Random Network Coding Research Articles

Promenade: Proportionally Fair Multipath Rate Control in Datacenter Networks with Random Network Coding

In today's datacenter topologies, there exist multiple equal-cost paths between each pair of communicating virtual machines. Yet, splitting flows and routing them along multiple paths may lead to packet reordering, which may affect the performance of TCP. In this paper, we propose Promenade, a new protocol that uses random network coding to mitigate the negative effects of packet reordering, while at the same time achieving weighted proportional fairness in bandwidth allocation across different tenants. To achieve weighted proportional fairness when allocating bandwidth to tenants, the problem of rate control is formulated as a convex optimization problem, and Promenade uses its distributed solution as a theoretical foundation to design its bandwidth allocation protocol. With our real-world implementation of Promenade in the Mininet testbed, we are able to show that Promenade is able to achieve weighted proportional fairness in its rate control when individual flows are split into multiple paths.

Improvising Reliability and Security in Multiple Relay Network using Optimal Scheduling

In the real-time scenario involving wireless sensor networks, the data forwarding and data gathering procedures are taking place from the remote environment. With the involvement of heterogeneous architecture and multi-hop data transmission paths, there lies a serious threat for secured data communication. There may be chances of data attacks either from the inside intruder or from the external intruder. The problem of data flow attack by adding malicious information, viz. Data injection attack and outside arbitrary attack, viz. Byzantine attacks are found to be more dangerous and cause vulnerability for the wireless sensor network. So improving the reliability and security in multi-relay networks is very much essential. In this work, the practical approach of detecting data injection and Byzantine attacks using the proposed method of random network coding is performed. Then, as improvisation measure, the priority scheduling algorithm is implemented to effectively schedule the data transfer. Real-time packets with highest priority in the distribution queue are placed first in the processing mechanism. The remaining packets are arranged based on the position of the sensor nodes and are placed in separate queues. Least priority packets can obstruct the dispensation of their direct higher precedence packets after waitlisted for a certain number of time frames. Simulation results using the NS2 environment show that using the priority scheduling algorithm has good performance values in terms of the packet delivery ratio, throughput and delay. Also, the attack detection metrics such as false positive ratio and detection ratio are also improved when using the priority scheduling algorithm. Thus an improvised priority algorithm for an uplink scheduler in WSN is implemented to increase the performance and detection metrics.

Resource Tuned Optimal Random Network Coding for Single Hop Multicast future 5G Networks

Optimal random network coding is reduced complexity in computation of coding coefficients, computation of encoded packets and coefficients are such that minimal transmission bandwidth is enough to transmit coding coefficient to the destinations and decoding process can be carried out as soon as encoded packets are started being received at the destination and decoding process has lower computational complexity. But in traditional random network coding, decoding process is possible only after receiving all encoded packets at receiving nodes. Optimal random network coding also reduces the cost of computation. In this research work, coding coefficient matrix size is determined by the size of layers which defines the number of symbols or packets being involved in coding process. Coding coefficient matrix elements are defined such that it has minimal operations of addition and multiplication during coding and decoding process reducing computational complexity by introducing sparseness in coding coefficients and partial decoding is also possible with the given coding coefficient matrix with systematic sparseness in coding coefficients resulting lower triangular coding coefficients matrix. For the optimal utility of computational resources, depending upon the computational resources unoccupied such as memory available resources budget tuned windowing size is used to define the size of the coefficient matrix.

Grassmannian Codes With New Distance Measures for Network Coding

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.

An enhanced heuristic XoR network coding-based method for high quality video streaming over VANETs.

One of the most important challenges in live video streaming in mobile vehicular networks is the optimal use of broadband and point-to-point packet delay. Recent studies show that the sheer use of frames flow compression methods (such as H.264 or HEVC) and the proper communication overlay, such as Peer-to-Peer (P2P), has no absolute influence on increasing the quality of received video in VANET networks. Therefore, the use of an appropriate data exchange method, such as network coding, seems to be of great importance. Compared to Random Network Coding (RNC), XoR Network Coding (XNC) method has the least computational load for the network which is an important factor in optimal use of limited energy of nodes in a wireless network. The basic problem in XNC is that when a node is supposed to combine several frames and transmit them through an encoded frame, how this combination could be made to enable other nodes of the network to be broadcasted through receiving this packet and how can the available packets in their buffers decode as well as extract the largest number of frames in order to experience a higher video quality. To fulfil this aim, an encoding intelligent method is required which is based on the buffers’ status of neighbours. In the proposed method in this article, the best frame combination is reached through buffers status of neighbours and AHP methods or AHP-TOPSIS methods, and the encoded frames are broadcasted through XNC. Simulation results show that due to the reduction in number of transmitted packets in the network, parameters such as congestion and point-to-point delay are significantly reduced and vehicles experience a higher video quality compared with other similar methods.

On hybrid network coding for visual traffic surveillance

Abstract A large volume of data is generated by traffic surveillance devices such as cameras and sensors integrated into an intelligent transportation system (ITS), a subfield of the Internet of Things (IoT). We argue that network coding can be applied to leverage on an emerging fog architecture that relies on edge resources, to achieve higher throughput, saving up network bandwidth, and provide resilience to link failures, while also achieving simple obfuscation against wire-tapping attacks by linearly combining the source packets. There are two broad linear network coding paradigms in the literature — deterministic and random network coding, each with their own strengths and limitations. With the aid of software-defined network (SDN), we rethink about the possibility of applying a hybrid approach to deal with networks at different scales. Under network conditions that reflect expected network properties of an ITS, our simulation results show that the proposed hybrid approach performs better than other alternates.

Technical Accuracy Based on Efficiency Algorithm for Measuring Standing Wave Ratio in Wireless Sensor Network

In telecommunication, antenna impedance matching plays a crucial role in a power transmission and broadcasting system. The existing network analyzer is imprecise for antenna impedance matching. A simple and an effective new technique based on an efficiency algorithm, which uses a random network coding and a low-density parity-check code is proposed in order to measure the standing wave ratio of a broadcasting antenna system in a wireless sensor network. It is shown that the proposed technique maximizes the instant measurement accuracy of the broadcasting/transmitting power at the radio frequency antennas input. In addition, measurement and calculation results show the potentials of the proposed technical accuracy for Measuring Standing Wave Ratio.

Hybrid Multicast and Device-to-Device Communications Based on Adaptive Random Network Coding

Random network coding (RNC) is an efficient coding scheme to improve the performance of wireless multicast networks, on the premise that a receiver is able to collect a full set of network-coded packets. However, in resource (time, bandwidth, and so on) constrained communications, a receiver may only receive a partial set of network coded packets, leading to poor system performance. On the other hand, device-to-device (D2D) communications utilize user proximity and spatial diversity to improve the communication efficiency. In this paper, we propose a hybrid multicast and D2D transmission scheme based on adaptive RNC (ARNC) to increase network throughput under packet erasure channels. In the proposed scheme, the packet encoding structure is optimized adaptively according to the network status, such that even if only a partial set of the coded packets are received, the user equipments (UEs) can still decode useful information and regenerate new encoded packet for D2D communications. In particular, the multicast mode and D2D mode are switched dynamically during a scheduling session according to the status of each UE. Under this hybrid mode, we can effectively overcome the effect of erasure channel and improve the overall network throughput. By comparing our scheme with other scheduling methods, we provide simulation results to corroborate the effectiveness of the proposed techniques.

A characterization of cyclic subspace codes via subspace polynomials

Subspace codes and particularly cyclic subspace codes have attracted a lot of research due to their applications in error correction for random network coding. In this paper, we explore the parameters of a class of the cyclic subspace codes whose subspace polynomials are more generalized. Therefore, some corollaries that extend the previously known results are obtained. Finally, a construction of k-dimensional cyclic subspace codes of size qN−1q−1 and minimal distance 2k−2 is provided.

Mobile Peer-to-Peer Assisted Coded Streaming

Current video streaming services use a conventional, client-server network topology that puts a heavy load on content servers. Previous work has shown that Peer-to-Peer (P2P) assisted streaming solutions can potentially reduce this load. However, implementing P2P-assisted streaming poses several challenges in modern networks. Users tend to stream videos on the go, using their mobile devices. This mobility makes the network difficult to orchestrate. Furthermore, peers have to contribute their storage to the network, which is challenging, since mobile devices have limited resources compared to desktop machines. In this paper, we introduce an analytical framework for mobile P2P-assisted streaming to estimate the server load that we define as the minimum required server upload rate. Using our framework, we evaluate four caching strategies: infinite cache as a baseline, first in first out (FIFO), random, and Random Linear Network Coded (RLNC) cache. We verify our analytical results with empirical data that was obtained by carrying out extensive measurements on our working P2P system. Our results show that when employing FIFO, random, and RLNC caching strategies, the server load converges to that of the infinite cache as the cache size increases. With a limit of 5 P2P connections per peer, we show that using the random caching, peers can store 40% fewer packets and still achieve the same benefit as with FIFO caching. When using the RLNC caching, it is enough to store 50% fewer packets to achieve the same benefit.

Subspace code based on flats in affine space over finite fields

Subspace code is very useful in the error correction for random network coding. In this paper, subspace code based on flats in affine space over finite fields is constructed. Sphere-packing bound, Wang–Xing–Safavi-Naini bound, anticode bound, Ahlswede–Aydinian bound and Gilbert–Varshamov bound for the size of code based on flats in affine space over finite fields are provided. Finally, we introduce another method of obtaining the anticode bound for the size of code based on flats in affine space from Erdős–Ko–Rado theorem.

Reliable and enhanced cooperative cross‐layer medium access control scheme for vehicular communication

In an unreliable cluster-based, broadcast vehicular network setting, the authors investigate the transmission reliability and throughput performance of random network coding (RNC) as a function of the percentage of packet generation rate and transmit power-to-noise ratio. In this study, a novel scheme called reliable and efficient cooperative cross-layer medium access control (RECMAC) is proposed. The proposed scheme consists of a source vehicle broadcasting packets to a set of receivers (i.e. one-to-many) over independent broadcast erasure channels. The source vehicle performs RNC on N packets and broadcasts the encoded message to a set of receivers. In each hop, several vehicles form a cluster and cooperatively transmit the encoded or re-encoded packet. The combination of the RNC, cluster based, and cooperative communications enables RECMAC to optimally minimise data redundancy, which means less overhead, and improve reliability as opposed to coding-based solutions. Theoretical analyses and simulation results show that under the same conditions RECMAC scheme can achieve improved performance in terms of transmission reliability and throughput.

Joint Power Allocation and Adaptive Random Network Coding in Wireless Multicast Networks

It is known that random network coding (RNC) can be used to improve the performance of wireless multicast networks. However, in the delay sensitive applications, no useful information can be recovered if a user cannot collect a full set of the encoded packets. This becomes more severe for multicast hard deadline constrained prioritized data because of the delivery time limitation and packet interdependency. Meanwhile, the performance of a multicast network is also limited by its bottleneck user(s) due to the heterogeneity of the underlying physical channels. Accordingly, we propose a cross-layer transmission scheme that utilizes beamforming at physical layer and adaptive RNC (ARNC) at network layer to maximize the overall network throughput. Under this joint-design, a smart antennas array that operates the beamforming is used to dynamically allocate the transmitting power among users, and the ARNC is adopted to achieve the network coding gain. In this way, the performance of each user is well balanced and the overall network throughput is increased observably. Furthermore, we propose a sample-based feedback scheme to reduce the system overhead. The analytical and simulation results are shown that the throughput of the network has increased by 30%~40% under our schemes compared with other RNC-based schemes.

The combination of sparse learning and list decoding of subspace codes for error correction in random network coding

The network coding can spread a single original error over the whole network. The simulation shows that the propagated error mostly all the time pollute just 100% of the received packets at the sink if Hamming distance is adopted. If subspace codes are adopted, usually the propagated error will not pollute 100% of the received packets in the sense of subspace distance. However, it also usually pollutes 90% of received packets which is a high error ratio. Even if the rank code and the subspace code are adopted, these existing schemes based on traditional block codes can correct corrupted errors no more than C/2 because of the limitation of the block coding where C is the max flow min cut. It is an agent to find a dense error correction method in random network coding. List decoding of subspace codes can correct frac{C}{k}hbox{-} 1 errors where k is the size of information. When frac{C}{k} is big, many errors in the sense of subspace distance can be corrected. However, the solution of list decoding is not unique. John Wright proposed a dense error correction technique based on L1 minimization, which can recover nearly 100% of the corrupted observations. In our proposal, the original packets are coded with John Wright’s coding matrix, and then, the coded message is coded again with subspace codes. In the sink, the decoding procedures about list decoding of subspace codes and John Wright’s scheme are performed. At last, the unique solution is achieved even though there are dense propagated errors in random network coding.

Packet Multicast in Cognitive Radio Ad Hoc Networks: A Method Based on Random Network Coding

This paper studies packet multicast technology in cognitive radio ad hoc networks (CRAHNs) by using a random network coding (RNC)-based approach. We first specify the key problem of packet multicast applications in CRAHNs, called multichannel single-hop wireless multicast problem (MCSHWMP), then present the definition of MCSHWMP of its four-element tuple model. Next, we propose a framework for the network coding-based scheme in packet multicast applications of CRAHNs and provide several candidate schemes based on the framework. The proposed schemes have several desirable features: 1) necessary packet transmissions are reduced greatly by exploiting the broadcast nature of wireless transmissions by using RNC; 2) each packet transmission is tried to maximize the packet reception gain of the receivers by considering opportunistic accessibility of wireless channels and heterogeneous packet reception success ratio of different wireless channels; and 3) packet size to be transmitted is effectively shortened by reducing decoding information to be transmitted in the packet. Simulation results show that NC-based schemes decrease packet transmissions significantly comparing with non-NC-based schemes. The advantage of NC-based schemes is more distinctive when packet reception success ratio of wireless links are near but smaller than 1, and in these cases around 50% of packet transmissions could be saved.

Reducing energy consumption of RNC based media streaming on smartphones via sampling

There have been lots of research efforts on applying random network coding (RNC) technology in media streaming for increased reliability, throughput, etc. In an RNC based media streaming system, it is critical to optimize its RNC implementation; otherwise, the system cannot benefit from RNC due to its high computational cost. For example, an RNC decoder implemented in streaming applications on smartphones may exhibit excessive energy consumption draining batteries too quickly. In this paper, we deal with reducing the energy consumption of RNC based media streaming applications on smartphones especially in presence of other resource-competing applications. To reduce the energy consumption of RNC applications, we try to control the processor clock frequency via manipulating the frequency controllers in smartphone operating systems and the manipulation is accomplished through regulating the processor utilization for RNC applications. To estimate the processor utilization for RNC applications, we rely on a simple sampling approach, i.e., reading system files on a regular basis. Through experimental results, we show that our proposal reduces significantly the energy consumption of RNC applications on smartphones in presence of other intervening applications.

Efficient coding techniques algorithm for cluster-heads communication in wireless sensor networks

Random network coding (RNC) for Error Correction have been the subject of intense research lately. However, in wireless sensor networks (WSNs) the correlation between the received symbols, from the same cluster head (CH), provoke the column correlation on the generator matrix of array Low-Density Parity-Check (LDPC) codes, whereby many columns of this matrix do not offer any additional information, thus the most important properties are lost despite the fact that the generator matrices are more easily dispersed. In this paper, the authors describe each step of the problem-solving algorithm employing efficient coding techniques focusing on improvement planning at Bit-error rsate (BER) vs signal-to-noise ratio (SNR) curves, in dense WSN, between CHs and the base station (BS) node. They use efficient coding techniques approach based on RNC and LDPC codes using the Time Division Multiple Access (TDMA) method. They generalize the notion of RNC-LDPC, study their properties and formulate the main steps of the proposed algorithm reconstruction method. The simulation results demonstrate that this approach is more effective in improving the BER using the proposed algorithm. Moreover, subsequently ameliorate the technical properties offered by this codification type.

Error Performance Analysis of Random Network Coded Cooperation Systems

This paper presents a framework for computing successful decoding probability of random network coding (RNC) in wireless networks. As cooperation emerges due to the naturally occurring broadcasting in wireless links, the application of RNC in wireless networks enables random network coded cooperation (RNCC). The theoretical successful decoding probability of RNCC systems is derived by obtaining the ratio of the full rank and the rank deficient matrices. The full rank condition of the global encoding matrix indicates the successful decoding of source symbols. The results of a single relay along with a relay selection scheme are also investigated. The validity of the presented theoretical expressions is demonstrated through identical simulation results. An implementation scenario is also presented to demonstrate the practical usage effectiveness of RNC in real-time applications, by using software-defined radio nodes.

Constructions of cyclic constant dimension codes

Subspace codes and particularly constant dimension codes have attracted much attention in recent years due to their applications in random network coding. As a particular subclass of subspace codes, cyclic subspace codes have additional properties that can be applied efficiently in encoding and decoding algorithms. It is desirable to find cyclic constant dimension codes such that both the code sizes and the minimum distances are as large as possible. In this paper, we explore the ideas of constructing cyclic constant dimension codes proposed in \big([2], IEEE Trans. Inf. Theory, 2016\big) and \big([17], Des. Codes Cryptogr., 2016\big) to obtain further results. Consequently, new code constructions are provided and several previously known results in [2] and [17] are extended.

Energy Efficient QoS-Aware Random Network Coding on Smartphones

Random network coding (RNC) technology is known to benefit various facets of information networks; however, there have been concerns for the computational complexity of RNC since its incipience. For instance, RNC's high complexity can be directly translated into high energy consumption and drain fast smartphone batteries, making it unsuitable for mobile environments. In this paper, we optimize the energy consumption of RNC implementations with a given QoS requirement, especially throughput, for smartphone environments. To this end, we propose a duty cycling approach minimizing the energy consumption of RNC with a given throughput constraint. By manipulating the processor clock frequency controlling mechanism (a.k.a. governor) in Android, our approach can indirectly regulate the processor clock frequency and enhance energy efficiency. Real experiments on Android systems with smartphone application processors such as Samsung's Exynos 5410, show that our method can reduce the energy consumption of RNC by up to 67% compared to a RNC implementation relying on ondemand governor for frequency control. Finally, we argue that our method can be applied to a wide range of applications by implementing it with a fast Fourier transform algorithm.