Enhancing indoor localization accuracy of sensor-based by advance genetic algorithms

  • Authors:

  • Tin-Yu Wu;I-Ju Liao;Wei-Tsong Lee;Guan-Hsiung Liaw;Jen-Wen Ding;Chung-Chi Wu

  • Affiliations:

  • Tamkang University, Taipei, Taiwan, ROC;Tamkang University Taipei, Taiwan;Tamkang University Taipei, Taiwan, ROC;I-Shou University, Kaohsiung, Taiwan, ROC;National Kaohsiung University of Applied Sciences, Taiwan;I-Shou University, Kaohsiung, Taiwan, ROC

  • Venue:
  • Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
  • Year:
  • 2010

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Abstract

Among the present wireless network technologies, wireless sensor is one of the most extensively applied devices. In addition, with the rapid advancement of wireless communication in recent years, Wireless Sensor Network (WSN) has become very popular research issue and how to realize indoor localization by using sensors is the most common topic. For the time being, localization in wireless sensor networks can be divided into two major categories: Range-based and Range-free localization algorithms. The Range-based algorithms mainly use ranging techniques (ToA, TDoA, AoA, RSS, for example) as the tools to measure the distances between the nodes. From the measured information, the estimated position can be figured out. On the other hand, instead of utilizing ranging techniques, the Range-free algorithms estimate the positions by making use of reference nodes. However, indoor environment exist many interference factors that easily lead to localization inaccuracy. In order to solve the problems that indoor localization may encounter, some algorithms use RF-mapping method to avoid the influence of indoor environment to the localization result. Nevertheless, to make the localization precise needs more data about reference nodes and this greatly increases both the time to set up and the volumes of calculation. Thus, this paper proposes a GA-based (Genetic Algorithms, GA) localization algorithm to estimate the location of unknown nodes and avoid the influence of environmental factors by pre-establishing the pattern. Moreover, the proposed algorithm does not need too much information about reference nodes to pre-establish the pattern.