{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T16:12:55Z","timestamp":1772554375765,"version":"3.50.1"},"reference-count":40,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T00:00:00Z","timestamp":1737676800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Recently, artificial intelligence (AI) has been adopted in a number of Internet of Things (IoT) application systems to enhance intelligence. We have developed a ready-made server with rich built-in functions to collect, process, display, analyze, and store data from various IoT devices, the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform, in which various AI techniques have been implemented to enhance its capabilities. In this paper, we present an application of SEMAR to a drone-based wall inspection system using an object detection AI model called You Only Look Once (YOLO). This system aims to detect wall cracks at high places using images taken via a camera on a flying drone. An edge computing device is installed to control the drone, sending the taken images through the Kafka system, storing them with the drone flight data, and sending the data to SEMAR. The images are analyzed via YOLO through SEMAR. For evaluations, we implemented the system using Ryze Tello for the drone and Raspberry Pi for the edge, and we evaluated the detection accuracy. The preliminary experiment results confirmed the effectiveness of the proposal.<\/jats:p>","DOI":"10.3390\/info16020091","type":"journal-article","created":{"date-parts":[[2025,1,24]],"date-time":"2025-01-24T09:40:39Z","timestamp":1737711639000},"page":"91","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Application of SEMAR IoT Application Server Platform to Drone-Based Wall Inspection System Using AI Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6208-8472","authenticated-orcid":false,"given":"Yohanes Yohanie Fridelin","family":"Panduman","sequence":"first","affiliation":[{"name":"Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Radhiatul","family":"Husna","sequence":"additional","affiliation":[{"name":"Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"family":"Noprianto","sequence":"additional","affiliation":[{"name":"Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3234-3473","authenticated-orcid":false,"given":"Nobuo","family":"Funabiki","sequence":"additional","affiliation":[{"name":"Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shunya","family":"Sakamaki","sequence":"additional","affiliation":[{"name":"Graduate School of Environmental, Life, Natural Science and Technology, Okayama University, Okayama 700-8530, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sritrusta","family":"Sukaridhoto","sequence":"additional","affiliation":[{"name":"Department of Informatics and Computer, Politeknik Elektronika Negeri Surabaya, Surabaya 60111, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6582-3495","authenticated-orcid":false,"given":"Yan Watequlis","family":"Syaifudin","sequence":"additional","affiliation":[{"name":"Department of Information Technology, State Polytechnic of Malang, Malang 65141, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfiandi Aulia","family":"Rahmadani","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, State Polytechnic of Malang, Malang 65141, Indonesia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","article-title":"Internet of things (IoT): A Vision, Architectural Elements, and Future Directions","volume":"29","author":"Gubbi","year":"2013","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1109\/JIOT.2014.2312291","article-title":"Research Directions for the Internet of Things","volume":"1","author":"Stankovic","year":"2014","journal-title":"IEEE Internet Things J."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alahi, M.E., Sukkuea, A., Tina, F.W., Nag, A., Kurdthongmee, W., Suwannarat, K., and Mukhopadhyay, S.C. (2023). Integration of IoT-enabled technologies and Artificial Intelligence (AI) for Smart City Scenario: Recent advancements and future trends. Sensors, 23.","DOI":"10.3390\/s23115206"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Sharma, K., and Shivandu, S.K. (2024). Integrating Artificial Intelligence and internet of things (IoT) for enhanced crop monitoring and management in Precision Agriculture. Sens. Int., 5.","DOI":"10.1016\/j.sintl.2024.100292"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.ijinfomgt.2019.01.021","article-title":"Artificial Intelligence for Decision Making in the Era of Big Data \u2013 Evolution, Challenges and Research Agenda","volume":"48","author":"Duan","year":"2019","journal-title":"Int. J. Inf. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Belgaum, M.R., Alansari, Z., Musa, S., Mansoor Alam, M., and Mazliham, M.S. (2021). Role of Artificial Intelligence in Cloud Computing, IoT and SDN: Reliability and Scalability Issues. Int. J. Electr. Comput. Eng. (IJECE), 11.","DOI":"10.11591\/ijece.v11i5.pp4458-4470"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"526","DOI":"10.1016\/j.dcan.2020.12.002","article-title":"Deep Learning for the Internet of Things: Potential Benefits and Use-cases","volume":"7","author":"Saleem","year":"2021","journal-title":"Digit. Commun. Netw."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Panduman, Y.Y.F., Funabiki, N., Puspitaningayu, P., Kuribayashi, M., Sukaridhoto, S., and Kao, W.-C. (2022). Design and Implementation of SEMAR IoT Server Platform with Applications. Sensors, 22.","DOI":"10.3390\/s22176436"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Panduman, Y.Y., Funabiki, N., Fajrianti, E.D., Fang, S., and Sukaridhoto, S. (2024). A survey of AI techniques in IoT applications with use case investigations in the smart environmental monitoring and analytics in real-time IOT platform. Information, 15.","DOI":"10.3390\/info15030153"},{"key":"ref_10","unstructured":"Zheng, G. (2021). YOLOX: Exceeding YOLO Series in 2021. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. arXiv.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Munawar, H.S., Hammad, A.W., Haddad, A., Soares, C.A., and Waller, S.T. (2021). Image-based crack detection methods: A review. Infrastructures, 6.","DOI":"10.3390\/infrastructures6080115"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Ali, R., Chuah, J.H., Talip, M.S., Mokhtar, N., and Shoaib, M.A. (2022). Structural crack detection using deep convolutional neural networks. Autom. Constr., 133.","DOI":"10.1016\/j.autcon.2021.103989"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Su, P., Han, H., Liu, M., Yang, T., and Liu, S. (2024). Mod-Yolo: Rethinking the Yolo Architecture at the level of feature information and applying it to crack detection. Expert Syst. Appl., 237.","DOI":"10.1016\/j.eswa.2023.121346"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Yu, Z., Shen, Y., and Shen, C. (2021). A real-time detection approach for bridge cracks based on YOLOv4-FPM. Autom. Constr., 122.","DOI":"10.1016\/j.autcon.2020.103514"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Jung, H.-K., and Choi, G.-S. (2022). Improved Yolov5: Efficient object detection using drone images under various conditions. Appl. Sci., 12.","DOI":"10.3390\/app12147255"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2023). Drone-yolo: An efficient neural network method for target detection in drone images. Drones, 7.","DOI":"10.3390\/drones7080526"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kucukayan, G., and Karacan, H. (2024). Yolo-IHD: Improved real-time human detection system for indoor drones. Sensors, 24.","DOI":"10.3390\/s24030922"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"479","DOI":"10.12694\/scpe.v21i3.1764","article-title":"Performance analysis of video on-demand and live video streaming using cloud based services","volume":"21","author":"Patel","year":"2020","journal-title":"Scalable Comput. Pract. Exp."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Liao, Y.-H., and Juang, J.-G. (2022). Real-time UAV trash monitoring system. Appl. Sci., 12.","DOI":"10.3390\/app12041838"},{"key":"ref_21","first-page":"46","article-title":"Usage of apache kafka for low-latency image processing","volume":"26","author":"Karpiuk","year":"2024","journal-title":"Electron. Inf. Technol."},{"key":"ref_22","unstructured":"MongoDB (2024, November 21). Mongodb: The Application Data Platform. Available online: https:\/\/www.mongodb.com\/."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Panduman, Y.Y.F., Funabiki, N., Ito, S., Husna, R., Kuribayashi, M., Okayasu, M., Shimazu, J., and Sukaridhoto, S. (2023). An Edge Device Framework in SEMAR IoT Application Server Platform. Information, 14.","DOI":"10.3390\/info14060312"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Bixio, L., Delzanno, G., Rebora, S., and Rulli, M. (2020). A flexible IoT stream processing architecture based on microservices. Information, 11.","DOI":"10.3390\/info11120565"},{"key":"ref_25","unstructured":"(2024, November 21). Docker. Available online: https:\/\/docs.docker.com\/get-started\/get-docker\/."},{"key":"ref_26","unstructured":"Kreps, J., Narkhede, N., and Rao, J. (2011, January 12\u201316). Kafka: A distributed messaging system for log processing. Proceedings of the 6th International Workshop on Networking Meets Databases, Athens, Greece."},{"key":"ref_27","first-page":"24","article-title":"Distributing messages using rabbitmq with advanced message exchanges","volume":"6","author":"Dixit","year":"2019","journal-title":"Int. J. Res. Stud. Comput. Sci. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Htut, A.M., and Aswakul, C. (2022). Development of near real-time wireless image sequence streaming cloud using Apache Kafka for Road Traffic Monitoring Application. PLoS ONE, 17.","DOI":"10.1371\/journal.pone.0264923"},{"key":"ref_29","unstructured":"(2024, February 22). University, \u201cCrack Instance Segmentation Dataset (V2) by University,\u201d Roboflow. Available online: https:\/\/universe.roboflow.com\/university-bswxt\/crack-bphdr\/dataset\/2."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yudha Erian Saputra, M., Noor Arief, S., Nur Wijayaningrum, V., and Syaifudin, Y.W. (2024, January 28\u201329). Real-time server monitoring and notification system with prometheus, Grafana, and telegram integration. Proceedings of the 2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems (ICETSIS), Manama, Bahrain.","DOI":"10.1109\/ICETSIS61505.2024.10459488"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6039","DOI":"10.1007\/s10586-024-04266-0","article-title":"Deployment and performance monitoring of Docker based Federated Learning Framework for software defect prediction","volume":"27","author":"Malhotra","year":"2024","journal-title":"Clust. Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhang, T., He, W., and Zhang, Z. (2023). YOLOv7-UAV: An Unmanned Aerial Vehicle Image Object Detection Algorithm Based on Improved YOLOv7. Electronics, 12.","DOI":"10.3390\/electronics12143141"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Padilla, R., Netto, S.L., and da Silva, E.A.B. (2020, January 1\u20133). A Survey on Performance Metrics for Object-Detection Algorithms. Proceedings of the 2020 International Conference on Systems, Signals and Image Processing (IWSSIP), Niter\u00f3i, Brazil.","DOI":"10.1109\/IWSSIP48289.2020.9145130"},{"key":"ref_34","unstructured":"Borui, J., Ruixuan, L., Jiayuan, M., Tete, X., and Yuning, J. (2018, January 8\u201314). Acquisition of Localization Confidence for Accurate Object Detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Kasper-Eulaers, M., Hahn, N., Berger, S., Sebulonsen, T., and Kummervold, P.E. (2021). Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5. Algorithms, 14.","DOI":"10.3390\/a14040114"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Vossoughi, H., and Siddiqui, R.I. (2004). Industrial rope access\u2014An alternate means for inspection, maintenance, and repair of building facades and structures. STP1444-EB Building Facade Maintenance, Repair, and Inspection, ASTM International.","DOI":"10.1520\/STP11465S"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Jung, S., Song, S., Youn, P., and Myung, H. (2018, January 1\u20135). Multi-Layer Coverage Path Planner for Autonomous Structural Inspection of High-Rise Structures. Proceedings of the 2018 IEEE\/RSJ International Conference on Intelligent Robots and Systems (IROS), Madrid, Spain.","DOI":"10.1109\/IROS.2018.8593537"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Karpf, A., Selig, M., Alchaar, A., and Iskander, M. (2023). Detection of cracks in concrete using near-IR fluorescence imaging. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-45917-3"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.autcon.2015.10.012","article-title":"Thermographic test for the geometric characterization of cracks in welding using IR image rectification","volume":"61","year":"2016","journal-title":"Autom. Constr."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Ivan, G., Susana, L., and Pedro, A. (2018). Infrared Thermography\u2019s Application to Infrastructure Inspections. Infrastructures, 3.","DOI":"10.3390\/infrastructures3030035"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/2\/91\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T10:35:40Z","timestamp":1759919740000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/2\/91"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,24]]},"references-count":40,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,2]]}},"alternative-id":["info16020091"],"URL":"https:\/\/doi.org\/10.3390\/info16020091","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,24]]}}}