{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:18:59Z","timestamp":1771697939773,"version":"3.50.1"},"reference-count":34,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T00:00:00Z","timestamp":1735603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009388","name":"Deanship of Graduate Studies and Scientific Research, Jazan University, Saudi Arabia","doi-asserted-by":"publisher","award":["GSSRD-24"],"award-info":[{"award-number":["GSSRD-24"]}],"id":[{"id":"10.13039\/100009388","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computers"],"abstract":"<jats:p>The continuous evolvement of IoT networks has introduced significant optimization challenges, particularly in resource management, energy efficiency, and performance enhancement. Most state-of-the-art solutions lack adequate adaptability and runtime cost-efficiency in dynamic 6G-enabled IoT environments. Accordingly, this paper proposes the Trust-centric Economically Optimized 6G-IoT (TEO-IoT) framework, which incorporates an adaptive trust management system based on historical behavior, data integrity, and compliance with security protocols. Additionally, dynamic pricing models, incentive mechanisms, and adaptive routing protocols are integrated into the framework to optimize resource usage in diverse IoT scenarios. TEO-IoT presents an end-to-end solution for security management and network traffic optimization, utilizing advanced algorithms for trust score estimation and anomaly detection. The proposed solution is emulated using the NS-3 network simulator across three datasets: Edge-IIoTset, N-BaIoT, and IoT-23. Results demonstrate that TEO-IoT achieves an optimal resource usage of 92.5% in Edge-IIoTset and reduces power consumption by 15.2% in IoT-23, outperforming state-of-the-art models like IDSOFT and RAT6G.<\/jats:p>","DOI":"10.3390\/computers14010010","type":"journal-article","created":{"date-parts":[[2024,12,31]],"date-time":"2024-12-31T04:10:23Z","timestamp":1735618223000},"page":"10","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Trust-Centric and Economically Optimized Resource Management for 6G-Enabled Internet of Things Environment"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2096-307X","authenticated-orcid":false,"given":"Osama Z.","family":"Aletri","sequence":"first","affiliation":[{"name":"Department of Computing, College of Engineering and Computing, Umm Al-Qura University, Makkah 21955, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0038-3772","authenticated-orcid":false,"given":"Kamran Ahmad","family":"Awan","sequence":"additional","affiliation":[{"name":"Department of Information Technology, The University of Haripur, Haripur 22620, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-7503-2682","authenticated-orcid":false,"given":"Abdullah M.","family":"Alqahtani","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, College of Engineering and Computer Science, Jazan University, Jazan 45142, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Aouedi, O., Vu, T.H., Sacco, A., Nguyen, D.C., Piamrat, K., Marchetto, G., and Pham, Q.V. (2024). A survey on intelligent Internet of Things: Applications, security, privacy, and future directions. IEEE Commun. Surv. Tutor.","DOI":"10.1109\/COMST.2024.3430368"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.icte.2024.05.008","article-title":"Artificial Intelligence, Internet of things and 6G methodologies in the context of Vehicular Ad-hoc Networks (VANETs): Survey","volume":"10","author":"Saoud","year":"2024","journal-title":"ICT Express"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Salam, A. (2024). Internet of things for sustainable community development: Introduction and overview. Internet of Things for Sustainable Community Development: Wireless Communications, Sensing, and Systems, Springer.","DOI":"10.1007\/978-3-031-62162-8"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Balaram, A., Rao, T.S., Rangaree, P., Siddiqui, S.T., Gopatoti, A., and Maguluri, L.P. (2024). Energy\u2013Efficient Distribution of Resources in Cyber-Physical Internet of Things with 5G\/6G Communication Framework. Wirel. Pers. Commun., 1\u201320.","DOI":"10.1007\/s11277-024-11145-9"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Kizza, J.M. (2024). Internet of things (iot): Growth, challenges, and security. Guide to Computer Network Security, Springer.","DOI":"10.1007\/978-3-031-47549-8"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"23132","DOI":"10.1109\/ACCESS.2024.3364349","article-title":"Energy efficiency and latency optimization for IoT URLLC and mMTC use cases","volume":"12","author":"Elgarhy","year":"2024","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"21457","DOI":"10.1109\/ACCESS.2021.3055775","article-title":"Mez: An adaptive messaging system for latency-sensitive multi-camera machine vision at the iot edge","volume":"9","author":"George","year":"2021","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhu, X., Liu, J., Lu, L., Zhang, T., Qiu, T., Wang, C., and Liu, Y. (2024). Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G Networks. IEEE Commun. Surv. Tutor.","DOI":"10.1109\/COMST.2024.3432871"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102","DOI":"10.1016\/j.comcom.2021.03.005","article-title":"Amalgamation of blockchain and IoT for smart cities underlying 6G communication: A comprehensive review","volume":"172","author":"Kumari","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1109\/JIOT.2021.3103320","article-title":"6G Internet of Things: A comprehensive survey","volume":"9","author":"Nguyen","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nayak, S., and Patgiri, R. (2021). 6G communication technology: A vision on intelligent healthcare. Health Informatics: A Computational Perspective in Healthcare, Springer.","DOI":"10.1007\/978-981-15-9735-0_1"},{"key":"ref_12","first-page":"405","article-title":"Data-driven business model innovation for 6G","volume":"9","author":"Rao","year":"2021","journal-title":"J. ICT Stand."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"76606","DOI":"10.1109\/ACCESS.2024.3405487","article-title":"Towards Efficient 6G IoT Networks: A Perspective on Resource Optimization Strategies, Challenges, and Future Directions","volume":"12","author":"Liwen","year":"2024","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Siniarski, B., Sandeepa, C., Wang, S., Liyanage, M., Ayyildiz, C., Yildirim, V.C., Alakoca, H., Kesik, F.G., Paltun, B.G., and Perin, G. (2024, January 2\u20135). ROBUST-6G: Smart, Automated, and Reliable Security Service Platform for 6G. Proceedings of the 2024 Fifteenth International Conference on Ubiquitous and Future Networks (ICUFN), Budapest, Hungary.","DOI":"10.1109\/ICUFN61752.2024.10624832"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1007\/s44196-024-00628-z","article-title":"Optimizing Drone-Based IoT Base Stations in 6G Networks Using the Quasi-opposition-Based Lemurs Optimization Algorithm","volume":"17","author":"Loganathan","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Plastras, S., Tsoumatidis, D., Skoutas, D.N., Rouskas, A., Kormentzas, G., and Skianis, C. (2024). Non-Terrestrial Networks for Energy-Efficient Connectivity of Remote IoT Devices in the 6G Era: A Survey. Sensors, 24.","DOI":"10.3390\/s24041227"},{"key":"ref_17","first-page":"101575","article-title":"IDSoft: A federated and softwarized intrusion detection framework for massive internet of things in 6G network","volume":"35","author":"Alotaibi","year":"2023","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"23115","DOI":"10.1109\/JIOT.2022.3185289","article-title":"Cloud\u2013edge collaborative resource allocation for blockchain-enabled Internet of Things: A collective reinforcement learning approach","volume":"9","author":"Li","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"93542","DOI":"10.1109\/ACCESS.2022.3203711","article-title":"Resource allocation and task off-loading for 6G enabled smart edge environments","volume":"10","author":"Jamil","year":"2022","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Singh, S.P., Kumar, N., Kumar, G., Balusamy, B., Bashir, A.K., and Al-Otaibi, Y.D. (2024). A Hybrid Multi-Objective Optimisation for 6G-Enabled Internet of Things (IoT). IEEE Trans. Consum. Electron.","DOI":"10.1109\/TCE.2024.3411037"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"24662","DOI":"10.1109\/TITS.2022.3209899","article-title":"Softwarized resource management and allocation with autonomous awareness for 6G-enabled cooperative intelligent transportation systems","volume":"23","author":"Cao","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Taneja, A., Alqahtani, A., Saluja, N., and Alqahtani, N. (2023). Robust Resource Control Based on AP Selection in 6G-Enabled IoT Networks. Sensors, 23.","DOI":"10.3390\/s23156788"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"36581","DOI":"10.1109\/JIOT.2024.3416054","article-title":"Knowledge Collaboration-Based Resource Allocation in 6G IoT: A Graph Attention RL Approach","volume":"11","author":"Huang","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ansere, J.A., Kamal, M., Khan, I.A., and Aman, M.N. (2023). Dynamic resource optimization for energy-efficient 6G-IoT ecosystems. Sensors, 23.","DOI":"10.3390\/s23104711"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Taneja, A., Alqahtani, N., and Alqahtani, A. (2023). Interference Aware Resource Control for 6G-Enabled Expanded IoT Networks. Sensors, 23.","DOI":"10.3390\/s23125649"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4989","DOI":"10.1109\/TNSM.2022.3186725","article-title":"A deep-Q learning scheme for secure spectrum allocation and resource management in 6G environment","volume":"19","author":"Bhattacharya","year":"2022","journal-title":"IEEE Trans. Netw. Serv. Manag."},{"key":"ref_27","first-page":"5708","article-title":"Cybertwin-driven resource allocation using deep reinforcement learning in 6G-enabled edge environment","volume":"34","author":"Jain","year":"2022","journal-title":"J. King Saud Univ.-Comput. Inf. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"108210","DOI":"10.1016\/j.compeleceng.2022.108210","article-title":"6G enabled federated learning for secure IoMT resource recommendation and propagation analysis","volume":"102","author":"Ahmed","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_29","unstructured":"Pardoux, E. (1972). Nonlinear stochastic partial differential equations. CR Acad. Sci. Paris."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1137\/0330018","article-title":"Stochastic hamilton\u2013jacobi\u2013bellman equations","volume":"30","author":"Peng","year":"1992","journal-title":"SIAM J. Control. Optim."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"40281","DOI":"10.1109\/ACCESS.2022.3165809","article-title":"Edge-IIoTset: A New Comprehensive Realistic Cyber Security Dataset of IoT and IIoT Applications for Centralized and Federated Learning","volume":"10","author":"Ferrag","year":"2022","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2018.03367731","article-title":"N-BaIoT\u2014Network-Based Detection of IoT Botnet Attacks Using Deep Autoencoders","volume":"17","author":"Meidan","year":"2018","journal-title":"IEEE Pervasive Comput."},{"key":"ref_33","unstructured":"Garcia, S., Parmisano, A., and Erquiaga, M.J. (2020). IoT-23: A Labeled Dataset with Malicious and Benign IoT Network Traffic, Zenodo."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Riley, G.F., and Henderson, T.R. (2010). The ns-3 network simulator. Modeling and Tools for Network Simulation, Springer.","DOI":"10.1007\/978-3-642-12331-3_2"}],"container-title":["Computers"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/1\/10\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:57:41Z","timestamp":1760115461000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-431X\/14\/1\/10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,31]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,1]]}},"alternative-id":["computers14010010"],"URL":"https:\/\/doi.org\/10.3390\/computers14010010","relation":{},"ISSN":["2073-431X"],"issn-type":[{"value":"2073-431X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,31]]}}}