aid of lowering the overhead for retransmissions and, as a result, the electricity consumption of the gadget. The authors of this summary analyzed the performance of CLNC algorithms in IEEE 802.11 Wi -Fi structures. They studied unique CLNC algorithms: fountain coding and random linear network coding. Through their simulations, the authors determined that CLNC algorithms lessen the possible throughput and also reduce the robustness of the machine to packet losses. This summary introduces a theoretical framework for comparing the performance of CLNC algorithms in wireless systems. Primarily based on their findings, the authors advise that CLNC can be used to enhance the transmission performance of Wi-Fi hyperlinks, at the same time also supplying an extra layer of safety against packet losses. However, in addition research is needed to fully compare the trade-offs among performance and robustness for specific CLNC algorithms and specific Wi-Fi requirements.

We investigate the application of network coding to file transfers from a single source to multiple destinations in a two-hop ad hoc relay network that consists of half-duplex packet radios. Our network-coded file distribution techniques make simultaneous use of a pair of relay nodes. All transmissions by the source and the relay nodes employ packet-by-packet adaptive modulation and channel coding to combat the effects of fading. Two types of network coding are examined and compared, fountain coding and random linear network coding.

A typical intelligent factory, a smart home or a smart city consists of several nodes or machines. In a typical Machine to Machine Communication mode (M2M), each machine senses some information (pressure, temperature, presence of person, etc.) and then communicates this information to other machines. This communication could be unicast or multicast depending on the application. Further, the destination node of the M2M communication is many-a-times not in the direct communication range. In the presence of tens and hundreds of machines forming a network, it is essential to have an efficient communication mechanism for multihop multicast transmission between different machines. This paper proposes a network coding (NC) based multihop multicast communication for different topologies - butterfly, flattened butterfly, hypercube and folded-clos model. It is observed that random linear network coding (RLNC) technique outperforms best routing protocol for different topologies, by requiring less time for data transmission, reducing the time required by upto 50%, depending on the model. Further, in order to improve the performance of network coding for M2M in a disconnected network; an integrated network coding and routing approach is proposed and evaluated. It is observed that such an integrated mechanism requires upto 25% less time than pure routing and upto 10% less time than required by RLNC.

In a network application, channel coding alone is not sufficient to reliably transmit a message of finite length K from a source to one or more destinations as in, e.g., file transfer. To ensure that no data is lost, it must be combined with rateless erasure correcting schemes on a higher layer, such as a time-division multiple access (TDMA) system paired with automatic repeat request (ARQ) or random linear network coding (RLNC). We consider binary channel coding on a binary symmetric channel (BSC) and q-ary RLNC for erasure correction in a star network, where Y sources send messages to each other with the help of a central relay. In this scenario RLNC has been shown to have a throughput advantage over TDMA schemes as K→∞ and q→∞. In this paper we focus on finite block lengths and compare the expected throughputs of RLNC and TDMA. For a total message length of K bits, which can be subdivided into blocks of smaller size prior to channel coding, we obtain the channel code rate and the number of blocks that maximize the expected throughput of both RLNC and TDMA, and we find that TDMA is more throughput-efficient for small message lengths K and small q.

Network coding is a promising technology which allows coding of multiple packets in routers. Random linear network coding (RLNC), a distributed network coding scheme, insists each node to forward independent random linear combinations of incoming packets through its outgoing links. RLNC appends a header which contains the information about how the linear combinations are taken. We observe that some additional flexibility can be induced in RLNC header with minor modification in decoding. Our modification does not make any increase in computational complexity. We also demonstrate some applications of it in secure network coding and error correction.

In this paper we propose random linear network coding (RLNC) as an efficient encoding scheme for unicast flows in single-hop ad hoc network and multi hop wireless mesh networks (WMN). The traditional approach for encoding unicast flows, known as inter-flow network coding (NC), assumes instant decoding and assumption of reliable feedback channel to minimizes the total number of transmissions. But under the practical consideration of the wireless medium access control (MAC), the gain of inter-flow NC technique suffers from higher latency due to the MAC overhead. We validate the result of our work on TelosB motes testbed. Our testbed implementation shows that the proposed approach of using RLNC reduces the transmission completion time by up to 40% compared to the inter-flow NC scheme for single-hop data dissemination and multi-hop wireless routing problems.

네트워크 코딩(Network Coding)은 통신망의 성능 향상에 도움을 줄 수 있으나 이의 소프트웨어적 구현은 부호화/복호화 단계에서 매우 큰 지연시간을 유발할 수 있어 이를 줄일 수 있는 병렬화된 구현이 필수적이라 할 수 있다. 본 논문에서는 랜덤 리니어 네트워크 코딩(Random Linear Network Coding)과 랜덤 리니어 네트워크 코딩의 단점을 보완하고자 최근 제안된 파이프라인 네트워크 코딩(Pipeline Network Coding)의 병렬처리 성능을 비교한다. 또한, 네트워크 코딩의 CPU에서의 병렬처리 기법과 GPGPU(General Purpose Graphics Processing Units)에서의 병렬처리 기법을 비교하여 네트워크 코딩의 사용 시 그 파라미터에 따라 적절한 병렬처리 기법을 선택할 필요성이 있음을 보여준다. Network coding has been shown to improve various performance metrics in network systems. However, if network coding is implemented as software a huge time delay may be incurred at encoding/decoding stage so it is imperative for network coding to be parallelized to reduce time delay when encoding/decoding. In this paper, we compare the performance of parallelized decoders for random linear network coding (RLC) and pipeline network coding (PNC), a recent development in order to alleviate problems of RLC. We also compare multi-threaded algorithms on multi-core CPUs and massively parallelized algorithms on GPGPU for PNC/RLC.

This paper addresses the issue of throughput-delay of one-to-many wireless multi-hop flows based on random linear network coding (RLNC). Existing research results have been focusing on the single-hop model which is not suitable for wireless multi-hop networks. In addition, the conditions of related system model are too idealistic. To address these limitations, we herein investigate the performance of a wireless multi-hop network, focusing on the one-to-many flows. Firstly, a system model with multi-hop delay was constructed; secondly, the transmission schemes of system model were gradually improved in terms of practical conditions such as limited queue length and asynchronous forwarding way; thirdly, the mean delay and the mean throughput were quantified in terms of coding window size if and number of destination nodes N for the wireless multi-hop transmission. Our findings show a clear relationship between the multi-hop transmission performance and the network coding parameters. This study results will contribute significantly to the evaluation and the optimization of network coding method.

In order to improve the throughput and transmission reliability in wireless sensor network with limited resources, multicast network coding technology is imported in. On this basis, through random linear network coding by nodes' cooperation to make improvement in delayed and expense. The simulation results show that the transmission, to the opportunity routing, by cooperation network coding strategies is improved in making full use of space-time reusability, reducing the delay and enhancing the throughput and transmission reliability of wireless sensor network.