Explainable-AI-based two-stage solution for WSN object localization using zero-touch mobile transceivers  

作　　者：Kai FANG Junxin CHEN Han ZHU Thippa Reddy GADEKALLU Xiaoping WU Wei WANG 

机构地区：[1]School of Mathematics and Computer Science,Zhejiang A&F University,Hangzhou 311300,China [2]School of Software,Dalian University of Technology,Dalian 116081,China [3]Faculty of Applied Sciences,Macao Polytechnic University,Macao 999078,China [4]Department of Electrical and Computer Engineering,Lebanese American University,Byblos 11022801,Lebanon [5]School of Information Engineering,Huzhou University,Huzhou 313000,China [6]Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing,Artificial Intelligence Research Institute,Shenzhen MSU-BIT University,Shenzhen 518172,China

出　　处：《Science China(Information Sciences)》2024年第7期23-41,共19页中国科学（信息科学）（英文版）

基　　金：supported by National Natural Science Foundation of China(Grant No.52102400);Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ23F020001);Quzhou City Science and Technology Project(Grant Nos.2023K252,2023K248);Zhejiang Key R&D Plan(Grant No.2017C03047);Zhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources(Grant No.2020E10017)。

摘　　要：Artificial intelligence technology is widely used in the field of wireless sensor networks(WSN).Due to its inexplicability,the interference factors in the process of WSN object localization cannot be effectively eliminated.In this paper,an explainable-AI-based two-stage solution is proposed for WSN object localization.In this solution,mobile transceivers are used to enlarge the positioning range and eliminate the blind area for object localization.The motion parameters of transceivers are considered to be unavailable,and the localization problem is highly nonlinear with respect to the unknown parameters.To address this,an explainable AI model is proposed to solve the localization problem.Since the relationship among the variables is difficult to fully include in the first-stage traditional model,we develop a two-stage explainable AI solution for this localization problem.The two-stage solution is actually a comprehensive consideration of the relationship between variables.The solution can continue to use the constraints unused in the firststage during the second-stage,thereby improving the performance of the solution.Therefore,the two-stage solution has stronger robustness compared to the closed-form solution.Experimental results show that the performance of both the two-stage solution and the traditional solution will be affected by numerical changes in unknown parameters.However,the two-stage solution performs better than the traditional solution,especially with a small number of mobile transceivers and sensors or in the presence of high noise.Furthermore,we have also verified the feasibility of the proposed explainable-AI-based two-stage solution.

关 键 词：explainable AI object localization semidefinite relaxation mobile transceiver two-stage solution closed-form solution 

分 类 号：TN929.5[电子电信—通信与信息系统] TP212.9[电子电信—信息与通信工程] TP18[自动化与计算机技术—检测技术与自动化装置]