This document (ns Notes and Documentation) provides reference documentation for ns. Although we begin with a simple simulation script, resources like Marc Greis's tutorial web pages (originally at his web site, now at http://www.isi. edu/nsnam/ns/tutorial/) or the slides from one of the ns tutorials are problably better places to begin for the ns novice

ns c ○ is LBNL’s Network Simulator [24]. The simulator is written in C++; it uses OTcl as a command and configuration interface. ns v2 has three substantial changes from ns v1: (1) the more complex objects in ns v1 have been decomposed into simpler components for greater flexibility and composability; (2) the configuration interface is now OTcl, an object oriented version of Tcl; and (3) the interface code to the OTcl interpreter is separate from the main simulator. Ns documentation is available in html, Postscript, and PDF formats. See

In this paper we present Worldsens, an integrated environment for development and rapid prototyping of wireless sensor network applications. Our environment relies on software simulation to help the designer during the whole development process. The refinement is done starting from the high level design choices down to the target code implementation, debug and performance analysis. In the early stages of the design, high level parameters, like for example the node sleep and activity periods, can be tuned using WS-Net, an event driven wireless network simulator. WSNet uses models for applications, protocols and radio medium communication with a parameterized accuracy. The second step of the sensor network application design takes place after the hardware implementation choices. This second step relies on the WSim cycle accurate hardware platform simulator. WSim is used to debug the application using the real target binary code. Precise performance evaluation, including real-time analysis at the interrupt level, are made possible at this low simulation level. WSim can be connected to WSNet, in place of the application and protocol models used during the high level simulation to achieve a full distributed application simulation. WSNet and WSNet+WSim allow a continuous refinement from high level estimations down to low level real-time validation. We illustrate the complete application design process using a real life demonstrator that implements a hello protocol for dynamic neighborhood discovery in a wireless sensor network environment.

Discrete event network simulators have emerged as popular tools for verification and performance evaluation for various wireless networks. Nevertheless, the desire to model such networks at high fidelity implies high computational costs, prohibiting most researchers from simulating wireless networks with thousands of nodes. There have been attempts on performance optimizations for large-scale wireless network simulation, but they have not appropriately modeled accumulation of weak interference, thereby suffering inaccuracies which may be magnified by upper layer protocols. This paper presents analysis of the effects of common optimization techniques for large-scale wireless network simulation on the overall network performance and also proposes modifications and novel techniques that introduce only limited inaccuracies or no additional inaccuracy at all. The study quantifies the effects of those optimizations on the simulation results for given thresholds and network parameters, and also identifies thresholds tolerable to most network studies. The experimental results show that these optimizations can improve the runtime performance of an already efficient wireless network simulator substantially, by a factor of up to 55 for wireless networks with 3200 nodes without compromising accuracy of the simulation results.

One of the principal characteristics of large scale wireless sensor networks is their distributed, multi-hop nature. Due to

A rogue insider, in a wireless network, is an authenticated member that exploits possession of a valid identity in order to launch an attack. A typical example is the transmission of a verifiable message containing false or incomplete information. An important step, in enabling the network authorities to attribute an attack message to its originator, involves locating the physical source of the transmission. We propose a probabilistic scheme to determine the location of a transmitting rogue, with a degree of confidence, using the relative signal strength received by neighboring devices, even if the effective isotropic radiated power (EIRP) employed by the rogue is unknown. The relative received signal strength between pairs of trusted receivers are combined with a range of possible EIRP values to construct an area in Euclidian space bounded by minimum and maximum distance hyperbolas. The area contained within the intersection of multiple hyperbola pairs pinpoints the location of the rogue transmitter with a specific level of confidence.

Energy efficiency is of critical importance in sensornets where the working life of wireless motes, and consequently the entire network, is limited by the finite energy capacity of batteries. Radio network activity typically dominates the energy consumption profile of motes running distributed applications, and hence represents the obvious target when attempting to use energy more frugally. Significant savings can be obtained by carefully tuning existing energy-ignorant protocols. Current practice in choosing parameters is generally based on experience, intuition, and trial and error. This approach rarely leads to the best choice. In this paper a novel method is presented through which the complex relationships between protocol parameters, network structure, application workload and observed network behaviour are understood and then tuned. 1

Abstract—The limited communication resources in VANETs have to be used efficiently to achieve reliable communication even in high load situations, where high packet loss is expected. Packet loss can occur on the channel or it occurs in the local queue due to a saturated channel. In this case, information becomes outdated and hence can be dropped. In this article, we propose multiple steps to solve this problem. We establish a model that determines the network capacity and the influence of hidden stations. The state of overloading the channel beyond the network capacity is named local congestion. Each vehicle has to evaluate the current channel load independently based on observations of the channel. With this consideration, we provide concepts for efficient use of available communication resources so that vehicles are able to receive as much information as possible from surrounding vehicles. To achieve that, we present a cross-layer framework for efficient channel usage motivated by the previous model. The framework comprises default mechanisms which efficiently avoid local congestion. We further show the integration of two advanced mechanisms and their interrelation. An evaluation of the combination provides strong indications that this framework allows to solve the identified problems and significantly improves communication reliability. Index Terms—Active safety applications, cross-layeroptimization, medium access control, reliability, vehicle-to-vehicle communication. I.

A Vehicular Ad hoc Network is a collection of mobile hosts forming a temporary network without the aid of any established infrastructure. This flexibility in space and time induces new challenges towards the security needed to support secure communications. Indeed, VANET are subject to attacks due to their vulnerabilities; one of the most compromising attacks is the Sybil nodes attack. We present in this context a Sybil detection approach, based on received signal strength variations, allowing a node to verify the authenticity of other communicating nodes, according to their localizations. In addition, we define an estimated metric of the distinguishability degree between two nodes, allowing to determine Sybil and malicious ones within VANET. The applicability of our contributions is validated through geometrical analysis, simulations and real measurements.

Recently, mobile location estimation is drawing considerable attention in the field of wireless com-munications. Among different mobile location estimation methods, the one which estimates the lo-cation of mobile stations with reference to the wave propagation model is drawing much attention on the grounds that it is applicable to different kinds of cellular network. However, the signal propaga-tion models require estimation of propagation parameters. We have revised the existing EM algorithm which is designed for signal propagation models. We have classified the EM algorithm into two cases — Sufficient Data Collection and Insufficient Data Collection. We experimented our modified EM al-gorithm with 192,177 sets of real life data collected from a major mobile phone operator of Hong Kong. Results show that the modified EM algorithm improves the existing parameters estimation algorithm in terms of estimation accuracy, stability in different types of landscapes. 1