A Novel Approach of Network Coding in Wireless Networks Overhearing

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.

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.

Network coding is a technology with great potentiality in improving wireless multi-hop network throughput. And some routings combined with network coding have been proposed for wireless multi-hop network. In the routings combined with network coding, network coding condition is used to find network coding opportunities, which is very important to the performance improving of routings combined with network coding. However, the network coding conditions defined by existing routings combined with network coding unilaterally focus on the growth of network coding opportunities, which results in the increasing of cost and delay in routing discovery. Moreover, the more flows participating network coding, the higher probability of decoding failure, which degrade the network coding gain. In this paper, low complexity network coding condition (LCNC) is proposed. LCNC only detects two-flows network coding opportunity, and ensures the destination make successful decoding. In addition, the actual network coding gain is designed through analysis of link quality. Network coding opportunity will be ignored in case the actual network coding gain is negative. Based on LCNC, a low complexity network coding aware routing (LNCR) is designed aiming wireless multi-hop network. Simulation results demonstrate that LNCR obtain a balance between network coding gain and routing discovery cost, and can obtain higher network throughput when the network load is high compared with the classical network coding aware routings.

The streaming of real-time audio/video data is very challenging because of the time-varying and unreliable wireless channels, video content characteristics, limited bandwidth, dynamic topology, heterogeneous and distributed environment and high packet loss rate because of wireless interference and channel fading. Because of such open issues present in wireless networks, it is very difficult to satisfy the requirements of audio/video streaming applications such as low delay, low packet loss, jitter control etc. In such scenarios of opportunistic networks where real time data is being streamed from server node to client node, Network Coding and its variants can be used by such nodes to meet different requirements. Network coding changes the role of such multimedia nodes from store and forward to encode data packets. Encoding process includes mathematical operations on data packets. This survey paper has considered number of speculative and practical approaches and cases where network coding or its variant applied either fully or partially on multimedia traffic with the aim to improve performance and to provide protection against packet losses. This review has mainly focused on the opportunity of performance enhancement of MPEG-4 traffic over Wireless Network using Random Linear Network Coding (RLNC) with Multi Generation Mixing (MGM). Using Multi Generation Mixing, packets of greater importance has got more protection, less loss, more reconstruction and recovery of real time data.

Nowadays network coding has attracted a lot of attentions from researchers in fields of information and coding theory, wireless communications, P2P, etc, as it has enormous potential to improve network throughput and to utilize the maximum bandwidth in networks. Whereas due to its high computation complexity, it is not easy to implement network coding in a real network environment. Network Flow Processor (NFP) is a multi-core specific processor, widely used in switches, routers. NPF has a powerful computation capacity, since it is composed of 40 cores and each core can concurrently executes up to 8 threads. In this paper, we discuss about the network coding fundamental and the NFP architecture in order to make NFP support the encoding function of network coding. During the implementation of network coding on NFP, we use loop-based and table-based approaches for multiplication in finite field. With our experimental results, a single card of NFP can offer a throughput of encoding up to 43.9Mbps.

Multi-Generation Mixing (MGM) is a generalized approach of Network Coding (NC) that has its improvements when applied in packet loss networks. In this paper we apply MGM network coding in networks communicating video contents. NC has shown viable improvements when applied in packet loss networks. With practical network coding (generation based network coding), packets are grouped in chunks called generations. Generation is the unit of network coding encoding and decoding. The generation grouping of packets is necessary for the practical deployment of network coding. On the other hand it increases the cost of NC losses. NC losses reduce the ability of receiver to decode packets, and hence can severely degrade the quality of recovered video. Video is encoded in layers with Scalable Video Coding (SVC) a base layer and one or more enhancement layers. MGM employs the layering of scalable video to enhance the reliability of communication. MGM provides different levels of reliable communication for the different video layers to improve the overall reliability of video communication. SVC enhancement layers are dependent on lower layers to be recovered. With MGM enhancement layers support the recovery of lower layers. This is done by network encoding lower video layers in higher layers. Through extensive simulations, we show that MGM highly improves the quality of recovered video.

Linear network coding is a promising technology that can maximize the throughput capacity of communication network. Despite this salient feature, there are still many challenges to be addressed, and security is clearly one of the most important challenges. In this paper, we will address the design of secure linear network coding. Specifically, we will investigate the network coding design that can both satisfy the weakly secure requirements and maximize the transmission data rate of multiple unicast streams between the same source and destination pair, which has not been addressed in the literature. In our study, we first prove that the secure unicast routing problem is equivalent to a constrained link-disjoint path problem. We then develop efficient algorithm that can find the optimal unicast topology in a polynomial amount of time. Based on the topology, we design deterministic linear network code that is weakly secure and can be constructed at the source node. And finally, we investigate the potential of random linear code for weakly secure unicast and prove the low bound of the probability that a random linear code is weakly secure.

Chapter 1 - An Introduction to Network Coding

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.

Cooperative transmission is an emerging communication technique that takes advantage of the broadcast nature of wireless channels. However, due to low spectral efficiency and the requirement of orthogonal channels, its potential for use in future wireless networks is limited. In this paper, by making use of multiuser detection (MUD) and network coding, cooperative transmission protocols with high spectral efficiency, diversity order, and coding gain are developed. Compared with the traditional cooperative transmission protocols with singleuser detection, in which the diversity gain is only for one source user, the proposed MUD cooperative transmission protocols have the merit that the improvement of one user's link can also benefit the other users. In addition, using MUD at the relay provides an environment in which network coding can be employed. The coding gain and high diversity order can be obtained by fully utilizing the link between the relay and the destination. From the analysis and simulation results, it is seen that the proposed protocols achieve higher diversity gain, better asymptotic efficiency, and lower bit error rate, compared to traditional MUD schemes and to existing cooperative transmission protocols. From the simulation results, the performance of the proposed scheme is near optimal as the performance gap is 0.12dB for average bit error rate (BER) 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-6</sup> and 1.04dB for average BER 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-3</sup> , compared to two performance upper bounds.

Network Coding has attracted significant attention in recent years, and it has been widely applied in broadcast, multicast and unicast. Nevertheless, the study on network coding in convergecast is still in the theoretical analysis and the simulation stage. In order to investigate the network coding gain of convergecast in realistic wireless sensor networks, a network coding-based convergecast scheme is realized in this paper. The experiment is conducted in the basic size of convergecast whose size is limited below four, which is for the purpose of eliminating as much interference from the large size of convergecast network as possible. Performed in traditional convergecast scheme and network coding convergecast scheme, the experiment on collection rate and energy consumption are evaluated under different link delivery ratios and scales of convergecast network. The experiment results show that network coding is certain to introduce benefit in the case of poor link equality. However, it is inferior to the traditional scheme when the wireless link delivery ratio is high enough due to the conscious overhearing and the prerequisite of successful decoding.

This paper extends the part of the theory on linear network coding (S.-Y.R. Li et al., 2003) over an acyclic network. The extension also applies to "static linear network code" that is introduced by R. Koetter and M. Medard, (2003). Another objective is the rigorous clarification of fundamental concepts in linear network coding

In communications networks, the capacity region of multisource network coding is given in terms of the set of entropy functions Gamma*. More broadly, determination of Gamma* would have an impact on converse theorems for multi-terminal problems in information theory. This paper provides several new dualities between entropy functions and network codes. Given a function g ges 0 defined on all subsets of N random variables, we provide a construction for a network multicast problem which is ldquosolvablerdquo if and only if g is the entropy function of a set of quasi-uniform random variables. The underlying network topology is fixed and the multicast problem depends on g only through link capacities and source rates. A corresponding duality is developed for linear network codes, where the constructed multicast problem is linearly solvable if and only if g is linear group characterizable. Relaxing the requirement that the domain of g be subsets of random variables, we obtain a similar duality between polymatroids and the linear programming bound. These duality results provide an alternative proof of the insufficiency of linear (and abelian) network codes, and demonstrate the utility of non-Shannon inequalities to tighten outer bounds on network coding capacity regions.

Grassmannian codes are known to be useful in error correction for random network coding. Recently, they were used to prove that vector network codes outperform scalar linear network codes, on multicast networks, with respect to the alphabet size. The multicast networks which were used for this purpose are generalized combination networks. In both the scalar and the vector network coding solutions, the subspace distance is used as the distance measure for the codes which solve the network coding problem in the generalized combination networks. In this paper, we show that the subspace distance can be replaced with two other possible distance measures which generalize the subspace distance. These two distance measures are shown to be equivalent under an orthogonal transformation. It is proved that the Grassmannian codes with the new distance measures generalize the Grassmannian codes with the subspace distance and the subspace designs with the strength of the design. Furthermore, optimal Grassmannian codes with the new distance measures have minimal requirements for the network coding solutions of some generalized combination networks. The coding problems related to these two distance measures, especially with respect to network coding, are discussed. Finally, by using these new concepts, it is proved that the codes in the Hamming scheme form a subfamily of the Grassmannian codes.

Future networks are expected to depart from traditional routing schemes in order to embrace network coding (NC)-based schemes. These have created a lot of interest both in academia and industry in recent years. Under the NC paradigm, symbols are transported through the network by combining several information streams originating from the same or different sources.This special issue contains thirteen papers, some dealing with design aspects of NC and related concepts (e.g., fountain codes) and some showcasing the application of NC to new services and technologies, such as data multi-view streaming of video or underwater sensor networks. One can find papers that show how NC turns data transmission more robust to packet losses, faster to decode, and more resilient to network changes, such as dynamic topologies and different user options, and how NC can improve the overall throughput. This issue also includes papers showing that NC principles can be used at different layers of the networks (including the physical layer) and how the same fundamental principles can lead to new distributed storage systems. Some of the papers in this issue have a theoretical nature, including code design, while others describe hardware testbeds and prototypes.