{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:19:21Z","timestamp":1784132361161,"version":"3.55.0"},"reference-count":58,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T00:00:00Z","timestamp":1671580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972433, 62102459"],"award-info":[{"award-number":["61972433, 62102459"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2021A1515012242"],"award-info":[{"award-number":["2021A1515012242"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2022,12,21]]},"abstract":"<jats:p>Indoor localization techniques play a fundamental role in empowering plenty of indoor location-based services (LBS) and exhibit great social and commercial values. The widespread fingerprint-based indoor localization methods usually suffer from the low feature discriminability with discrete signal fingerprint or high time overhead for continuous signal fingerprint collection. To address this, we introduce the collaboration mechanism and propose a graph attention based collaborative indoor localization framework, termed GC-Loc, which provides another perspective for efficient indoor localization. GC-Loc utilizes multiple discrete signal fingerprints collected by several users as input for collaborative localization. Specifically, we first construct an adaptive graph representation to efficiently model the relationships among the collaborative fingerprints. Then taking state-of-the-art GAT model as basic unit, we design a deep network with the residual structure and the hierarchical attention mechanism to extract and aggregate the features from the constructed graph for collaborative localization. Finally, we further employ ensemble learning mechanism in GC-Loc and devise a location refinement strategy based on model consensus for enhancing the robustness of GC-Loc. We have conducted extensive experiments in three different trial sites, and the experimental results demonstrate the superiority of GC-Loc, outperforming the comparison schemes by a wide margin (reducing the mean localization error by more than 42%).<\/jats:p>","DOI":"10.1145\/3569495","type":"journal-article","created":{"date-parts":[[2023,1,11]],"date-time":"2023-01-11T15:34:01Z","timestamp":1673451241000},"page":"1-27","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":14,"title":["GC-Loc"],"prefix":"10.1145","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5715-5578","authenticated-orcid":false,"given":"Tao","family":"He","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering and GuangDong Province Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5048-060X","authenticated-orcid":false,"given":"Qun","family":"Niu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Sun Yat-sen University, Zhuhai, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0884-0058","authenticated-orcid":false,"given":"Ning","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering and GuangDong Province Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,1,11]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2015.2478451"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICC.2019.8761118"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/3463510"},{"key":"e_1_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2960780"},{"key":"e_1_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2959308"},{"key":"e_1_2_1_6_1","volume-title":"Indoor Localization with a Single Access Point Based on TDoA and AoA. Wireless Communications and Mobile Computing 2022","author":"Deng Zhian","year":"2022","unstructured":"Zhian Deng, Junchao Wu, Shengao Wang, and Ming Zhang. 2022. Indoor Localization with a Single Access Point Based on TDoA and AoA. Wireless Communications and Mobile Computing 2022 (2022)."},{"key":"e_1_2_1_7_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3494972","article-title":"SmartLOC: Indoor Localization with Smartphone Anchors for On-Demand Delivery","volume":"5","author":"Ding Yi","year":"2021","unstructured":"Yi Ding, Dongzhe Jiang, Yunhuai Liu, Desheng Zhang, and Tian He. 2021. SmartLOC: Indoor Localization with Smartphone Anchors for On-Demand Delivery. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 4 (2021), 1--24.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3550303"},{"key":"e_1_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2018.2857772"},{"key":"e_1_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1145\/3380979"},{"key":"e_1_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191741"},{"key":"e_1_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/3302506.3310389"},{"key":"e_1_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/3322241"},{"key":"e_1_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2017.2680448"},{"key":"e_1_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3139222"},{"key":"e_1_2_1_16_1","volume-title":"Indoor Localization with Spatial and Temporal Representations of Signal Sequences. In 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 1--7.","author":"He Tao","year":"2019","unstructured":"Tao He, Qun Niu, Suining He, and Ning Liu. 2019. Indoor Localization with Spatial and Temporal Representations of Signal Sequences. In 2019 IEEE Global Communications Conference (GLOBECOM). IEEE, 1--7."},{"key":"e_1_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOCOM.2017.8254556"},{"key":"e_1_2_1_18_1","volume-title":"International Conference on Communications and Networking in China. Springer, 200--211","author":"Jia Bing","year":"2020","unstructured":"Bing Jia, Zhaopeng Zong, Baoqi Huang, and Thar Baker. 2020. A DNN-based WiFi-RSSI Indoor Localization Method in IoT. In International Conference on Communications and Networking in China. Springer, 200--211."},{"key":"e_1_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1145\/2517351.2517352"},{"key":"e_1_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2019.2897561"},{"key":"e_1_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3191749"},{"key":"e_1_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN.2013.6817906"},{"key":"e_1_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3494954"},{"key":"e_1_2_1_24_1","first-page":"1","article-title":"SweepLoc: Automatic video-based indoor localization by camera sweeping","volume":"2","author":"Li Mingkuan","year":"2018","unstructured":"Mingkuan Li, Ning Liu, Qun Niu, Chang Liu, S-H Gary Chan, and Chengying Gao. 2018. SweepLoc: Automatic video-based indoor localization by camera sweeping. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2, 3 (2018), 1--25.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_25_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3478095","article-title":"EchoSpot: Spotting Your Locations via Acoustic Sensing","volume":"5","author":"Lian Jie","year":"2021","unstructured":"Jie Lian, Jiadong Lou, Li Chen, and Xu Yuan. 2021. EchoSpot: Spotting Your Locations via Acoustic Sensing. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, 3 (2021), 1--21.","journal-title":"Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"},{"key":"e_1_2_1_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/2348543.2348581"},{"key":"e_1_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2021.3113333"},{"key":"e_1_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/2348543.2348579"},{"key":"e_1_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1109\/GLOBECOM42002.2020.9348025"},{"key":"e_1_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01070"},{"key":"e_1_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397335"},{"key":"e_1_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3284555"},{"key":"e_1_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/3351257"},{"key":"e_1_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2020.3004496"},{"key":"e_1_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSMC.2016.2615652"},{"key":"e_1_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.3390\/s21031002"},{"key":"e_1_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9122117"},{"key":"e_1_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2016.2566679"},{"key":"e_1_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1249"},{"key":"e_1_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2015.2430274"},{"key":"e_1_2_1_41_1","volume-title":"DuLoc: Dual-channel Convolutional Neural Network based on Channel State Information for Indoor Localization","author":"Song Xin","year":"2022","unstructured":"Xin Song, Yufeng Zhou, Haoyang Qi, Weipeng Qiu, and Yanbo Xue. 2022. DuLoc: Dual-channel Convolutional Neural Network based on Channel State Information for Indoor Localization. IEEE Sensors Journal (2022)."},{"key":"e_1_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1109\/TVT.2019.2939209"},{"key":"e_1_2_1_43_1","volume-title":"A Novel GCN based Indoor Localization System with Multiple Access Points. In 2021 International Wireless Communications and Mobile Computing (IWCMC)","author":"Sun Yanzan","unstructured":"Yanzan Sun, Qinggang Xie, Guangjin Pan, Shunqing Zhang, and Shugong Xu. 2021. A Novel GCN based Indoor Localization System with Multiple Access Points. In 2021 International Wireless Communications and Mobile Computing (IWCMC). IEEE, 9--14."},{"key":"e_1_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/IPIN.2018.8533809"},{"key":"e_1_2_1_45_1","volume-title":"Graph Attention Networks. In 6th International Conference on Learning Representations.","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations."},{"key":"e_1_2_1_46_1","volume-title":"Indoor localization using smartphone magnetic and light sensors: A deep LSTM approach. Mobile Networks and Applications","author":"Wang Xuyu","year":"2019","unstructured":"Xuyu Wang, Zhitao Yu, and Shiwen Mao. 2019. Indoor localization using smartphone magnetic and light sensors: A deep LSTM approach. Mobile Networks and Applications (2019), 1--14."},{"key":"e_1_2_1_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/3342517"},{"key":"e_1_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2015.2480064"},{"key":"e_1_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3372224.3419198"},{"key":"e_1_2_1_50_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2017.2784386"},{"key":"e_1_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2021.3055794"},{"key":"e_1_2_1_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/COMST.2019.2911558"},{"key":"e_1_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2019.2926433"},{"key":"e_1_2_1_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3432192"},{"key":"e_1_2_1_55_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNET.2018.2879967"},{"key":"e_1_2_1_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICPP.2015.101"},{"key":"e_1_2_1_57_1","doi-asserted-by":"publisher","DOI":"10.1109\/INFOCOM.2017.8057184"},{"key":"e_1_2_1_58_1","volume-title":"Travi-navi: Self-deployable indoor navigation system","author":"Zheng Yuanqing","year":"2017","unstructured":"Yuanqing Zheng, Guobin Shen, Liqun Li, Chunshui Zhao, Mo Li, and Feng Zhao. 2017. Travi-navi: Self-deployable indoor navigation system. IEEE\/ACM transactions on networking 25, 5 (2017), 2655--2669."}],"container-title":["Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3569495","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3569495","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,15]],"date-time":"2025-07-15T20:54:00Z","timestamp":1752612840000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3569495"}},"subtitle":["A Graph Attention Based Framework for Collaborative Indoor Localization Using Infrastructure-free Signals"],"short-title":[],"issued":{"date-parts":[[2022,12,21]]},"references-count":58,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2022,12,21]]}},"alternative-id":["10.1145\/3569495"],"URL":"https:\/\/doi.org\/10.1145\/3569495","relation":{},"ISSN":["2474-9567"],"issn-type":[{"value":"2474-9567","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,21]]},"assertion":[{"value":"2023-01-11","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}