Real-time indoor object tracking and tracing for supply chain management

Abstract

Location tracking and tracing of objects, especially in indoor environments, has been widely discussed in supply chain management to generate business value and support the decision-making process. The recent advancement of indoor positioning related technologies has provided possibilities for tracking and tracing solution in many scenarios. In the logistics and assets management, the acquisition of real-time location information of objects significantly provides both visibility and traceability and enhances the efficiency and effectiveness in the total operation process. However, the solution to proactive location massive, movable and global physical assets in a three-dimensional environment with limited calibration is still open to discuss. This thesis starts with investigating the reactive positioning solution using traditional RFID or barcode technologies. Limitations such as short reading distance and inevitable human-involved operations are concluded. Then, this research proposes and develops proactive indoor positioning solution for managing the cloud objects, which is an innovative framework to integrate the indoor positioning technologies and location base service. Innovative hardware and software for both desktop and mobile cloud applications are designed and developed for assisting the indoor positioning services. The developed intelligent base station and gateway play the role of a “ladder” for connecting the physical and cloud world. A distributed and collaborative tandem tracking algorithm is developed and built in the hardware for fulfilling quick locating. Storage location assignment model is proposed in the reactive positioning management. A case study from a real-life forklift manufacturer is presented for demonstrating how reactive and proactive positioning system are able to facilitate the order-picking and decision-making procedures. Hardware gateways and tags are deployed in the warehouse and algorithms developed and integrated to guarantee the positioning accuracy. The collaborating company benefits from this project by reducing the time and labor force for searching the forklifts. The usability and usefulness has been tested and verified through this case study while considering the cost-effectiveness. The location algorithm also has been refined after the project implementation. Several major contributions can be summarized in the following. Firstly, this work innovatively proposes life-cycle oriented cloud asset management and instantiate cloud forklifts. The trilogy of uplifting to cloud assets has been proposed by integrating smart tags, gateways and cloud services. Secondly, in order to achieve automatic indoor positioning and triggering the corresponding operation procedure, a proactive positioning system and its overall architecture has been proposed and developed through iBeacon technology. An easy-to-deploy and flexible-to-configure manner of implementation method is discussed and verified. Thirdly, a bionic indoor positioning model with self-learning and calibration-free mechanism for locating 3D global assets has been introduced. Congenital genetic radio map established automatically given the gateway coordinate. The previous location estimation result in 3D Cartesian space and its parameters serve as inputs for generating posterior genetic radio map for the next location estimation. Finally, insights and lessons learnt from this research and case implementations are generated which could be referred to both academics and practitioners when contemplating real-time indoor positioning.