{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T12:40:17Z","timestamp":1773232817716,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2018,11,2]],"date-time":"2018-11-02T00:00:00Z","timestamp":1541116800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["NRF-2015R1D1A1A01059049"],"award-info":[{"award-number":["NRF-2015R1D1A1A01059049"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003696","name":"Electronics and Telecommunications Research Institute","doi-asserted-by":"publisher","award":["18AS1310"],"award-info":[{"award-number":["18AS1310"]}],"id":[{"id":"10.13039\/501100003696","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Drones have recently become extremely popular, especially in military and civilian applications. Examples of drone utilization include reconnaissance, surveillance, and packet delivery. As time has passed, drones\u2019 tasks have become larger and more complex. As a result, swarms or clusters of drones are preferred, because they offer more coverage, flexibility, and reliability. However, drone systems have limited computing power and energy resources, which means that sometimes it is difficult for drones to finish their tasks on schedule. A solution to this is required so that drone clusters can complete their work faster. One possible solution is an offloading scheme between drone clusters. In this study, we propose an opportunistic computational offloading system, which allows for a drone cluster with a high intensity task to borrow computing resources opportunistically from other nearby drone clusters. We design an artificial neural network-based response time prediction module for deciding whether it is faster to finish tasks by offloading them to other drone clusters. The offloading scheme is conducted only if the predicted offloading response time is smaller than the local computing time. Through simulation results, we show that our proposed scheme can decrease the response time of drone clusters through an opportunistic offloading process.<\/jats:p>","DOI":"10.3390\/s18113751","type":"journal-article","created":{"date-parts":[[2018,11,5]],"date-time":"2018-11-05T04:26:39Z","timestamp":1541391999000},"page":"3751","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Design and Simulation of the Opportunistic Computation Offloading with Learning-Based Prediction for Unmanned Aerial Vehicle (UAV) Clustering Networks"],"prefix":"10.3390","volume":"18","author":[{"given":"Rico","family":"Valentino","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Ajou University, Suwon 16499, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2755-2721","authenticated-orcid":false,"given":"Woo-Sung","family":"Jung","sequence":"additional","affiliation":[{"name":"Electronics and Telecommunications Research Institute (ETRI), Daejeon 34129, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8799-1761","authenticated-orcid":false,"given":"Young-Bae","family":"Ko","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Ajou University, Suwon 16499, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bupe, P., Haddad, R., and Rios-Gutierrez, F. (2015, January 9\u201312). Relief and emergency communication network based on an autonomous decentralized UAV clustering network. Proceedings of the SoutheastCon, Fort Lauderdale, FL, USA.","DOI":"10.1109\/SECON.2015.7133027"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3988","DOI":"10.1109\/TWC.2018.2818734","article-title":"UAV-Aided Offloading for Cellular Hotspot","volume":"17","author":"Lyu","year":"2018","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3949","DOI":"10.1109\/TWC.2016.2531652","article-title":"Unmanned Aerial Vehicle with Underlaid Device-to-Device Communications: Performance and Tradeoffs","volume":"15","author":"Mozaffari","year":"2016","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"899","DOI":"10.1109\/JIOT.2016.2612119","article-title":"Low-altitude unmanned aerial vehicles-based internet of things services: Comprehensive survey and future perspectives","volume":"3","author":"Motlagh","year":"2016","journal-title":"IEEE Internet Things J."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ma\u2019sum, M.A., Arrofi, M.K., Jati, G., Arifin, F., Kurniawan, M.N., Mursanto, P., and Jatmiko, W. (2013, January 28\u201329). Simulation of intelligent Unmanned Aerial Vehicle (UAV) For military surveillance. Proceedings of the International Conference on Advanced Computer Science and Information Systems (ICACSIS), Bali, Indonesia.","DOI":"10.1109\/ICACSIS.2013.6761569"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Karthigeyan, P., Raja, M.S., Prabu, S., and Gnanaselvam, R. (2015, January 8\u201310). Flying robot\u2014A drone for urban warfare. Proceedings of the International Conference on Pervasive Computing (ICPC), Pune, India.","DOI":"10.1109\/PERVASIVE.2015.7087127"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Schuyler, T.J., and Guzman, M.I. (2017). Unmanned aerial systems for monitoring trace tropospheric gases. Atmosphere, 8.","DOI":"10.3390\/atmos8100206"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Jacob, J.D., Chilson, P.B., Houston, A.L., and Smith, S.W. (2018). Considerations for atmospheric measurements with small unmanned aircraft systems. Atmosphere, 9.","DOI":"10.3390\/atmos9070252"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, S., Peng, S., Wang, M., Shen, A., and Liu, Z. (2018). The characteristics and contributing factors of air pollution in Nanjing: A case study based on an unmanned aerial vehicle experiment and multiple datasets. Atmosphere, 9.","DOI":"10.3390\/atmos9090343"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Witte, B.M., Singler, R.F., and Bailey, S.C.C. (2017). Development of an unmanned aerial vehicle for the measurement of turbulence in the atmospheric boundary layer. Atmosphere, 8.","DOI":"10.3390\/atmos8100195"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Liu, K., Zhang, J., and Zhang, T. (2008, January 2\u20135). The clustering algorithm of UAV Networking in Near-space. Proceedings of the 2008 8th International Symposium on Antennas, Propagation and EM Theory, Kunming, China.","DOI":"10.1109\/ISAPE.2008.4735528"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zang, C., and Zang, S. (2011, January 5\u20139). Mobility prediction clustering algorithm for UAV networking. Proceedings of the 2011 IEEE GLOBECOM Workshops (GC Wkshps), Houston, TX, USA.","DOI":"10.1109\/GLOCOMW.2011.6162360"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1254","DOI":"10.1016\/j.adhoc.2012.12.004","article-title":"Flying Ad-Hoc Networks (FANETs): A Survey","volume":"11","author":"Bekmezci","year":"2013","journal-title":"Ad Hoc Netw."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ashok, A., Steenkiste, P., and Bai, F. (2015, January 11). Enabling Vehicular Applications using Cloud Services through Adaptive Computation Offloading. Proceedings of the MCS, Paris, France.","DOI":"10.1145\/2802130.2802131"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Messous, M., Sedjelmaci, H., Houari, N., and Senouci, S. (2017, January 21\u201325). Computation offloading game for an UAV network in mobile edge computing. Proceedings of the 2017 IEEE International Conference on Communications (ICC), Paris, France.","DOI":"10.1109\/ICC.2017.7996483"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1109\/MC.2010.98","article-title":"Cloud Computing for Mobile Users: Can Offloading Computation Save Energy?","volume":"43","author":"Kumar","year":"2010","journal-title":"Computer"},{"key":"ref_17","unstructured":"Loke, S.W. (arXiv, 2015). The Internet of Flying-Things: Opportunities and Challenges with Airborne Fog Computing and Mobile Cloud in the Clouds, arXiv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kovachev, D., Yu, T., and Klamma, R. (2012, January 10\u201313). Adaptive Computation Offloading from Mobile Devices into the Cloud. Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, Leganes, Madrid, Spain.","DOI":"10.1109\/ISPA.2012.115"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J., Gao, H., Lv, T., and Lu, Y. (2018, January 15\u201318). Deep reinforcement learning based computation offloading and resource allocation for MEC. Proceedings of the 2018 IEEE Wireless Communications and Networking Conference, Barcelona, Spain.","DOI":"10.1109\/WCNC.2018.8377343"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Jung, W.S., Yim, J., Ko, Y.B., and Singh, S. (2017, January 28\u201330). ACODS: Adaptive computation offloading for drone surveillance system. Proceedings of the 16th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net), Budva, Montenegro.","DOI":"10.1109\/MedHocNet.2017.8001647"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Ouahouah, S., Taleb, T., Song, J., and Benzaid, C. (2017, January 21\u201325). Efficient offloading mechanism for UAVs-based value added services. Proceedings of the IEEE International Conference on Communications (ICC), Paris, France.","DOI":"10.1109\/ICC.2017.7997362"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1109\/MCOM.2017.1600587CM","article-title":"UAV-Based IoT Platform: A Crowd Surveillance Use Case","volume":"55","author":"Motlagh","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, B., Min, H., Heo, J., and Jung, J. (2018). Dynamic Computation Offloading Scheme for Drone-Based Surveillance Systems. Sensors, 18.","DOI":"10.3390\/s18092982"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Messous, M., Arfaoui, A., Alioua, A., and Senouci, S. (2017, January 4\u20138). A Sequential Game Approach for Computation-Offloading in an UAV Network. Proceedings of the GLOBECOM 2017\u20142017 IEEE Global Communications Conference, Singapore.","DOI":"10.1109\/GLOCOM.2017.8253967"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bujari, A., Palazzi, C.E., and Ronzani, D. (2017, January 23). FANET Applications Scenarios and Mobility Models. Proceedings of the 3rd Workshop on Micro Aerial Vehicle Networks, Systems, and Applications, Niagara Falls, NY, USA.","DOI":"10.1145\/3086439.3086440"},{"key":"ref_26","unstructured":"Rico, V., Jung, W.S., and Ko, Y.B. (2018, January 11\u201314). Opportunistic Computational Offloading System for Cluster of Drones. Proceedings of the IEEE International Conference on Advanced Communications Technology (ICACT), Chuncheon-si Gangwon-do, Korea."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Wolski, R., Gurun, S., Krintz, C., and Nurmi, D. (2008, January 14\u201318). Using bandwidth data to make computation offloading decisions. Proceedings of the 2008 IEEE International Symposium on Parallel and Distributed Processing, Miami, FL, USA.","DOI":"10.1109\/IPDPS.2008.4536215"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1002\/rob.20383","article-title":"A distributed architecture for a robotic platform with aerial sensor transportation and self-deployment capabilities","volume":"28","author":"Maza","year":"2011","journal-title":"J. Field Robot."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1023\/A:1020915000287","article-title":"A Novel Optimal Load Distribution Algorithm for Divisible Loads","volume":"6","year":"2003","journal-title":"Clust. Comput."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1109\/TNN.2010.2045657","article-title":"Improved computation for Levenberg-Marquardt training","volume":"21","author":"Wilamowski","year":"2010","journal-title":"Trans. Neural Netw."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Wen, Y., Zhang, W., and Luo, H. (2012, January 25\u201330). Energy-optimal mobile application execution: Taming resource-poor mobile devices with cloud clones. Proceedings of the 2012 IEEE INFOCOM, Orlando, FL, USA.","DOI":"10.1109\/INFCOM.2012.6195685"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3751\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:27:44Z","timestamp":1760196464000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/18\/11\/3751"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,2]]},"references-count":31,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2018,11]]}},"alternative-id":["s18113751"],"URL":"https:\/\/doi.org\/10.3390\/s18113751","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,2]]}}}