{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T16:02:48Z","timestamp":1784822568036,"version":"3.55.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100012704","name":"University of Agder","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100012704","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Ambient Intell Human Comput"],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The ubiquitous use of drones in a broad spectrum of fields, particularly in farming and agriculture, surveillance, and delivery services not only presents significant opportunities but also raises safety and security concerns. Therefore, to ensure a safe and secure operation, effective management and critical monitoring of drone activity are essential. However, the conventional drone detection approaches that include radar and visual surveillance are constrained to limitations related to accuracy, range and environmental susceptibility. Hence, to address these shortcomings, this study focuses on 5GDC that fuses 5G Channel State Information (CSI) with advanced machine learning approaches to approximate the drone count in a particular scene. The underlying high-resolution and precision properties of 5G technology enable CSI data to be effectively used for drone detection and identification. A multichannel 1D Convolutional Neural Network (1D-CNN) is employed in this work to analyze the one-dimensional time series data from CSI measurements. The proposed neural network model automatically learns and extracts the features indicative of drone presence and movement, improving detection accuracy and reliability. Finally, the performance of the 5GDC method is then evaluated through numerical results.<\/jats:p>","DOI":"10.1007\/s12652-025-05026-7","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:18:59Z","timestamp":1765610339000},"page":"153-163","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["5GDC-estimating drone count using 5G CSI measurements and multi-channel CNN"],"prefix":"10.1007","volume":"17","author":[{"given":"Sriram Mahateja","family":"Akella","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Priya","family":"Thangaraj","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sreenivasa Reddy","family":"Yeduri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1023-2118","authenticated-orcid":false,"given":"Linga Reddy","family":"Cenkeramaddi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"key":"5026_CR1","unstructured":"\u201cAcer aspire 5,\u201d https:\/\/www.acer.com\/us-en\/laptops\/aspire\/aspire-5-amd\/pdp\/NX.K82AA.001, [Accessed 21-05-2024]"},{"key":"5026_CR2","unstructured":"\u201cE310\/E312 - Ettus Knowledge Base \u2014 kb.ettus.com,\u201d https:\/\/kb.ettus.com\/E310\/E312, [Accessed 26-05-2024]"},{"key":"5026_CR3","unstructured":"\u201cGNU Radio \u2014 gnuradio.org,\u201d https:\/\/www.gnuradio.org\/, [Accessed 21-05-2024]"},{"key":"5026_CR4","unstructured":"a. Ettus\u00a0Research NIB \u201cVERT400 Antenna \u2014 ettus.com,\u201d https:\/\/www.ettus.com\/all-products\/vert400\/, [Accessed 06-06-2024]"},{"key":"5026_CR5","doi-asserted-by":"publisher","first-page":"3595","DOI":"10.1109\/OJCOMS.2024.3411529","volume":"5","author":"I Ahmad","year":"2024","unstructured":"Ahmad I, Ullah A, Choi W (2024) Wifi-based human sensing with deep learning: recent advances, challenges, and opportunities. IEEE Open J Commun Soc 5:3595\u20133623","journal-title":"IEEE Open J Commun Soc"},{"issue":"1","key":"5026_CR6","doi-asserted-by":"publisher","first-page":"533","DOI":"10.1121\/10.0020292","volume":"154","author":"W An","year":"2023","unstructured":"An W, Jha A, Kumar A, Cenkeramaddi LR (2023) Estimation of number of unmanned aerial vehicles in a scene utilizing acoustic signatures and machine learning. J Acoust Soc Am 154(1):533\u2013546","journal-title":"J Acoust Soc Am"},{"issue":"5","key":"5026_CR7","doi-asserted-by":"publisher","first-page":"5977","DOI":"10.1007\/s12652-020-02521-x","volume":"14","author":"M Attaran","year":"2023","unstructured":"Attaran M (2023) The impact of 5g on the evolution of intelligent automation and industry digitization. J Ambient Intell Humaniz Comput 14(5):5977\u20135993","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"5026_CR8","doi-asserted-by":"publisher","first-page":"248","DOI":"10.1109\/JSEN.2022.3208518","volume":"22","author":"K Bremnes","year":"2022","unstructured":"Bremnes K, Moen R, Yeduri SR, Yakkati RR, Cenkeramaddi LR (2022) Classification of uavs utilizing fixed boundary empirical wavelet sub-bands of rf fingerprints and deep convolutional neural network. IEEE Sens J 22:248\u2013256","journal-title":"IEEE Sens J"},{"issue":"4","key":"5026_CR9","first-page":"04022002","volume":"150","author":"A Candell","year":"2024","unstructured":"Candell A, Dogan G, Mazloumian M (2024) Real-time activity monitoring on construction sites using 5g networks. J Constr Eng Manag 150(4):04022002","journal-title":"J Constr Eng Manag"},{"key":"5026_CR10","doi-asserted-by":"publisher","first-page":"18709","DOI":"10.1109\/JSEN.2022.3198248","volume":"22","author":"X Cheng","year":"2022","unstructured":"Cheng X, Huang B (2022) Csi-based human continuous activity recognition using gmm-hmm. IEEE Sens J 22:18709\u201318717","journal-title":"IEEE Sens J"},{"key":"5026_CR11","doi-asserted-by":"crossref","unstructured":"Chen Y, Zhang J, Feng W, Alouini MS (2022) Radio sensing using 5g signals: Concepts, state of the art, and challenges. IEEE Internet of Things J 9:1037\u20131052 [Online]. Available: https:\/\/api.semanticscholar.org\/CorpusID:244884109","DOI":"10.1109\/JIOT.2021.3132494"},{"key":"5026_CR12","doi-asserted-by":"crossref","unstructured":"Coluccia A, Parisi G, Fascista A (2020) Detection and classification of multirotor drones in radar sensor networks: a review. Sensors 20:15, [Online]. Available: https:\/\/www.mdpi.com\/1424-8220\/20\/15\/4172","DOI":"10.3390\/s20154172"},{"issue":"4","key":"5026_CR13","doi-asserted-by":"publisher","first-page":"4005","DOI":"10.1007\/s12652-022-04467-8","volume":"14","author":"AFU Din","year":"2023","unstructured":"Din AFU, Mir I, Gul F, Akhtar S (2023) Development of reinforced learning based non-linear controller for unmanned aerial vehicle. J Ambient Intell Humaniz Comput 14(4):4005\u20134022","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"2","key":"5026_CR14","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1017\/S0020818317000121","volume":"71","author":"M Fuhrmann","year":"2017","unstructured":"Fuhrmann M, Horowitz MC (2017) Droning on: Explaining the proliferation of unmanned aerial vehicles. Int Organ 71(2):397\u2013418","journal-title":"Int Organ"},{"key":"5026_CR15","doi-asserted-by":"publisher","first-page":"1134","DOI":"10.1109\/OJCOMS.2022.3189013","volume":"3","author":"BS Khan","year":"2022","unstructured":"Khan BS, Jangsher S, Ahmed A, Al-Dweik A (2022) Urllc and embb in 5g industrial iot: a survey. IEEE Open J Commun Soc 3:1134\u20131163","journal-title":"IEEE Open J Commun Soc"},{"key":"5026_CR16","doi-asserted-by":"crossref","unstructured":"Khosravi M, Pishro-Nik H (2020) Unmanned aerial vehicles for package delivery and network coverage. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 1\u20135","DOI":"10.1109\/VTC2020-Spring48590.2020.9129495"},{"issue":"5","key":"5026_CR17","first-page":"2786","volume":"20","author":"H Liu","year":"2021","unstructured":"Liu H, Yang Y, Zhu C (2021) Understanding of channel state information in wireless sensing. IEEE Trans Wireless Commun 20(5):2786\u20132799","journal-title":"IEEE Trans Wireless Commun"},{"key":"5026_CR18","volume":"123","author":"Y Mao","year":"2024","unstructured":"Mao Y, Tian H, Mazzieri M (2024) Cross-domain activity recognition using a modified generative adversarial network. Pattern Recogn 123:108307","journal-title":"Pattern Recogn"},{"issue":"1","key":"5026_CR19","first-page":"122","volume":"23","author":"P Nguyen","year":"2023","unstructured":"Nguyen P, Lau W (2023) Low-cost wireless sensor network for environmental monitoring using 1d-cnn layers. Sensors 23(1):122","journal-title":"Sensors"},{"key":"5026_CR20","unstructured":"Project GR (2024) Gnu radio. https:\/\/www.gnuradio.org\/, accessed: 2024-05-26"},{"key":"5026_CR21","doi-asserted-by":"publisher","first-page":"4055","DOI":"10.1007\/s12652-019-01662-y","volume":"11","author":"X Rao","year":"2020","unstructured":"Rao X, Li Z, Yang Y (2020) Device-free passive wireless localization system with transfer deep learning method. J Ambient Intell Humaniz Comput 11:4055\u20134071","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"5026_CR22","unstructured":"Research E (2024) Uhd - usrp hardware driver. https:\/\/www.ettus.com\/all-products\/uhd\/, accessed: 2024-05-26"},{"issue":"1","key":"5026_CR23","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s12652-022-03897-8","volume":"15","author":"M Rostami","year":"2024","unstructured":"Rostami M, Farajollahi A, Parvin H (2024) Deep learning-based face detection and recognition on drones. J Ambient Intell Humaniz Comput 15(1):373\u2013387","journal-title":"J Ambient Intell Humaniz Comput"},{"key":"5026_CR24","volume":"126","author":"T Santhosh","year":"2023","unstructured":"Santhosh T, Nguyen P, Rostovski K (2023) Intelligent robust 1d-cnn model for human activity recognition using wearable sensor data. J Biomed Inform 126:103997","journal-title":"J Biomed Inform"},{"issue":"3","key":"5026_CR25","first-page":"1445","volume":"23","author":"J Wang","year":"2024","unstructured":"Wang J (2024) Multi-task contrastive learning for high-accuracy vital sign detection using wi-fi csi. IEEE Trans Mob Comput 23(3):1445\u20131456","journal-title":"IEEE Trans Mob Comput"},{"key":"5026_CR26","doi-asserted-by":"crossref","unstructured":"Wazid M, Das AK, Lee JH (2018) Authentication protocols for the internet of drones: taxonomy, analysis and future directions. J Amb Intell Human Comput 1\u201310","DOI":"10.1007\/s12652-018-1006-x"},{"key":"5026_CR27","doi-asserted-by":"crossref","unstructured":"Wilson AN, Jha A, Kumar A, Cenkeramaddi LR (2023) Estimation of uav count using thermal imaging and lightweight cnn. in 2023 11th International Conference on Control, Mechatronics and Automation (ICCMA), 92\u201396","DOI":"10.1109\/ICCMA59762.2023.10374791"},{"issue":"6","key":"5026_CR28","first-page":"3284","volume":"24","author":"D Yadav","year":"2024","unstructured":"Yadav D, Santhosh T, Liu H (2024) Environmental monitoring using 5g technology: a case study. IEEE Sens J 24(6):3284\u20133292","journal-title":"IEEE Sens J"},{"key":"5026_CR29","doi-asserted-by":"crossref","unstructured":"Zhu Y, Xu B, Wang J, Li Y, Qi W (2023) A simple efficient lightweight cnn method for los\/nlos identification in wireless communication systems. IEEE Commun Lett 27:1515\u20131519, [Online]. Available: https:\/\/api.semanticscholar.org\/CorpusID:258021435","DOI":"10.1109\/LCOMM.2023.3265272"}],"container-title":["Journal of Ambient Intelligence and Humanized Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-025-05026-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12652-025-05026-7","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12652-025-05026-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T16:29:03Z","timestamp":1781627343000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12652-025-05026-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":29,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1]]}},"alternative-id":["5026"],"URL":"https:\/\/doi.org\/10.1007\/s12652-025-05026-7","relation":{},"ISSN":["1868-5137","1868-5145"],"issn-type":[{"value":"1868-5137","type":"print"},{"value":"1868-5145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,13]]},"assertion":[{"value":"6 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no Conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}