Abstract

Use of Wireless Sensor Networks (WSNs) has become widespread in many critical environments. For example, this technology has come into use in tunnels, mines and so on. In this kind of environments, a WSN system design must be far away from randomness and be done systematically. Therefore simulators play a significant role in a WSN system design. Success of a simulator affects directly success of the system. In this paper, we preferred Castalia simulator which is a successful WSN simulator based on OMNET. Many parameters such as different data size, indoor environment features, and various transmission power levels etc., which have an effect on packet reception ratio (PRR), were examined by Castalia. Finally, we explain how to design a WSN system, and what should be considered in a WSN simulation.

Highlights

  • WIRELESS Sensor Networks (WSNs) are network technologies used for measuring various physical phenomenon such as humidity, temperature, pressure etc

  • We have seen many WSN systems that are deployed in closed critical environments like tunnels, mines

  • We show some general steps to be considered in design of a WSN system, which is especially deployed in closed environments

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Summary

INTRODUCTION

WIRELESS Sensor Networks (WSNs) are network technologies used for measuring various physical phenomenon such as humidity, temperature, pressure etc. They communicate generally through radio frequency (RF). Simulations are of paramount importance to detect unanticipated results in a system design. There are some significant features in applications in which environment modelling is important. Some of these features are wireless channel model, radio model, MAC support, adoption and scalability. A WSN simulator is supposed to have these models. We show some general steps to be considered in design of a WSN system, which is especially deployed in closed environments. We used OMNET++ based Castalia simulator and different scenarios

RELATED WORK
CASTALIA
IMPLEMENTATION
PRR Variation for various Tx power levels
PRR Variation for various data payload
PRR Variation for various ƞ values
LQI Values
CONCULUSION AND DISCUSSION

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