ZigBee based wireless indoor localization with sensor placement optimization towards practical home sensing*. (3rd March 2016)

• Record Type: Journal Article
• Authors: Shimosaka, Masamichi; Saisho, Osamu; Sunakawa, Takuya; Koyasu, Hidenori; Maeda, Keisuke; Kawajiri, Ryoma
• Is Part Of: Advanced robotics. Volume 30:Number 5(2016)
• Page Start: 315
• Page End: 325
• Publication Date: 2016-03-03
• Subjects: Indoor localization -- sensor placement optimization -- real-house environment -- RSSI -- wearable device; Robotics -- Periodicals; Robotics -- Japan -- Periodicals; Robotics; Japan; Periodicals; 629.89205
• DOI: 10.1080/01691864.2015.1132636
• Languages: English
• ISSNs: 0169-1864

Abstract

Abstract : Indoor localization with wireless communication technologies is gaining popularity among the ubiquitous computing and intelligent robotics communities. Thanks to the rapid growth in statistical machine learning research, indoor localization methods are being actively pursued with accelerated localization accuracy. However, cost in terms of budget constraints with respect to the number of installed sensors should be considered towards practical home sensing, however few studies have addressed this issue. We propose an installation scenario for practical home sensing applications and a flexible procedure for it with optimal sensor selection algorithms. With this scenario, we assume that a large number of ZigBee devices are temporarily deployed to the home in the initial status, then redundant sensors are efficiently taken off via optimal sensor selection. Thanks to a wide variety of options to formulate sensor selection, we describe an empirical evaluation with various formulations. Specifically, we used prototype ZigBee-based wireless sensor systems in an actual residential environment spread over then evaluated the trade-offs between accuracy and the number of sensors. The empirical results suggest that the backward greedy sensor selection algorithm achieves the most stable performance and that a few selected sensors exhibited competitive performance compared to the initial large-number setting.