{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T18:12:41Z","timestamp":1788545561050,"version":"build-2803163510"},"reference-count":41,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:00:00Z","timestamp":1788480000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004686","name":"King Faisal University","doi-asserted-by":"publisher","award":["KFU264619"],"award-info":[{"award-number":["KFU264619"]}],"id":[{"id":"10.13039\/501100004686","id-type":"DOI","asserted-by":"publisher"}]},{"award":["KFU264619"],"award-info":[{"award-number":["KFU264619"]}],"id":[{"id":"https:\/\/ror.org\/00dn43547","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The time available for a counter-drone system to detect an unauthorized aircraft and determine an appropriate response can be limited. Although cloud processing remains useful for storage and offline analysis, communication delays may constrain its use in time-critical decision loops. This paper proposes EdgeTwin-DRL, an edge-assisted digital twin framework that integrates multimodal sensing and multi-agent deep reinforcement learning (DRL) for counter-drone detection and response optimization. The digital twin maintains a synchronized representation of the protected airspace using radar, electro-optical\/infrared (EO\/IR), radio-frequency (RF), and acoustic observations. This synchronized state is used by cooperative DRL actors to adjust computational-resource allocation, detection sensitivity, and candidate countermeasures, while a model-based forward-evaluation assesses proposed responses before they are passed to the simulated response pathway. The framework is evaluated in a simulation testbed in which the RF sensing models are calibrated and independently validated using publicly available datasets, while the remaining sensing components are parameterized using published experimental measurements. Within this calibrated simulation environment, EdgeTwin-DRL achieved a false-positive rate of 1.4% and reduced mean detection-to-response latency by up to 72% relative to the Cloud-DRL baseline and by 26% relative to the MAPPO baseline without calibrated, environment-dependent sensing under the communication and computational assumptions used in the simulator. The evaluation was conducted across modeled urban, suburban, and open-field conditions. These results demonstrate the comparative performance of the proposed architecture within the simulated environment and motivate further investigation of edge-assisted digital twins for counter-drone decision support. Hardware-in-the-loop and controlled field validation are required before operational deployment.<\/jats:p>","DOI":"10.3390\/s26175632","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T17:25:56Z","timestamp":1788542756000},"page":"5632","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["EdgeTwin-DRL: Real-Time Counter-UAS Detection and Response Optimization Using Edge-Assisted Digital Twins and Multi-Agent Deep Reinforcement Learning"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9313-4643","authenticated-orcid":false,"given":"Abdulrahman K.","family":"Alnaim","sequence":"first","affiliation":[{"name":"Department of Management Information Systems, College of Business, King Faisal University, Alahsa 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1394-7159","authenticated-orcid":false,"given":"Ahmed M.","family":"Alwakeel","sequence":"additional","affiliation":[{"name":"Faculty of Computers & Information Technology, University of Tabuk, Tabuk 71491, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102324","DOI":"10.1016\/j.adhoc.2020.102324","article-title":"A comprehensive review of unmanned aerial vehicle attacks and neutralization techniques","volume":"111","author":"Chamola","year":"2021","journal-title":"Ad Hoc Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"168671","DOI":"10.1109\/ACCESS.2020.3023473","article-title":"Protect your sky: A survey of counter unmanned aerial vehicle systems","volume":"8","author":"Kang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"460","DOI":"10.14429\/dsj.68.12442","article-title":"Countering UAVs\u2014The mover of research in military technology","volume":"68","author":"Kratky","year":"2018","journal-title":"Def. Sci. J."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"12095","DOI":"10.1007\/s10489-024-05733-2","article-title":"Analyzing deep reinforcement learning model decisions with Shapley additive explanations for counter-drone operations","volume":"54","author":"Barrado","year":"2024","journal-title":"Appl. Intell."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1109\/MAES.2023.3289928","article-title":"Radar challenges, current solutions, and future advancements for the counter-UAS mission","volume":"38","author":"Brown","year":"2023","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1109\/COMST.2023.3244674","article-title":"Security and privacy on 6G network edge: A survey","volume":"25","author":"Mao","year":"2023","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1474","DOI":"10.1109\/JPROC.2019.2928287","article-title":"Edge computing","volume":"107","author":"Shi","year":"2019","journal-title":"Proc. IEEE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1109\/COMST.2017.2682318","article-title":"Mobile edge computing: A survey on architecture and computation offloading","volume":"19","author":"Mach","year":"2017","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"108952","DOI":"10.1109\/ACCESS.2020.2998358","article-title":"Digital twin: Enabling technologies, challenges and open research","volume":"8","author":"Fuller","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5098","DOI":"10.1109\/TII.2020.3017668","article-title":"Low-latency federated learning and blockchain for edge association in digital twin empowered 6G networks","volume":"17","author":"Lu","year":"2021","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"982","DOI":"10.1038\/s41586-023-06419-4","article-title":"Champion-level drone racing using deep reinforcement learning","volume":"620","author":"Kaufmann","year":"2023","journal-title":"Nature"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1109\/TCOMM.2022.3226193","article-title":"Deep reinforcement learning-based resource allocation in multi-UAV-aided MEC networks","volume":"71","author":"Chen","year":"2023","journal-title":"IEEE Trans. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"\u00c7etin, E., Barrado, C., and Pastor, E. (2022). Countering a drone in a 3D space: Analyzing deep reinforcement learning methods. Sensors, 22.","DOI":"10.3390\/s22228863"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Han, S.-K., Lee, J.-H., and Jung, Y.-H. (2024). Convolutional Neural Network-Based Drone Detection and Classification Using Overlaid Frequency-Modulated Continuous-Wave (FMCW) Range\u2013Doppler Images. Sensors, 24.","DOI":"10.3390\/s24175805"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.future.2019.05.007","article-title":"RF-based drone detection and identification using deep learning approaches: An initiative towards a large open source drone database","volume":"100","author":"Mohamed","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2300251","DOI":"10.1002\/aisy.202300251","article-title":"Drone detection and tracking system based on fused acoustical and optical approaches","volume":"5","author":"Ding","year":"2023","journal-title":"Adv. Intell. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Frid, A., Ben-Shimol, Y., Manor, E., and Greenberg, S. (2024). Drone detection using a fusion of RF and acoustic features and deep neural networks. Sensors, 24.","DOI":"10.3390\/s24082427"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"68791","DOI":"10.1109\/ACCESS.2023.3293124","article-title":"CNN-based UAV detection and classification using sensor fusion","volume":"11","author":"Lee","year":"2023","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"103120","DOI":"10.1016\/j.sysarc.2024.103120","article-title":"A comprehensive survey of digital twins: Applications, technologies and security challenges","volume":"151","author":"Jeremiah","year":"2024","journal-title":"J. Syst. Archit."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1695","DOI":"10.1109\/TASE.2023.3243147","article-title":"Digital twin-based cyber-attack detection framework for cyber-physical manufacturing systems","volume":"21","author":"Balta","year":"2023","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"El-Hajj, M. (2024). Leveraging digital twins and intrusion detection systems for enhanced security in IoT-based smart city infrastructures. Electronics, 13.","DOI":"10.3390\/electronics13193941"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"e8334","DOI":"10.1002\/cpe.8334","article-title":"TwinSec-IDS: An enhanced intrusion detection system in SDN digital-twin-based industrial cyber-physical systems","volume":"37","author":"Krishnaveni","year":"2025","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"12310","DOI":"10.1038\/s41598-023-39366-1","article-title":"TwinPort: 5G drone-assisted data collection with digital twin for smart seaports","volume":"13","author":"Yigit","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1109\/MCOMSTD.0001.2100022","article-title":"Digital twin-enabled intelligent DDoS detection mechanism for autonomous core networks","volume":"6","author":"Yigit","year":"2022","journal-title":"IEEE Commun. Stand. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Iqbal, D., and Buhnova, B. (2024, January 8\u201311). Digital twin design for autonomous drones. Proceedings of the 19th International Conference on Computer Science and Information Systems (FedCSIS), Belgrade, Serbia.","DOI":"10.15439\/2024F6765"},{"key":"ref_26","first-page":"2239","article-title":"Deep reinforcement learning for mobile 5G and beyond","volume":"37","author":"Xiong","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"24611","DOI":"10.52202\/068431-1787","article-title":"The surprising effectiveness of PPO in cooperative multi-agent games","volume":"Volume 35","author":"Yu","year":"2022","journal-title":"Advances in Neural Information Processing Systems"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4531","DOI":"10.1109\/TNSM.2021.3096673","article-title":"Multi-agent deep reinforcement learning for task offloading and resource allocation in multi-UAV-enabled IoT edge networks","volume":"18","author":"Seid","year":"2021","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"4416","DOI":"10.1109\/TNSM.2023.3267809","article-title":"Multi-agent deep reinforcement learning for cooperative computing offloading and route optimization in multi-cloud edge networks","volume":"20","author":"Suzuki","year":"2023","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3386","DOI":"10.1109\/JSAC.2023.3310062","article-title":"A digital twin-assisted intelligent partial offloading approach for vehicular edge computing","volume":"41","author":"Zhao","year":"2023","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Prevot, T., Rios, J., Kopardekar, P., Robinson, J.E., Johnson, M., and Jung, J. (2016, January 13\u201317). UAS traffic management (UTM) concept of operations to safely enable low-altitude flight operations. Proceedings of the AIAA Aviation Forum, Washington, DC, USA.","DOI":"10.2514\/6.2016-3292"},{"key":"ref_32","unstructured":"NVIDIA Corporation (2022). NVIDIA Jetson AGX Orin Technical Brief, NVIDIA Corporation. Available online: https:\/\/www.nvidia.com\/en-us\/autonomous-machines\/embedded-systems\/jetson-orin\/."},{"key":"ref_33","unstructured":"Schulman, J., Wolski, F., Dhariwal, P., Radford, A., and Klimov, O. (2017). Proximal policy optimization algorithms. arXiv."},{"key":"ref_34","unstructured":"Lowe, R., Wu, Y., Tamar, A., Harb, J., Abbeel, P., and Mordatch, I. (2017). Multi-agent actor-critic for mixed cooperative-competitive environments. Advances in Neural Information Processing Systems, NeurIPS."},{"key":"ref_35","unstructured":"Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P. (2016, January 2\u20134). High-dimensional continuous control using generalized advantage estimation. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., and Abbeel, P. (2017, January 24\u201328). Domain randomization for transferring deep neural networks from simulation to the real world. Proceedings of the IEEE\/RSJ International Conference on Intelligent Robots and Systems(IROS), Vancouver, BC, Canada.","DOI":"10.1109\/IROS.2017.8202133"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"197017","DOI":"10.1109\/ACCESS.2020.3034136","article-title":"Multi-access edge computing: A survey","volume":"8","author":"Filali","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"104313","DOI":"10.1016\/j.dib.2019.104313","article-title":"DroneRF dataset: A dataset of drones for RF-based detection, classification, and identification","volume":"26","author":"Allahham","year":"2019","journal-title":"Data Brief"},{"key":"ref_39","first-page":"1147","article-title":"DroneRFa: A large-scale dataset of drone radio frequency signals for detecting low-altitude drones","volume":"46","author":"Yu","year":"2024","journal-title":"J. Electron. Inf. Technol."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Brewczy\u0144ski, K.D., \u017byczkowski, M., Cichulski, K., Kami\u0144ski, K.A., Petsioti, P., and De Cubber, G. (2024). Methods for assessing the effectiveness of modern counter-unmanned aircraft systems. Remote Sens., 16.","DOI":"10.3390\/rs16193714"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Zhao, W., Queralta, J.P., and Westerlund, T. (2020). Sim-to-real transfer in deep reinforcement learning for robotics: A survey. arXiv.","DOI":"10.1109\/SSCI47803.2020.9308468"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5632\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T17:28:42Z","timestamp":1788542922000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/17\/5632"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,4]]},"references-count":41,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26175632"],"URL":"https:\/\/doi.org\/10.3390\/s26175632","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,4]]}}}