Study of industrial environment using Zigbee protocol and modeling industrial noise

Sammanfattning: This thesis is focused on the industrial environment. This study aims to understand the difficulties wireless communications have when performing in these types of environment with a specific standard (and protocol) Zigbee. These difficulties are due mainly to the physical effects and interferences as well the electromagnetic waves from these wireless transmission signals can suffer. The background of this project is to analyze how industries can implement wireless sensor networks (using Zigbee standard) for their factories in order for them to be beneficial i.e. reliable or if it is possible. Industries normally tend to keep in the path of the old fashioned way i.e. wired systems which are more robust and can cope with the hard system requirements. Some study on these environment effects (interference as well) is performed. The results show how this (Zigbee) sensor network could be or should be implemented in order to have the best performance (disposition of devices, type of environment considering only industrial environments, etc.). Specifically, these results show that Zigbe wireless sensor networks are limited to some conditions in order to obtain the desired reliability. These conditions are that these networks must be performed in absorbing environments, LoS disposition of devices, not too long diatance between devices and not other networks in the same area using the same frequency band. The limitation of absorbing environments is because the dispersion effects in the (highly) reflective environments are critically damageful for the link. The limitation of the other devices operating in the same frequency band in a close area is due to the fact that Zigbee has no frequency diversity. Last but not least, the limitations to LoS and not too long maximum range (approximately 50 meters) are related since the range would be (much) lower than 50 meters if the communication was in NLoS disposition of devices. The noise in this environment is also studied and modeled. The results show that as the impulsive index (which is ameasure of the number of impulses that reach the receiver in a certain unit of time) takes larger values, the distribution approximates that of a Gaussian and as A takes lower values the reults show an impulsive characteristic. The Probability of error is computed for values of A less than 1, where the impulsive characterstic is shown, and as A takes larger values the error is greater.