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

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

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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 ef-fectively 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 first-stage 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,espe-cially 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.

explainable AIobject localizationsemidefinite relaxationmobile transceivertwo-stage solu-tionclosed-form solution

Kai FANG、Junxin CHEN、Han ZHU、Thippa Reddy GADEKALLU、Xiaoping WU、Wei WANG

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School of Mathematics and Computer Science,Zhejiang A&F University,Hangzhou 311300,China

School of Software,Dalian University of Technology,Dalian 116081,China

Faculty of Applied Sciences,Macao Polytechnic University,Macao 999078,China

Department of Electrical and Computer Engineering,Lebanese American University,Byblos 1102 2801,Lebanon

School of Information Engineering,Huzhou University,Huzhou 313000,China

Guangdong-Hong Kong-Macao Joint Laboratory for Emotional Intelligence and Pervasive Computing,Artificial Intelligence Research Institute,Shenzhen MSU-BIT University,Shenzhen 518172,China

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National Natural Science Foundation of ChinaZhejiang Provincial Natural Science Foundation of ChinaQuzhou City Science and Technology ProjectQuzhou City Science and Technology ProjectZhejiang Key R&D PlanZhejiang Province Key Laboratory of Smart Management and Application of Modern Agricultural Resources

52102400LQ23F0200012023K2522023K2482017C030472020E10017

2024

10.1007/s11432-023-3968-9

中国科学:信息科学(英文版)
中国科学院

中国科学:信息科学(英文版)

CSTPCDEI
影响因子：0.715
ISSN：1674-733X
年,卷(期)：2024.67(7)