Pablo Garrido

ns-2 vs. OPNET: a comparative study of the IEEE 802.11e technology on MANET environments

In this work we present the results of a comparative study between two well-known network simulators: ns-2 and OP-NET Modeler. In particular, we focus on a performance evaluation of the IEEE 802.11e technology on Mobile Ad-hoc Networks (MANETs) in both stationary and mobile scenarios. The paper describes the tested scenarios in detail, and discusses simulation results obtained with OPNET Modeler, comparing them with those obtained with ns-2. The performance of IEEE 802.11e in the presence of legacy IEEE 802.11 stations is also analyzed. Due to the significant differences between both simulators, we enumerate those changes required so as to make results obtained via both simulators comparable. The results that have been reached support the conclusion that the behavior of both simulators is quite similar in general.

Analyzing the behavior of acoustic link models in underwater wireless sensor networks

In the last years, wireless sensor networks have been proposed for their deployment in underwater environments where a lot of applications like aquiculture, pollution monitoring and offshore exploration would benefit from this technology. Despite having a very similar functionality, Underwater Wireless Sensor Networks (UWSNs) exhibit several architectural differences with respect to the terrestrial ones, which are mainly due to the transmission medium characteristics (sea water) and the signal employed to transmit data (acoustic ultrasound signals). So, the design of appropriate network architecture for UWSNs is seriously hardened by the specific characteristics of the communication system. In this work we analyze several acoustic channel models for their use in underwater wireless sensor network architectures. For that purpose, we have implemented them by using the OPNET Modeler tool in order to perform an evaluation of their behavior under different network scenarios. Finally, some conclusions are drawn showing the impact on UWSN performance of different elements of channel model and particular specific environment conditions

Markov Chain Model for the Decoding Probability of Sparse Network Coding

Random linear network coding has been shown to offer an efficient communication scheme, leveraging a remarkable robustness against packet losses. However, it suffers from a high-computational complexity, and some novel approaches, which follow the same idea, have been recently proposed. One of such solutions is sparse network coding (SNC), where only few packets are combined with each transmission. The amount of data packets to be combined can be set from a density parameter/distribution, which could be eventually adapted. In this paper, we present a semi-analytical model that captures the performance of SNC on an accurate way. We exploit an absorbing Markov process, where the states are defined by the number of useful packets received by the decoder, i.e., the decoding matrix rank, and the number of non-zero columns at such matrix. The model is validated by the means of a thorough simulation campaign, and the difference between model and simulation is negligible. We also include in the comparison of some more general bounds that have been recently used, showing that their accuracy is rather poor. The proposed model would enable a more precise assessment of the behavior of SNC techniques.

Enhanced opportunistic random linear source/network coding with cross-layer techniques over wireless mesh networks

In this paper we thoroughly study, by means of simulation, the combination of source and network random linear coding to provide reliable communications over wireless mesh networks. We assume that intermediate nodes code the packets they have previously stored before forwarding them. We also analyze the possibilities that are brought about by opportunistic listening and forwarding, combining them with the use of cross-layer information. We study the performance of the proposed scheme by means of an extensive simulation campaign carried out over the ns-3 platform; the results show that the coding functionality at the intermediate nodes leads to additional performance enhancements. In addition, they also demonstrate the potential benefits of integrating cross-layer techniques.

TCP Performance Enhancement over Wireless Mesh Networks by means of the Combination of Multi-RAT Devices and the MPTCP Protocol

The last trends at communications realms, in particular, wireless technologies, where it is more and more usual that devices carry more than one interface (i.e. multi-RAT, Radio Access Technology), to get access to the Internet, question the classic single-path paradigm, imposed by the mainstream transport protocol, TCP. In this work we assess the behavior of Multipath TCP (MPTCP), which allows the transparent breakdown of a single TCP session into multiple simultaneous subflows. This straightforward feature might lead to remarkable performance enhancements, yielding as well a stronger resilience against failures within any of the routes. Moreover, we evaluate three different routing algorithms (link, node and zone disjoint) that aim to discover the optimal route configuration of disjoint paths over a Wireless Mesh Network (WMN), exploiting the possibilities arisen by this brand new protocol. We use the obtained results to evaluate, by means of simulation, the behavior of the MPTCP protocol, showing that the aggregated performance is significatively higher than that of achieved by the traditional single-path and single-flow TCP.

Performance of Random Linear Coding Over Multiple Error-Prone Wireless Links

In this letter we derive an exact formulation for the performance of Random Linear Coding (RLC) when applied over multiple wireless links. We combine this technique with UDP so as to offer a reliable communication service . We extend a previous result, which only considered one single link, to embrace both multiple sources as well as varying quality of wireless links. We establish the number of excess packets that are required to successfully accomplish the communication and, based on the Bianchi model, we calculate the achieved throughput. We also propose a context-aware probabilistic transmission scheme that leads to a relevant performance gain. We use a thorough simulation-based study over the ns-3 framework to assess the validity of the proposed model and to broaden the corresponding analysis.

Performance and complexity of tunable sparse network coding with gradual growing tuning functions over wireless networks

Random Linear Network Coding (RLNC) has been shown to be a technique with several benefits, in particular when applied over wireless mesh networks, since it provides robustness against packet losses. On the other hand, Tunable Sparse Network Coding (TSNC) is a promising concept, which leverages a trade-off between computational complexity and goodput. An optimal density tuning function has not been found yet, due to the lack of a closed-form expression that links density, performance and computational cost. In addition, it would be difficult to implement, due to the feedback delay. In this work we propose two novel tuning functions with a lower computational cost, which do not highly increase the overhead in terms of the transmission of linear dependent packets compared with RLNC and previous proposals. Furthermore, we also broaden previous studies of TSNC techniques, by means of an extensive simulation campaign carried out using the ns-3 simulator. This brings the possibility of assessing their performance over more realistic scenarios, e.g considering MAC effects and delays. We exploit this implementation to analyze the impact of the feedback sent by the decoder. The results, compared to RLNC, show a reduction of 3.5 times in the number of operations without jeopardizing the network performance, in terms of goodput, even when we consider the delay effect on the feedback sent by the decoder.

Exploiting sparse coding: A sliding window enhancement of a random linear network coding scheme

Random Linear Network Coding (RLNC) is a technique that provides several benefits. For instance, when applied over wireless mesh networks, it can be exploited to ease routing solutions as well as to increase the robustness against packet losses. Nevertheless, the complexity of the decoding process and the required overhead might jeopardize its performance. There is a trade-off when deciding the field and block sizes; larger values decrease the probability of transmitting linearly dependent packets, but they also increase both the required overhead and the decoding complexity. In order to overcome these limitations, we propose a sliding window enhancement; a fixed number of packets (fewer than the block size) is combined within every transmission, and the decoding process can therefore take advantage of the algebra with sparse matrices. The paper presents an analytical model, which is first validated and later broaden by means of an extensive simulation campaign carried out over the ns-3 simulator.

Combination of intra-flow network coding and opportunistic routing: reliable communications over wireless mesh networks

Opportunistic routing has recently appeared as a technique aimed to increase the performance of wireless mesh networks, by taking advantage of the broadcast nature of the wireless medium. Despite the remarkable attention the research community has paid to it, there are still some issues that need to be addressed; one of the most relevant ones is the unnecessary forwarding of the same packet by a number of nodes. Since Random Linear Coding (RLC) mechanisms randomly mix packets before forwarding them, they can be exploited to avoid (or at least to minimize) the aforementioned problem. In a previous work we introduced a flexible Network Coding (NC) entity that we integrated within the ns-3 framework. We extend herewith its functionalities, by integrating an opportunistic routing module that enables it to be used over random topologies. In addition, we assess the performance of using different external algebraical libraries to carry out the coding/recoding/decoding operations (i.e. matrix inverse and rank calculation).

Combination of random linear coding and cross-layer opportunistic routing: Performance over bursty wireless channels

In this work we focus on the application of an intra-flow linear random coding scheme over wireless mesh networks. We propose a cross-layer technique to balance the load between relaying nodes, considering the quality of the wireless links. We assess the performance of our proposal by means of an extensive simulation campaign carried out over the ns-3 platform, exploiting a channel model based on a Hidden Markov Process, which accurately mimics the bursty behavior that is observed over real indoor channels. The results show the benefits of the proposed cross-layer technique. In addition, we also discuss the trade-off (between accuracy and overhead) that needs to be considered when obtaining the information that such scheme rely on. Our simulations yield a 10% performance gain when exploiting the link quality information, as it is already obtained by various routing protocols.