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Due to limitations in bandwidth and the number of RX chains (antennas), existing LoRaWAN-based localization methods often rely on frequency hopping or additional infrastructure to improve location accuracy. Although promising, these methods struggle to localize multiple nodes during packet collisions. In this paper, we propose MC-LoRa, which features a multi-node localization pipeline that includes reliable preamble detection under the near-far effect, tackling inter-symbol interference among multiple packets, and a virtual antenna array method to obtain extra channel state measurements within a single channel. This approach not only enhances angle resolution in our AoA-based system but also eliminates the need for time-consuming frequency hopping, requiring only software processing in existing gateways. Our extensive evaluation results show that MC-LoRa achieves median errors of 7.1m (single-node), 9.2m (multi-node) in an outdoor area of 140m \u00d7 100m, and 2.0m (single-node), 3.9m (multi-node) in an indoor area of 20m \u00d7 16m, which represent improvements of 1.1\u00d7, 2\u00d7 and 1.5\u00d7, 1.7\u00d7 compared to the baseline. Additionally, MC-LoRa can provide localization service for hundreds of LoRaWAN nodes with accuracy comparable to that of a state-of-the-art single-node system. Its wide localization range and high accuracy enable MC-LoRa to benefit a variety of applications, including asset tracking, navigation in vast indoor spaces (e.g., airports, warehouses and halls), and smart cities.<\/jats:p>","DOI":"10.1145\/3712279","type":"journal-article","created":{"date-parts":[[2025,3,4]],"date-time":"2025-03-04T12:10:14Z","timestamp":1741090214000},"page":"1-32","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["MC-LoRa: Multi-node Concurrent Localization for LoRaWAN Indoors and Outdoors"],"prefix":"10.1145","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7439-5979","authenticated-orcid":false,"given":"Han","family":"Hao","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9348-2982","authenticated-orcid":false,"given":"Wei","family":"Xi","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8056-5911","authenticated-orcid":false,"given":"Andreas","family":"Kuster","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-9663","authenticated-orcid":false,"given":"Amalinda","family":"Gamage","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9820-8480","authenticated-orcid":false,"given":"Xianjin","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,3,4]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/MCOM.2017.1600613"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2020.100236"},{"key":"e_1_2_1_3_1","volume-title":"LoRaWAN Specification v1.1","author":"Alliance LoRa","year":"2017","unstructured":"LoRa Alliance. 2017. 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