An XG-PON module for the NS-3 network simulator

10-Gigabit-capable Passive Optical Network (XG-PON), one of the latest standards of optical access networks, is regarded as one of the key technologies for future Internet access networks. In this paper, we propose and discuss the design and implementation of an XG-PON module for the NS-3 network simulator. The aim is to provide a standards-compliant, configurable, and extensible module that can simulate XG-PON with reasonable speed and can support a wide range of research topics. These include analysing and improving the performance of XG-PON, studying the interactions between XG-PON and the upper-layer protocols, and investigating its integration with various wireless networks

Underwater Optical Communication Module : An Extension to the ns-3 Network Simulator

In the last decade, the field of wireless optical communication has gathered immense interest due to its adoption in growing bandwidth-hungry underwater applications. The expensive and non-standardized on-field research measurements call for a reliable simulation tool that allows researchers to realistically design and assess the performance of Underwater Optical Communication (UOC) systems before conducting actual underwater experiments. In this paper, we present a UOC module as an extension to the network simulator ns-3. The module can study the impact of different water conditions on underwater optical networks from the physical layer to the network layer. The proposed UOC module realizes physical layer models of the UOC channels where the added noise and interference effects are modeled as Additive White Gaussian Noise (AWGN). Results show the capability of our module to facilitate large underwater optical network design and optimization. Since ns-3 is an open-source software, the module has the flexibility and reusability to be further developed by the worldwide research community.acceptedVersionPeer reviewe

Simulating services-based systems hosted in networks with dynamic topology

The emerging use of mobile ad hoc networks combined with current trends in the use of service-based systems pose new challenges to accurate simulation of these systems. Current network simulators lack the ability to replicate the complex message exchange behaviour of services, while service simulators do not accurately capture of mobile network properties. In this paper we provide an overview of a framework for simulating both a service behavioural model and a mobile network. The framework is implemented as an extension of the NS-3 network simulator

Performance Comparison of Queue Management Algorithms in LTE Networks using NS-3 Simulator

One of the most important issues accepted by researchers in LTE cellular systems is to develop Queue Management Algorithms for RLC (Radio Link Control). The performance of queue-management algorithms depends on parameters such as latency, packet dropping, and bandwidth usage. Simulation software is used to evaluate the queue-management algorithms developed and to test their performance. In the literature, active queue management algorithms have been compared with wired and wireless networks. In contrast to prior works, in this study, we have analyzed active queue management algorithms using the LTE model in the NS-3 network simulator. When the data and the results obtained from the simulations have been evaluated, it is concluded that the RED algorithm using probabilistic methods and the threshold value is more successful than the other algorithms in LTE networks

Performance Analysis of IEEE 802.15.4 Bootstrap Process

The IEEE 802.15.4 is a popular standard used in wireless sensor networks (WSNs) and the Internet of Things (IoT) applications. In these networks, devices are organized into groups formally known as personal area networks (PAN) which require a bootstrap procedure to become operational. Bootstrap plays a key role in the initialization and maintenance of these networks. For this reason, this work presents our implementation and performance analysis for the ns-3 network simulator. Specifically, this bootstrap implementation includes the support of three types of scanning mechanisms (energy scan, passive scan, and active scan) and the complete classic association mechanism described by the standard. Both of these mechanisms can be used independently by higher layers protocols to support network initialization, network joining, and maintenance tasks. Performance evaluation is conducted in total network association time and packet overhead terms. Our source code is documented and publicly available in the latest ns-3 official release

Visualization Techniques For The Analysis Of Network Simulation Results

The Simulation Automation Framework for Experiments (SAFE) streamlines the de- sign and execution of experiments with the ns-3 network simulator. SAFE ensures that best practices are followed throughout the workflow a network simulation study, guaranteeing that results are both credible and reproducible by third parties. Data analysis is a crucial part of this workflow, where mistakes are often made. Even when appearing in highly regarded venues, scientific graphics in numerous network simulation publications fail to include graphic titles, units, legends, and confidence intervals. After studying the literature in network simulation methodology and in- formation graphics visualization, I developed a visualization component for SAFE to help users avoid these errors in their scientific workflow. The functionality of this new component includes support for interactive visualization through a web-based interface and for the generation of high-quality, static plots that can be included in publications. The overarching goal of my contribution is to help users create graphics that follow best practices in visualization and thereby succeed in conveying the right information about simulation results

Zone-Based Energy Aware Data Collection Protocol for WSNs

In this paper we propose the Zone-based Energy Aware data coLlection (ZEAL) protocol. ZEAL is designed to be used in agricultural applications for wireless sensor networks. In these type of applications, all data is often routed to a single point (named “sink” in sensor networks). The overuse of the same routes quickly depletes the energy of the nodes closer to the sink. In order to minimize this problem, ZEAL automatically creates zones (groups of nodes) independent from each other based on the trajectory of one or more mobile sinks. In this approach the sinks collects data queued in sub-sinks in each zone. Unlike existing protocols, ZEAL accomplish its routing tasks without using GPS modules for location awareness or synchronization mechanisms. Additionally, ZEAL provides an energy saving mechanism on the network layer that puts zones to sleep when there are no mobile sinks nearby. To evaluate ZEAL, it is compared with the Maximum Amount Shortest Path (MASP) protocol. Our simulations using the ns-3 network simulator show that ZEAL is able to collect a larger number of packets with significantly less energy in the same amount of time

Modeling, Implementation and Evaluation of IEEE 802.11ac in Enterprise Networks

Simulation studies are today an important tool in the development of new networks. For this purpose the NS-3 network simulator can be used. The focus of this thesis work lies on 802.11ac, the latest version of the Wi-Fi standard. NS-3 does not support the IEEE 802.11ac standard and the goal was to implement features for this. Different deployment scenarios were modeled in various conditions and the result evaluated. The thesis work describes the changes made to the existing network simulator NS-3, and also evaluations of different simulations. The most extensive simulation result is from the IEEE 802.11ax scenario document were there is four different scenarios. The selected scenario to model was the enterprise where various 802.11ac simulations were carried out. Support for wider channels were implemented and also bit-error calculation for 256-QAM. Simulation results concluded that increasing the number of nodes from 64 to 256 in an enterprise network will yield 17% lower average throughput to each AP and, several APs on the same channel will create unreliable networks with some stations getting high throughput and some not able to send at all. Also that aggregation makes the peak throughput close to the upper bound