The Localization Algorithm of Wireless Sensor Network using Mass-Spring Model

碩士 === 國立臺北科技大學 === 資訊工程系 === 107 === The localization algorithm of Wireless Sensor Network is a hot research issue. Global Positioning System (GPS) positioning technology has been widely used for outdoor positioning, but it cannot be employed successfully in indoor positioning. On the other hand, t...

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Bibliographic Details
Main Authors: LEE,I-LUN, 李宜倫
Other Authors: WU,HO-TING
Format: Others
Language:zh-TW
Published: 2019
Online Access:http://ndltd.ncl.edu.tw/handle/c6j75p
Description
Summary:碩士 === 國立臺北科技大學 === 資訊工程系 === 107 === The localization algorithm of Wireless Sensor Network is a hot research issue. Global Positioning System (GPS) positioning technology has been widely used for outdoor positioning, but it cannot be employed successfully in indoor positioning. On the other hand, the popular indoor positioning techniques, such as AOA ,TOA ,TODA, can only be realized on the devices with relatively high costs, thus they are not suitable for low cost applications. The sensor nodes in Industrial internet of things are typically operated in the low-powered and Lossy Networks(LLN) network environments. For this reason, we write the firmware programming codes according to both standard documents of RFC6550(RPL) and RFC7554(IEEE 802.15.4e) to deploy them on the low cost sensor nodes and meet the requirement of Industrial internet of things. This research proposes the location estimation mechanism for wireless devices in the indoor 2D environment. This localization algorithm uses Mass-Spring Model positioning technique, by which each node finds its location by balancing the geometric relationships with neighboring nodes iteratively until the system reaches an equilibrium state. In addition, the proposed method develops a distance ranging method using the widely used log-distance path loss model and actual Received Signal Strength Indicator (RSSI) measurement. Finally, the cooja simulation tool of the Contiki OS platform is executed to collect average distance error and convergence speed of the localization algorithm for evaluating the estimation accuracy and feasibility of the proposed mechanism.