Network coding for wireless applications

In this paper, we describe GREENET (an early stage training network in enabling technologies for green radio), which is a new project recently funded by the European Commission under the auspices of the 2010 Marie Curie People Programme. Through the recruitment and personalized training of 17 Early Stage Researchers (ESRs), in GREENET we are committed to the development of new disruptive technologies to address all aspects of energy efficiency in wireless networks, from the user devices to the core network infrastructure, along with the ways the devices and equipment interact with one another. Novel techniques at the physical, link, and network layers to reduce the energy consumption and carbon footprint of 4G devices will be investigated, such as Spatial Modulation (SM) for Multiple-Input-Multiple-Output (MIMO) systems, Cooperative Automatic Repeat reQuest (C-ARQ) protocols, and Network Coding (NC) for lossy networks. Furthermore, cooperation and cognition paradigms will be exploited as additional assets to improve the energy efficiency of wireless networks with the challenging but indispensable constraint of optimizing the system capacity without degrading the user's Quality-of-Service (QoS).

Ever increasing user of electronic devices and high-data-rate applications has lead to continuous growing data traffic and hence carbon dioxide emission. Economical and environmental concern needs improved energy efficient network. As an energy efficient solution needs to outperform under various dynamic situations like varying traffic load, mobility, interference conditions etc Keeping in view of energy efficiency in wireless networks this paper aims at providing an extensive survey of the various researches at network architecture level for minimizing energy consumption in wireless mobile networks. A brief overview of the ongoing projects has also been discussed.

We propose an efficient Adaptive Random Convolutional Network Coding (ARCNC)\nalgorithm to address the issue of field size in random network coding. ARCNC\noperates as a convolutional code, with the coefficients of local encoding\nkernels chosen randomly over a small finite field. The lengths of local\nencoding kernels increase with time until the global encoding kernel matrices\nat related sink nodes all have full rank. Instead of estimating the necessary\nfield size a priori, ARCNC operates in a small finite field. It adapts to\nunknown network topologies without prior knowledge, by locally incrementing the\ndimensionality of the convolutional code. Because convolutional codes of\ndifferent constraint lengths can coexist in different portions of the network,\nreductions in decoding delay and memory overheads can be achieved with ARCNC.\nWe show through analysis that this method performs no worse than random linear\nnetwork codes in general networks, and can provide significant gains in terms\nof average decoding delay in combination networks.\n

Network throughout and energy efficiency are of paramount importance for network performance in an energy-constrained wireless network. However, it is difficult to achieve optimal network throughout and energy efficiency simultaneously. So it is very necessary to find a solution based on tradeoff between network throughput and energy efficiency, which have not yet been well studied in existing works. In this paper, we present a cooperative differential game model and find a time-consistency solution by Shapley value which determines a fair distribution of the total cooperative cost among players.

Recently, energy efficiency in wireless networks has become an important objective. Aside from the growing proliferation of smartphones and other high-end devices in conventional human-to-human (H2H) communication, the introduction of machine-to-machine (M2M) communication or machine-type communication into cellular networks is another contributing factor. In this paper, we investigate quality-of-service (QoS)-driven energy-efficient design for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence scenarios. We formulate the resource allocation problem as a maximization of effective capacity-based bits-per-joule capacity under statistical QoS provisioning. The specific constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) pertaining to power and resource block allocation not only complicate the resource allocation problem, but also render the standard Lagrangian duality techniques inapplicable. We overcome the analytical and computational intractability by first transforming the original problem into a mixed integer programming (MIP) problem and then formulating its dual problem using the canonical duality theory. The proposed energy-efficient design is compared with the spectral efficient design along with round robin (RR) and best channel quality indicator (BCQI) algorithms. Numerical results, which are obtained using the invasive weed optimization (IWO) algorithm, show that the proposed energy-efficient uplink design not only outperforms other algorithms in terms of energy efficiency while satisfying the QoS requirements, but also performs closer to the optimal design.

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.

In order to meet the growing mobile data demand, future wireless networks will be equipped with a multitude of access points (APs). Besides the important implications for the energy consumption, the trend towards densification requires the development of decentralized and sustainable radio resource management techniques. It is critically important to understand how the distribution of signal processing operations affects the energy efficiency of wireless networks. In this paper, we provide a cross-layer framework to evaluate and compare the energy efficiency of wireless networks under different levels of distribution of the signal processing load: (i) hybrid, where the signal processing operations are shared between nodes and APs, (ii) centralized, where signal processing is entirely implemented at the APs, and (iii) fully distributed, where all operations are performed by the nodes. We find that in practical wireless networks, hybrid signal processing exhibits a significant energy efficiency gain over both centralized and fully distributed approaches.

Network coding offers a promising platform for multicast transmission to improve the throughput. However, the users may not collect a full set of the encoded packets to recover the useful information because of its block processing property, leading to the low throughput under wireless fading channel. While the schemes that using feedback from all the receivers to cover the problem above always causes the huge feedback overhead in large network. Accordingly, in this paper we proposed an adaptive sliding window random network coding(ASWRNC) that introduce RNC with lower triangular coefficient matrix and sliding window to achieve higher network throughput and lower feedback overhead. Particularly, we introduce an adaptive encoding scheme that can let the user decode out partial original packets without collecting the full set of encoded packets. On the other hand, the window sliding scheduling helps the user with better channel decode out more original packets. Further, we discuss in detail the feedback-compete mechanism to decline the feedback overhead.

Network coding and relay selection have attracted significant attention due to their performance improvement and spectral efficiency in wireless networks. In this work, we study joint relay selection and network coding cooperation schemes for M source-destination pairs assisted with N relays. We propose a new smart decode-and-forward max-min relay selection scheme based on the source-destination links’ outage status and L selected relays to enhance the communication system diversity. Our objective is realize joint network coding and relay selection strategies for maximizing the expected number of recovered packets by the intended destinations in various situations. We analyze the performance of the proposed scheme and prove that the maximum diversity order of $$N+1$$ is achieved when the number of active relays is at least $$\lceil {\frac{N+1}{3}}\rceil $$ . Also, we show that the scheme can achieve a diversity order of 3L when $$L<\lceil {\frac{N+1}{3}}\rceil $$ . Our analytical and simulation results indicate that the proposed scheme provides higher performance than the existing network coded cooperation schemes.

In WSN, DoS (denial of service) attack makes shortcoming system. The packets travel over and over in the sensor network. By that, all the assets like data transmission, memory, and vitality are squandered by this attack. However, the attacker ought to optimize its attacker plan for request to boost the impact on the system performance because of the deficiency of vitality at the aggressor side. Denial of service (DoS) attack on the Internet has become a squeezing issue. By staying away from these sorts of attacks, network performance can be improved. Therefore, security is a fundamental requirement for these networks. Effective routing is necessary in order to overcome the issued faced by the crosslayer in the DOS attack of the WSN network for the purpose of good transmission. This research work mainly focuses on performance evaluation using optimization methods. To establish the efficient path in the crosslayer against DoS attack, this paper has proposed enhanced lion optimization with an efficient path routing equalization technique (LOEPRE). If any failure node occurs in the network, then the node is recognized and the transfer of the data packet is done to the other node. Retransmission of data causes overload in the network. The proposed model focuses on these issues and overcomes these issues by improving the path efficiently with robust security. It consists of three phases: the initial phase includes the route discovery in the network. In the second phase, the transfer of data is done in the high router path for security purposes. Finally, the efficient path routing equalization technique is used for minimizing the overload in the network; it provides the equalized path length in the network and is highly efficient. Hence, the proposed LOEPRE technique is used to achieve energy efficiency in wireless network for prolonged network lifetime and minimum packet latency and minimize consumption of energy. Moreover, the simulation outcome of the proposed LOEPRE method is highly robust while comparing to the existing methods EFCRS, SSPRA ELOER, EFLOR, and TSTP. It achieves better performance than existing algorithms in comparing metric connectivity ratio, end-to-end delay, overhead, throughput, and packet delivery ratio.

In this letter, we consider a wireless network where the transmitter, such as a base station, multicasts hard-deadline-constrained prioritized data to users over fading channels. We propose a new cross-layer transmission scheme that involves simultaneous smart antenna beamforming at physical layer and random network coding at network layer to maximize the overall network throughput. In this joint design, beamforming is used for dynamic transmitting power allocation to achieve power gain; while the random network coding is adopted to achieve coding gain. The simulation results show the effectiveness of the proposed algorithm.

SUMMARYThis paper investigates the problem of constructing a secret and reliable network coding communication scheme against an adversary that can reselect the μ tapping links in different time slices and inject z erroneous packets into the network, while the network suffers from ρ packet erasures. We first derive a necessary condition for keeping the transmitted information secret from the adversary, while the network is only subject to eavesdropping attack, and no errors are injected. We then provide two network coding schemes for error and erasure correction, which can decode the transmitted information correctly. With that, a secret and reliable network coding is proposed by combining two error and erasure correction schemes with secret communication. We show that under the requirement of 2z + ρ+ μ &lt; k ⩽2z + 2ρ communication can achieve a rate of k −μ−z −ρ packets, where k denotes the network capacity. Moreover, it ensures that the communicated information has complete reliability and information‐theoretic security from the adversary. In particular, the requirement of packet length does not need to be sufficiently large, which is close to the practical packet length. Finally, we also compare our scheme's performance with existing work; the numeric results and security analyses illustrate the performance and security of our scheme. Copyright © 2011 John Wiley &amp; Sons, Ltd.

In this paper we are interested in improving the performance of constructive network coding schemes for video transmission over packet lossy networks. A novel unequal packet loss protection scheme R2NC based on low-triangular global coding matrix with ladder-shaped partition will be presented, which combines redundant and random network coding for robust H.264/SVC video transmission. Firstly, the error-correcting capabilities of redundant network coding make our scheme resilient to loss. Secondly, the implementation of random network coding at the intermediate nodes with multiple input links can reduce the cost of network bandwidth, thus reducing the end to end delay for video transmission. Thirdly, the low-triangular global coding matrix with ladder-shaped partition is maintained throughout R2NC process to provide unequal erasure protection for H.264/SVC priority layers. The redundant network coding avoids the retransmission of lost packets and improves error correcting capabilities of lost packets. Based only on the knowledge of the loss rates on the output links, the source node and intermediate nodes can make decisions for redundant network coding and random network coding (i.e., how much redundancy to add at this node). However, the redundancy caused by redundant network coding makes the network load increases, in order to improve network throughput, we perform random network coding at the intermediate nodes. Our approach is grounded on the overall distortion of reconstructed video minimization by optimizing the amount of redundancy assigned to each layer. Experimental results are shown to demonstrate the significant improvement of H.264/SVC video reconstruction quality with R2NC over packet lossy networks.

Ensuring transparency and explainability in Artificial Intelligence (AI)/Machine Learning (ML) models is crucial for their effective deployment in wireless networks. This paper addresses the challenge of enhancing the interpretability and trustworthiness of ML-based decisions in wireless networks by integrating root cause analysis with observational causal inference. It proposes a causal framework aimed at optimising the energy sustainability class of Base Stations (BSs) - a composite metric that classifies each BS into four possible classes (A, B, C or D) based on its spectral and energy efficiency. This causal framework goes beyond traditional root cause analysis by establishing causal relationships among performance and energy consumption indicators, and other network topology data, quantifying their impacts. The proposed framework combines domain expertise and ML to construct causal graphs of the relevant indicators, enabling quantifiable causal effects between the energy sustainability class of BSs and a chosen treatment variable to support targeted optimisation. The framework was tested with live data from a fourth generation (4G)/fifth generation (5G) network, demonstrating a causal link between the average Channel Quality Index (CQI) and the energy sustainability class of BSs. The causal analysis revealed an Average Treatment Effect (ATE) ranging from 12% to 14%, indicating that improving average CQI has a positive impact on the likelihood of a BS being in class “A”. A continuous marginal structural model further showed that incremental improvements in average CQI raise the class “A” probability by approximately 11%.

Network coding has recently been applied to wireless networks and has achieved some initial success. Researches in wireless network coding have been mostly focusing on utilizing the broadcast nature of the wireless networks. In this paper, we propose a novel network coding framework for wireless relay networks that also takes into consideration the fading and error prone nature of the wireless networks. First, we extend the traditional network coding in lossless networks which operates on 0-1 bits, to a new framework which defines network coding on the posterior probability of each bit. This new framework allows an imperfect decode-recode process at a relay node and avoids possible error propagation when a hard decision is made at the relay node. It implicitly integrates decode-and-forward and estimate-and-forward strategies for wireless network coding to address the technical issues of channel fading and transmission errors. The proposed approach is validated through both theoretical analysis and extensive simulations. Both analysis and simulation confirm that this new framework is able to achieve significant gain over traditional network coding. This new framework also enables the introduction of adaptive scheme into network coding. We demonstrate a basic adaptation scheme and present some preliminary experimental results. The proposed adaptive scheme will lay down an essential foundation in this emerging field of wireless network coding in order to address issues related to link heterogeneity.