{"id":25898300,"url":"https://github.com/iitis/outdoor_localization_based_on_lora_networks","last_synced_at":"2025-07-22T00:31:56.782Z","repository":{"id":279210340,"uuid":"924059732","full_name":"iitis/Outdoor_Localization_Based_on_LoRa_Networks","owner":"iitis","description":"This study presents a machine learning-based outdoor localization approach utilizing LoRa networks.","archived":false,"fork":false,"pushed_at":"2025-04-11T13:20:02.000Z","size":21,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-11T14:44:10.025Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/iitis.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2025-01-29T10:46:39.000Z","updated_at":"2025-04-11T13:20:06.000Z","dependencies_parsed_at":"2025-04-11T14:24:45.289Z","dependency_job_id":"3f8883ec-4d82-45da-985a-52e3a827869b","html_url":"https://github.com/iitis/Outdoor_Localization_Based_on_LoRa_Networks","commit_stats":null,"previous_names":["iitis/outdoor_localization_based_on_lora_networks"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/iitis/Outdoor_Localization_Based_on_LoRa_Networks","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FOutdoor_Localization_Based_on_LoRa_Networks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FOutdoor_Localization_Based_on_LoRa_Networks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FOutdoor_Localization_Based_on_LoRa_Networks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FOutdoor_Localization_Based_on_LoRa_Networks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/iitis","download_url":"https://codeload.github.com/iitis/Outdoor_Localization_Based_on_LoRa_Networks/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/iitis%2FOutdoor_Localization_Based_on_LoRa_Networks/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":266404941,"owners_count":23923491,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-07-21T11:47:31.412Z","response_time":64,"last_error":null,"robots_txt_status":null,"robots_txt_updated_at":null,"robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-03-03T00:17:59.723Z","updated_at":"2025-07-22T00:31:56.751Z","avatar_url":"https://github.com/iitis.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Device Localization Based on RSSI in LoRa Networks\nThis study presents a machine learning (ML) based device localization approach in an outdoor environment in LoRa networks.\n\n*Dataset*\n\nIn this study, we use the latest version of the open-source LoraWAN data set called \"Sigfox and LoRaWAN datasets for fingerprint localization in large urban and rural areas.\"\nYou can access the data from the link below.\n\nhttps://zenodo.org/records/3342253\n\nThree Low Power Wide Area Network (LPWAN) datasets, namely \"Sigfox_dataset_antrwerp,\" \"Sigfox_dataset_rural,\" and \"lorawan_dataset_antwerp,\" were recorded from 16 November 2017 to 5 February 2018. In this study, since we focused on outdoor positioning in the LoRa Network, we only used the \"lorawan_dataset_antwerp\" dataset.\n\nThe data set, which contains around $130,000$ in LoRaWAN messages, was collected in Antwerp, Belgium. Each message (each sample) contains $77$ features, including $72$ RSSI values, RX Time, SF, HDOP, Latitude, and Longitude.\n\n# Machine Learning-Based Localization in LoRa\nWe present the benchmark ML models that we propose for the localization of outdoor devices in RSSI-based LoRaWAN networks: k-NN, CNN, SVR, ANN, XGBoost, and LightGBM.\n\n# Repository Description\n* *Data_Description.ipynb* file contains the data preprocessing stage without the normalization script.\n* *Enhancing_LoRa_based_Outdoor_Localization_Accuracy_Using_Machine_Learning.ipynb* file is a Supplementary Jupyter Notebook, and it presents the architecture of the ML models. \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiitis%2Foutdoor_localization_based_on_lora_networks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fiitis%2Foutdoor_localization_based_on_lora_networks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fiitis%2Foutdoor_localization_based_on_lora_networks/lists"}