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What is Continuous-Valued Simulation

Handbook of Research on Discrete Event Simulation Environments: Technologies and Applications
In a continuous-valued simulation, the values of the system states are continuously change with time. The various states of the system are usually represented by a set of algebraic differential, or integro-differential equations. The simulation program solves the equations and uses the numbers to change the state and output of the simulation.
Published in Chapter:
On the Use of Discrete-Event Simulation in Computer Networks Analysis and Design
Hussein Al-Bahadili (The Arab Academy for Banking & Financial Sciences, Jordan)
DOI: 10.4018/978-1-60566-774-4.ch019
Abstract
This chapter presents a description of a newly developed research-level computer network simulator, which can be used to evaluate the performance of a number of flooding algorithms in ideal and realistic mobile ad hoc network (MANET) environments. It is referred to as MANSim. The simulator is written in C++ programming language and it consists of four main modules: network, mobility, computational, and algorithm modules. This chapter describes the philosophy behind the simulator and explains its internal structure. The new simulator can be characterized as: a process-oriented discrete-event simulator using terminating simulation approach and stochastic input-traffic pattern. In order to demonstrate the effectiveness and flexibility of MANSim, it was used to study the performance of five flooding algorithms, these as: pure flooding, probabilistic flooding, LAR-1, LAR-1P, and OMPR. The simulator demonstrates an excellent accuracy, reliability, and flexibility to be used as a cost-effective tool in analyzing and designing wireless computer networks in comparison with analytical modeling and experimental tests. It can be learned quickly and it is sufficiently powerful, comprehensive, and extensible to allow investigation of a considerable range of problems of complicated geometrical configuration, mobility patterns, probability density functions, and flooding algorithms.
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