Haoming Yang;Qiran Zhao;Hao Wang;Chi Harold Liu;Guozheng Li;Guoren Wang;Jian Tang;Dapeng Wu
{"title":"Indoor Periodic Fingerprint Collections by Vehicular Crowdsensing via Primal-Dual Multi-Agent Deep Reinforcement Learning","authors":"Haoming Yang;Qiran Zhao;Hao Wang;Chi Harold Liu;Guozheng Li;Guoren Wang;Jian Tang;Dapeng Wu","doi":"10.1109/JSAC.2024.3414608","DOIUrl":null,"url":null,"abstract":"Indoor localization is drawing more and more attentions due to the growing demand of various location-based services, where fingerprinting is a popular data driven techniques that does not rely on complex measurement equipment, yet it requires site surveys which is both labor-intensive and time-consuming. Vehicular crowdsensing (VCS) with unmanned vehicles (UVs) is a novel paradigm to navigate a group of UVs to collect sensory data from certain point-of-interests periodically (PoIs, i.e., coverage holes in localization scenarios). In this paper, we formulate the multi-floor indoor fingerprint collection task with periodical PoI coverage requirements as a constrained optimization problem. Then, we propose a multi-agent deep reinforcement learning (MADRL) based solution, “MADRL-PosVCS”, which consists of a primal-dual framework to transform the above optimization problem into the unconstrained duality, with adjustable Lagrangian multipliers to ensure periodic fingerprint collection. We also propose a novel intrinsic reward mechanism consists of the mutual information between a UV’s observations and environment transition probability parameterized by a Bayesian Neural Network (BNN) for exploration, and a elevator-based reward to allow UVs to go cross different floors for collaborative fingerprint collections. Extensive simulation results on three real-world datasets in SML Center (Shanghai), Joy City (Hangzhou) and Haopu Fashion City (Shanghai) show that MADRL-PosVCS achieves better results over four baselines on fingerprint collection ratio, PoI coverage ratio for collection intervals, geographic fairness and average moving distance.","PeriodicalId":73294,"journal":{"name":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE journal on selected areas in communications : a publication of the IEEE Communications Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10558776/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Indoor localization is drawing more and more attentions due to the growing demand of various location-based services, where fingerprinting is a popular data driven techniques that does not rely on complex measurement equipment, yet it requires site surveys which is both labor-intensive and time-consuming. Vehicular crowdsensing (VCS) with unmanned vehicles (UVs) is a novel paradigm to navigate a group of UVs to collect sensory data from certain point-of-interests periodically (PoIs, i.e., coverage holes in localization scenarios). In this paper, we formulate the multi-floor indoor fingerprint collection task with periodical PoI coverage requirements as a constrained optimization problem. Then, we propose a multi-agent deep reinforcement learning (MADRL) based solution, “MADRL-PosVCS”, which consists of a primal-dual framework to transform the above optimization problem into the unconstrained duality, with adjustable Lagrangian multipliers to ensure periodic fingerprint collection. We also propose a novel intrinsic reward mechanism consists of the mutual information between a UV’s observations and environment transition probability parameterized by a Bayesian Neural Network (BNN) for exploration, and a elevator-based reward to allow UVs to go cross different floors for collaborative fingerprint collections. Extensive simulation results on three real-world datasets in SML Center (Shanghai), Joy City (Hangzhou) and Haopu Fashion City (Shanghai) show that MADRL-PosVCS achieves better results over four baselines on fingerprint collection ratio, PoI coverage ratio for collection intervals, geographic fairness and average moving distance.