Wireless Technologies for Indoor Asset Positioning

The Positioning of assets in a manufacturing industry is one of the milestones in the process to increase the visibility inside the factory and improve the current manufacturing practices. Furthermore, in order to cope with the high mobility of the assets in a factory, the utilization of wireless technologies has been increased in the past few years in order to develop the positioning applications. However, the utilization of these technologies must not increase the complexity of the manufacturing systems. Therefore, the utilization of a common network protocol such as the Internet Protocol is preferred. The theoretical part of this thesis work presents a general description of the wireless technologies used in industrial environments. Additionally, it discusses the different methodologies and algorithms used for the positioning of assets applications in wireless networks in more detail. Furthermore, an introduction to the latest efforts and systems developed to address the problem of position estimation of assets in wireless networks is provided. In order to understand the realization of the IP-based wireless sensor networks, a brief review of the operating systems supporting this characteristic is presented. Finally a survey about the IP-ready wireless sensor network is performed in order to select the most suitable platform to use in the practical part of this work. The practical part of this thesis work focuses on the implementation of a real-time position estimation tool for manufacturing assets based on a Wireless Sensor Network for indoor environments. The main purpose is to estimate the position of a pallet allocated on a light assembly manufacturing line. In addition, the wireless sensor network utilizes the Internet Protocol version 6 as the networking protocol. Furthermore, the estimation parameter utilized by the tool is the received signal strength. Consequently, the position estimation methodologies based on the received signal strength are implemented by this tool. Finally, the position estimation tool was tested which is documented in the results section. /Kir1

Wireless Technologies for Indoor Asset Positioning

The Positioning of assets in a manufacturing industry is one of the milestones in the process to increase the visibility inside the factory and improve the current manufacturing practices. Furthermore, in order to cope with the high mobility of the assets in a factory, the utilization of wireless technologies has been increased in the past few years in order to develop the positioning applications. However, the utilization of these technologies must not increase the complexity of the manufacturing systems. Therefore, the utilization of a common network protocol such as the Internet Protocol is preferred. The theoretical part of this thesis work presents a general description of the wireless technologies used in industrial environments. Additionally, it discusses the different methodologies and algorithms used for the positioning of assets applications in wireless networks in more detail. Furthermore, an introduction to the latest efforts and systems developed to address the problem of position estimation of assets in wireless networks is provided. In order to understand the realization of the IP-based wireless sensor networks, a brief review of the operating systems supporting this characteristic is presented. Finally a survey about the IP-ready wireless sensor network is performed in order to select the most suitable platform to use in the practical part of this work. The practical part of this thesis work focuses on the implementation of a real-time position estimation tool for manufacturing assets based on a Wireless Sensor Network for indoor environments. The main purpose is to estimate the position of a pallet allocated on a light assembly manufacturing line. In addition, the wireless sensor network utilizes the Internet Protocol version 6 as the networking protocol. Furthermore, the estimation parameter utilized by the tool is the received signal strength. Consequently, the position estimation methodologies based on the received signal strength are implemented by this tool. Finally, the position estimation tool was tested which is documented in the results section. /Kir1