{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,21]],"date-time":"2026-03-21T09:21:03Z","timestamp":1774084863323,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T00:00:00Z","timestamp":1663804800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007446","name":"Deanship of Scientific Research at King Khalid University","doi-asserted-by":"publisher","award":["RGP.2\/111\/43"],"award-info":[{"award-number":["RGP.2\/111\/43"]}],"id":[{"id":"10.13039\/501100007446","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Internet availability and its integration with smart technologies have favored everyday objects and things and offered new areas, such as the Internet of Things (IoT). IoT refers to a concept where smart devices or things are connected and create a network. This new area has suffered from big data handling and security issues. There is a need to design a data analytics model by using new 5G technologies, architecture, and a security model. Reliable data communication in the presence of legitimate nodes is always one of the challenges in these networks. Malicious nodes are generating inaccurate information and breach the user\u2019s security. In this paper, a data analytics model and self-organizing architecture for IoT networks are proposed to understand the different layers of technologies and processes. The proposed model is designed for smart environmental monitoring systems. This paper also proposes a security model based on an authentication, detection, and prediction mechanism for IoT networks. The proposed model enhances security and protects the network from DoS and DDoS attacks. The proposed model evaluates in terms of accuracy, sensitivity, and specificity by using machine learning algorithms.<\/jats:p>","DOI":"10.3390\/s22197201","type":"journal-article","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T23:07:55Z","timestamp":1663888075000},"page":"7201","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Data Analytics, Self-Organization, and Security Provisioning for Smart Monitoring Systems"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5118-7739","authenticated-orcid":false,"given":"Raja Waseem","family":"Anwar","sequence":"first","affiliation":[{"name":"Faculty of Computer Studies (FCS), Arab Open University, Muscat P.O. Box 1596, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kashif Naseer","family":"Qureshi","sequence":"additional","affiliation":[{"name":"Department of Electronic & Computer Engineering, University of Limerick, V94 T9PX Limerick, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8382-3242","authenticated-orcid":false,"given":"Wamda","family":"Nagmeldin","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7474-9100","authenticated-orcid":false,"given":"Abdelzahir","family":"Abdelmaboud","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Science and Arts, King Khalid University, Muhayil Asir 61913, Saudi Arabia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9046-9475","authenticated-orcid":false,"given":"Kayhan Zrar","family":"Ghafoor","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Knowledge University, University Park, Kirkuk Road, Erbil 446015, Iraq"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7030-5200","authenticated-orcid":false,"given":"Ibrahim Tariq","family":"Javed","sequence":"additional","affiliation":[{"name":"Center of Excellence in Artificial Intelligence (CoE-AI), Department of Computer Science, Bahria University, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noel","family":"Crespi","sequence":"additional","affiliation":[{"name":"Institut Polytechnique de Paris Telecom SudParis Evry, Courcouronnes FR, 9 Rue Charles Fourier, 91000 Evry, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"42","DOI":"10.9734\/ajrcos\/2021\/v8i230198","article-title":"IoT and ICT based smart water management, monitoring and controlling system: A review","volume":"8","author":"Yasin","year":"2021","journal-title":"Asian J. Res. Comput. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pattanayak, B.K., Nohur, D., Cowlessur, S.K., and Mohanty, R.K. (2021). An IoT-Based System Architecture for Environmental Monitoring. Progress in Advanced Computing and Intelligent Engineering, Springer.","DOI":"10.1007\/978-981-33-4299-6_42"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"774","DOI":"10.1016\/j.compeleceng.2017.12.015","article-title":"A Dynamic Congestion Control Scheme for safety applications in vehicular ad hoc networks","volume":"72","author":"Qureshi","year":"2018","journal-title":"Comput. Electr. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1777","DOI":"10.1109\/TITS.2020.2994972","article-title":"Internet of Vehicles: Key Technologies, Network Model, Solutions and Challenges With Future Aspects","volume":"22","author":"Qureshi","year":"2020","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103756","DOI":"10.1016\/j.engappai.2020.103756","article-title":"Trust management and evaluation for edge intelligence in the Internet of Things","volume":"94","author":"Qureshi","year":"2020","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"10449","DOI":"10.1007\/s00521-020-05678-w","article-title":"Deep learning-based ambient assisted living for self-management of cardiovascular conditions","volume":"34","author":"Qureshi","year":"2022","journal-title":"Neural Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"10363","DOI":"10.1109\/ACCESS.2020.2964530","article-title":"Self-Assessment Based Clustering Data Dissemination for Sparse and Dense Traffic Conditions for Internet of Vehicles","volume":"8","author":"Qureshi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"10637","DOI":"10.1007\/s00521-020-04900-z","article-title":"Nature-inspired algorithm-based secure data dissemination framework for smart city networks","volume":"33","author":"Qureshi","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_9","first-page":"12","article-title":"Synergy of big data and 5g wireless networks","volume":"25","author":"Zhang","year":"2018","journal-title":"Oppor. Approaches Chall."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhu, C., Cheng, X., Ye, H., Yang, J., Xu, L., and Chao, K. (2019, January 19\u201323). 5G Wireless Networks Meet Big Data Challenges, Trends, and Applications. Proceedings of the 2019 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), Leicester, UK.","DOI":"10.1109\/SmartWorld-UIC-ATC-SCALCOM-IOP-SCI.2019.00272"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1109\/ACCESS.2016.2540520","article-title":"Big data analytics in mobile cellular networks","volume":"4","author":"He","year":"2016","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3619","DOI":"10.1109\/ACCESS.2017.2779844","article-title":"A survey on 5G networks for the Internet of Things: Communication technologies and challenges","volume":"6","author":"Akpakwu","year":"2017","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhang, C., Shen, X., Pei, X., and Yao, Y. (2016, January 18\u201320). Applying big data analytics into network security: Challenges, techniques and outlooks. Proceedings of the 2016 IEEE International Conference on Smart Cloud (SmartCloud), New York, NY, USA.","DOI":"10.1109\/SmartCloud.2016.62"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Maim\u00f3, L.F., Clemente, F.J.G., P\u00e9rez, M.G., and P\u00e9rez, G.M. (2017, January 4\u20138). On the performance of a deep learning-based anomaly detection system for 5G networks. Proceedings of the 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld\/SCALCOM\/UIC\/ATC\/CBDCom\/IOP\/SCI), San Francisco, CA, USA.","DOI":"10.1109\/UIC-ATC.2017.8397440"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rashid, S., and Razak, S.A. (2019, January 2\u20135). Big Data Challenges in 5G Networks. Proceedings of the 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia.","DOI":"10.1109\/ICUFN.2019.8806076"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, C.-W., Chiang, C.-T., and Li, Q. (2017, January 8\u201313). A study of deep learning networks on mobile traffic forecasting. Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada.","DOI":"10.1109\/PIMRC.2017.8292737"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MNET.2018.1800085","article-title":"A deep-learning-based radio resource assignment technique for 5G ultra dense networks","volume":"32","author":"Zhou","year":"2018","journal-title":"IEEE Netw."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"7700","DOI":"10.1109\/ACCESS.2018.2803446","article-title":"A self-adaptive deep learning-based system for anomaly detection in 5G networks","volume":"6","author":"Clemente","year":"2018","journal-title":"IEEE Access"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1109\/TNSE.2018.2848960","article-title":"Channel state information prediction for 5G wireless communications: A deep learning approach","volume":"7","author":"Luo","year":"2018","journal-title":"IEEE Trans. Netw. Sci. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pang, H., Liu, J., Fan, X., and Sun, L. (2018, January 4\u20136). Toward smart and cooperative edge caching for 5g networks: A deep learning based approach. Proceedings of the 2018 IEEE\/ACM 26th International Symposium on Quality of Service (IWQoS), Banff, AB, Canada.","DOI":"10.1109\/IWQoS.2018.8624176"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Huang, D., Gao, Y., Li, Y., Hou, M., Tang, W., Cheng, S., Li, X., and Sun, Y. (2018). Deep learning based cooperative resource allocation in 5G wireless networks. Mob. Netw. Appl., 1\u20138.","DOI":"10.1007\/s11036-018-1178-9"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Luo, C., Ji, J., Wang, Q., Yu, L., and Li, P. (2018, January 20\u201324). Online power control for 5G wireless communications: A deep Q-network approach. Proceedings of the 2018 IEEE International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422442"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ho, C.-C., Huang, B.-H., Wu, M.-T., and Wu, T.-Y. (2019, January 15\u201318). Optimized Base Station Allocation for Platooning Vehicles Underway by Using Deep Learning Algorithm Based on 5G-V2X. Proceedings of the 2019 IEEE 8th Global Conference on Consumer Electronics (GCCE), Osaka, Japan.","DOI":"10.1109\/GCCE46687.2019.9014645"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1109\/MCOM.2014.6871666","article-title":"Opportunities in mobile crowd sensing","volume":"52","author":"Ma","year":"2014","journal-title":"IEEE Commun. Mag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.comnet.2014.12.007","article-title":"PcapWT: An efficient packet extraction tool for large volume network traces","volume":"79","author":"Kim","year":"2015","journal-title":"Comput. Netw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Elsayed, M.S., Le-Khac, N.-A., Dev, S., and Jurcut, A.D. (2020). DDoSNet: A Deep-Learning Model for Detecting Network Attacks. arXiv.","DOI":"10.1109\/WoWMoM49955.2020.00072"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Othman, M.F.B., Abdullah, N.B., and Kamal, N.F.B. (2011, January 19\u201321). MRI brain classification using support vector machine. Proceedings of the 2011 Fourth International Conference on Modeling, Simulation and Applied Optimization, Kuala Lumpur, Malaysia.","DOI":"10.1109\/ICMSAO.2011.5775605"},{"key":"ref_28","unstructured":"Nuti, G., Rugama, L.A.J., and Cross, A.-I. (2019). A Bayesian Decision Tree Algorithm. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liu, Q., and Liu, C. (2015, January 7\u201312). A novel locally linear KNN model for visual recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298738"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., and Greer, K. (2003, January 3\u20137). KNN model-based approach in classification. Proceedings of the OTM Confederated International Conferences \u201cOn the Move to Meaningful Internet Systems\u201d, Catania, Italy.","DOI":"10.1007\/978-3-540-39964-3_62"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7201\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:37:42Z","timestamp":1760143062000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/19\/7201"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,22]]},"references-count":30,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2022,10]]}},"alternative-id":["s22197201"],"URL":"https:\/\/doi.org\/10.3390\/s22197201","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,22]]}}}