Treffer: Federated learning-based trust and energy-aware routing in Fog-Cloud computing environments for the Internet of Things.
Title:
Federated learning-based trust and energy-aware routing in Fog-Cloud computing environments for the Internet of Things.
Authors:
Source:
Scientific reports [Sci Rep] 2025 Dec 20; Vol. 16 (1), pp. 2244. Date of Electronic Publication: 2025 Dec 20.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: London : Nature Publishing Group, copyright 2011-
References:
Omoniwa, B., Hussain, R., Javed, M. A., Bouk, S. H. & Malik, S. A. Fog/edge computing-based IoT (FECIoT): Architecture, applications, and research issues. IEEE Internet Things J. 6(3), 4118–4149 (2018). (PMID: 10.1109/JIOT.2018.2875544)
Wang, S. & Ma, T. A two-way trust-based routing approach to identify malicious and energy-aware nodes in fog computing. Clust. Comput. 28(7), 460 (2025). (PMID: 10.1007/s10586-025-05254-8)
Heng, L., Yin, G. & Zhao, X. Energy aware cloud-edge service placement approaches in the Internet of Things communications. Int. J. Commun. Syst. 35(1), e4899 (2022). (PMID: 10.1002/dac.4899)
Mir, M. & Trik, M. A novel intrusion detection framework for industrial IoT: GCN-GRU architecture optimized with ant colony optimization. Comput. Electr. Eng. 126, 110541 (2025). (PMID: 10.1016/j.compeleceng.2025.110541)
Wang, Z., Jin, Z., Yang, Z., Zhao, W. & Trik, M. Increasing efficiency for routing in internet of things using binary gray wolf optimization and fuzzy logic. J. King Saud Univ. Comput. Inf. Sci. 35(9), 101732 (2023). (PMID: 10.1016/j.jksuci.2023.101732)
Chen, Y. et al. GENDN: A geospatially enhanced NDN framework for location-related pub/sub services in NTN-enabled IoT. IEEE Internet Things J. 12(7), 8381–8393. https://doi.org/10.1109/JIOT.2024.3507533 (2025). (PMID: 10.1109/JIOT.2024.3507533)
Li, Y., Yi, Z., Guo, D., Luo, L., Ren, B. & Zhang, Q. (2025). Joint communication and offloading strategy of CoMP UAV-assisted MEC networks. IEEE Internet Things J. 1 https://doi.org/10.1109/JIOT.2025.3588840.
Xu, G. et al. RAT ring: Event driven publish/subscribe communication protocol for IIoT by report and traceable ring signature. IEEE Trans. Ind. Inform. 21(9), 6670–6678. https://doi.org/10.1109/TII.2025.3567265 (2025). (PMID: 10.1109/TII.2025.3567265)
Xu, G. et al. MPAEE: A multipath adaptive energy-efficient routing scheme for low earth orbit-based industrial internet of things. IEEE Internet Things J. 12(17), 34793–34805. https://doi.org/10.1109/JIOT.2025.3581314 (2025). (PMID: 10.1109/JIOT.2025.3581314)
Zhang, K., Zheng, B., Xue, J. & Zhou, Y. Explainable and trust-aware AI-driven network slicing framework for 6G IoT using deep learning. IEEE Internet Things J. 1 https://doi.org/10.1109/JIOT.2025.3619970 (2025).
Xu, G. et al. Towards authenticated encrypted search with constant trapdoor for mobile cloud systems. IEEE Trans. Mobile Comput. https://doi.org/10.1109/TMC.2025.3627241 (2025). (PMID: 10.1109/TMC.2025.3627241)
Xu, G. et al. CBRFL: A framework for committee-based byzantine-resilient federated learning. J. Netw. Comput. Appl. 238, 104165. https://doi.org/10.1016/j.jnca.2025.104165 (2025). (PMID: 10.1016/j.jnca.2025.104165)
Lu, K., Wang, J. & Li, M. An Eigentrust dynamic evolutionary model in P2P file-sharing systems. Peer-to-Peer Netw. Appl. 9(3), 599–612 (2016). (PMID: 10.1007/s12083-015-0416-1)
Zhang, Y., Yu, Y., Sun, W. & Cao, Z. A survey of trust management for the internet of things: Taxonomy, challenges, and future directions. IEEE Internet Things J. 10(15), 13645–13667. https://doi.org/10.1109/JIOT.2023.3241125 (2023). (PMID: 10.1109/JIOT.2023.3241125)
Shakya, A., & Kaushik, A. TAGA-FLEACH: Energy-efficient and secure clustering in WSNs using trust-aware GA-FLEACH with dead-hole monitoring. Ad Hoc Netw. 103990. (2025).
Mujeeb, S. M., Sam, R. P. & Madhavi, K. Trust and energy aware routing algorithm for internet of things networks. Int. J. Numer. Model. Electron. Netw. Devices Fields 34(4), e2858. https://doi.org/10.1002/jnm.2858 (2021). (PMID: 10.1002/jnm.2858)
Arulselvan, G. & Rajaram, A. Routing attacks detection in MANET using trust management enabled hybrid machine learning. Wirel. Netw. 31(2), 1481–1495 (2025). (PMID: 10.1007/s11276-024-03846-7)
Marina, M. K. & Das, S. R. Ad hoc on-demand multipath distance vector routing. Wirel. Commun. Mob. Comput. 6(7), 969–988. https://doi.org/10.1002/wcm.432 (2006). (PMID: 10.1002/wcm.432)
Kaur, A. & Kaur, P. E-ODMA: Energy-efficient optimized dynamic multipath adaptive routing protocol for IoT networks. Wirel. Netw. 27(8), 5195–5212. https://doi.org/10.1007/s11276-021-02706-1 (2021). (PMID: 10.1007/s11276-021-02706-1)
Zhang, Y., Yu, Y., Sun, W. & Cao, Z. Towards an energy-aware two-way trust routing scheme in fog computing environments. Telecommun. Syst. 87(4), 973–989 (2024). (PMID: 10.1007/s11235-024-01226-2)
Fu, T. et al. An energy-aware secure routing scheme in internet of things networks via two-way trust evaluation. Pervasive Mob. Comput. 105, 101995 (2024). (PMID: 10.1016/j.pmcj.2024.101995)
Liu, X., Tan, Z., Liang, L., & Li, G. A multidimensional trust evaluation mechanism for improving network security in fog computing. IEEE Trans. Ind. Inf. (2024).
Ogundoyin, S. O. & Kamil, I. A. A trust management system for fog computing services. Internet Things 14, 100382 (2021). (PMID: 10.1016/j.iot.2021.100382)
Fang, W., Zhang, W., Chen, W., Liu, Y. & Tang, C. TMSRS: Trust management-based secure routing scheme in industrial wireless sensor network with fog computing. Wirel. Netw. 26(5), 3169–3182 (2020). (PMID: 10.1007/s11276-019-02129-w)
Malik, T. S. et al. An efficient and secure fog-based routing mechanism in IoT network. Mathematics 11(17), 3652 (2023). (PMID: 10.3390/math11173652)
Bounaira, S., Alioua, A. & Souici, I. Blockchain-enabled trust management for secure content caching in mobile edge computing using deep reinforcement learning. Internet Things 25, 101081 (2024). (PMID: 10.1016/j.iot.2024.101081)
Premalatha, B. & Prakasam, P. TwI-FTM: Two-way IoT–FoG trust management scheme for task offloading in IoT–FoG networks. Res. Eng. 22, 102197 (2024).
Hao, J., Chen, P., Chen, J. & Li, X. Effectively detecting and diagnosing distributed multivariate time series anomalies via unsupervised federated hypernetwork. Inf. Process. Manage. 62(4), 104107 (2025). (PMID: 10.1016/j.ipm.2025.104107)
Din, I. U. et al. LightTrust: Lightweight trust management for edge devices in industrial internet of things. IEEE Internet Things J. 10(4), 2776–2783 (2021). (PMID: 10.1109/JIOT.2021.3081422)
Wang, K. & Tan, C. W. Reverse Engineering Segment Routing Policies and Link Costs With Inverse Reinforcement Learning and EM. IEEE Trans. Mach. Learn. Commun. Netw. 3, 1014–1029. https://doi.org/10.1109/TMLCN.2025.3598739 (2025). (PMID: 10.1109/TMLCN.2025.3598739)
Yao, Y., Xiao, W., Miao, P., Chen, G., Yang, H., Chae, C. & Wong, K. UAV-relay-aided secure maritime networks coexisting with satellite networks: robust beamforming and trajectory optimization. IEEE Trans. on Wirel. Commun. 1 https://doi.org/10.1109/TWC.2025.3596136 (2025).
Mao, Y., You, C., Zhang, J., Huang, K. & Letaief, K. B. A survey on mobile edge computing: The communication perspective. IEEE Commun. Surv. Tutor. 19(4), 2322–2358 (2017). (PMID: 10.1109/COMST.2017.2745201)
Abbas, Y. et al. Drone-based public surveillance using 3D point clouds and neuro-fuzzy classifier. Comput. Mater. Contin. 82(3), 4759–4776. https://doi.org/10.32604/cmc.2025.059224 (2025). (PMID: 10.32604/cmc.2025.059224)
Manning, C. D., Raghavan, P. & Schütze, H. Introduction to Information Retrieval (Cambridge University Press, 2008). (PMID: 10.1017/CBO9780511809071)
Kairouz, P. et al. Advances and open problems in federated learning. Found. Trends® Mach. Learn. 14(1–2), 1–210 (2021).
Cheon, J. H., Kim, A., Kim, M. & Song, Y. Homomorphic encryption for arithmetic of approximate numbers. In ASIACRYPT 409–437 (2017).
He, W., Tan, J., Wang, R., Liu, Z., Luo, X., Hu, H. & Zhang, H. A deep reinforcement learning approach to time delay differential game deception resource deployment. IEEE Trans. Dependable Secure Comput. 1–16 https://doi.org/10.1109/TDSC.2025.3620151 (2025).
Ren, Y. et al. Is cooperative always better? Multi-agent reinforcement learning with explicit neighborhood backtracking for network-wide traffic signal control. Transp. Res. Part C Emerg. Technol. 179, 105265. https://doi.org/10.1016/j.trc.2025.105265 (2025). (PMID: 10.1016/j.trc.2025.105265)
Sun, G., Liao, D., Zhao, D., Xu, Z. & Yu, H. Live migration for multiple correlated virtual machines in cloud-based data centers. IEEE Trans. Serv. Comput. 11(2), 279–291. https://doi.org/10.1109/TSC.2015.2477825 (2018). (PMID: 10.1109/TSC.2015.2477825)
Zhu, B., Zhao, N., Niu, B., Zong, G., Zhao, X. Distributed Adaptive Optimized Sliding-Mode Time-Varying Formation Control With Prescribed-Time Performance Constraints for Nonlinear Heterogeneous Multiagent Systems, IEEE INTERNET OF THINGS JOURNAL, https://doi.org/10.1109/JIOT.2025.3626164 (2025).
Zhu, H., Xu, J., Liu, S. & Jin, Y. Federated learning on non-IID data: A survey. Neurocomputing 465, 371–390 (2021). (PMID: 10.1016/j.neucom.2021.07.098)
Xu, H., Zhao, N., Xu, N., Niu, B., Zhao, X. Reinforcement learning-based dynamic event-triggered prescribed performance control for nonlinear systems with input delay. Int. J. Syst. Sci., https://doi.org/10.1080/00207721.2025.2557528 (2025).
Chen, P., Luo, L., Guo, D., Wu, J., Chi, K., Yan, C.,... Dong, X. QoS-oriented Task Offloading in NOMA-based Multi-UAV Cooperative MEC Systems. IEEE Transactions on Wireless Communications, 1, https://doi.org/10.1109/TWC.2025.3593884 (2025).
Sun, G., Zhang, Y., Liao, D., Yu, H., Du, X.,... Guizani, M. Bus-Trajectory-Based Street-Centric Routing for Message Delivery in Urban Vehicular Ad Hoc Networks. IEEE Transactions on Vehicular Technology, 67(8), 7550–7563. https://doi.org/10.1109/TVT.2018.2828651 (2018).
Almudayni, Z., Soh, B., Samra, H. & Li, A. Energy inefficiency in IoT networks: causes, impact, and a strategic framework for sustainable optimisation. Electronics 14(1), 159 (2025). (PMID: 10.3390/electronics14010159)
Wang, S. & Ma, T. A two-way trust-based routing approach to identify malicious and energy-aware nodes in fog computing. Clust. Comput. 28(7), 460 (2025). (PMID: 10.1007/s10586-025-05254-8)
Heng, L., Yin, G. & Zhao, X. Energy aware cloud-edge service placement approaches in the Internet of Things communications. Int. J. Commun. Syst. 35(1), e4899 (2022). (PMID: 10.1002/dac.4899)
Mir, M. & Trik, M. A novel intrusion detection framework for industrial IoT: GCN-GRU architecture optimized with ant colony optimization. Comput. Electr. Eng. 126, 110541 (2025). (PMID: 10.1016/j.compeleceng.2025.110541)
Wang, Z., Jin, Z., Yang, Z., Zhao, W. & Trik, M. Increasing efficiency for routing in internet of things using binary gray wolf optimization and fuzzy logic. J. King Saud Univ. Comput. Inf. Sci. 35(9), 101732 (2023). (PMID: 10.1016/j.jksuci.2023.101732)
Chen, Y. et al. GENDN: A geospatially enhanced NDN framework for location-related pub/sub services in NTN-enabled IoT. IEEE Internet Things J. 12(7), 8381–8393. https://doi.org/10.1109/JIOT.2024.3507533 (2025). (PMID: 10.1109/JIOT.2024.3507533)
Li, Y., Yi, Z., Guo, D., Luo, L., Ren, B. & Zhang, Q. (2025). Joint communication and offloading strategy of CoMP UAV-assisted MEC networks. IEEE Internet Things J. 1 https://doi.org/10.1109/JIOT.2025.3588840.
Xu, G. et al. RAT ring: Event driven publish/subscribe communication protocol for IIoT by report and traceable ring signature. IEEE Trans. Ind. Inform. 21(9), 6670–6678. https://doi.org/10.1109/TII.2025.3567265 (2025). (PMID: 10.1109/TII.2025.3567265)
Xu, G. et al. MPAEE: A multipath adaptive energy-efficient routing scheme for low earth orbit-based industrial internet of things. IEEE Internet Things J. 12(17), 34793–34805. https://doi.org/10.1109/JIOT.2025.3581314 (2025). (PMID: 10.1109/JIOT.2025.3581314)
Zhang, K., Zheng, B., Xue, J. & Zhou, Y. Explainable and trust-aware AI-driven network slicing framework for 6G IoT using deep learning. IEEE Internet Things J. 1 https://doi.org/10.1109/JIOT.2025.3619970 (2025).
Xu, G. et al. Towards authenticated encrypted search with constant trapdoor for mobile cloud systems. IEEE Trans. Mobile Comput. https://doi.org/10.1109/TMC.2025.3627241 (2025). (PMID: 10.1109/TMC.2025.3627241)
Xu, G. et al. CBRFL: A framework for committee-based byzantine-resilient federated learning. J. Netw. Comput. Appl. 238, 104165. https://doi.org/10.1016/j.jnca.2025.104165 (2025). (PMID: 10.1016/j.jnca.2025.104165)
Lu, K., Wang, J. & Li, M. An Eigentrust dynamic evolutionary model in P2P file-sharing systems. Peer-to-Peer Netw. Appl. 9(3), 599–612 (2016). (PMID: 10.1007/s12083-015-0416-1)
Zhang, Y., Yu, Y., Sun, W. & Cao, Z. A survey of trust management for the internet of things: Taxonomy, challenges, and future directions. IEEE Internet Things J. 10(15), 13645–13667. https://doi.org/10.1109/JIOT.2023.3241125 (2023). (PMID: 10.1109/JIOT.2023.3241125)
Shakya, A., & Kaushik, A. TAGA-FLEACH: Energy-efficient and secure clustering in WSNs using trust-aware GA-FLEACH with dead-hole monitoring. Ad Hoc Netw. 103990. (2025).
Mujeeb, S. M., Sam, R. P. & Madhavi, K. Trust and energy aware routing algorithm for internet of things networks. Int. J. Numer. Model. Electron. Netw. Devices Fields 34(4), e2858. https://doi.org/10.1002/jnm.2858 (2021). (PMID: 10.1002/jnm.2858)
Arulselvan, G. & Rajaram, A. Routing attacks detection in MANET using trust management enabled hybrid machine learning. Wirel. Netw. 31(2), 1481–1495 (2025). (PMID: 10.1007/s11276-024-03846-7)
Marina, M. K. & Das, S. R. Ad hoc on-demand multipath distance vector routing. Wirel. Commun. Mob. Comput. 6(7), 969–988. https://doi.org/10.1002/wcm.432 (2006). (PMID: 10.1002/wcm.432)
Kaur, A. & Kaur, P. E-ODMA: Energy-efficient optimized dynamic multipath adaptive routing protocol for IoT networks. Wirel. Netw. 27(8), 5195–5212. https://doi.org/10.1007/s11276-021-02706-1 (2021). (PMID: 10.1007/s11276-021-02706-1)
Zhang, Y., Yu, Y., Sun, W. & Cao, Z. Towards an energy-aware two-way trust routing scheme in fog computing environments. Telecommun. Syst. 87(4), 973–989 (2024). (PMID: 10.1007/s11235-024-01226-2)
Fu, T. et al. An energy-aware secure routing scheme in internet of things networks via two-way trust evaluation. Pervasive Mob. Comput. 105, 101995 (2024). (PMID: 10.1016/j.pmcj.2024.101995)
Liu, X., Tan, Z., Liang, L., & Li, G. A multidimensional trust evaluation mechanism for improving network security in fog computing. IEEE Trans. Ind. Inf. (2024).
Ogundoyin, S. O. & Kamil, I. A. A trust management system for fog computing services. Internet Things 14, 100382 (2021). (PMID: 10.1016/j.iot.2021.100382)
Fang, W., Zhang, W., Chen, W., Liu, Y. & Tang, C. TMSRS: Trust management-based secure routing scheme in industrial wireless sensor network with fog computing. Wirel. Netw. 26(5), 3169–3182 (2020). (PMID: 10.1007/s11276-019-02129-w)
Malik, T. S. et al. An efficient and secure fog-based routing mechanism in IoT network. Mathematics 11(17), 3652 (2023). (PMID: 10.3390/math11173652)
Bounaira, S., Alioua, A. & Souici, I. Blockchain-enabled trust management for secure content caching in mobile edge computing using deep reinforcement learning. Internet Things 25, 101081 (2024). (PMID: 10.1016/j.iot.2024.101081)
Premalatha, B. & Prakasam, P. TwI-FTM: Two-way IoT–FoG trust management scheme for task offloading in IoT–FoG networks. Res. Eng. 22, 102197 (2024).
Hao, J., Chen, P., Chen, J. & Li, X. Effectively detecting and diagnosing distributed multivariate time series anomalies via unsupervised federated hypernetwork. Inf. Process. Manage. 62(4), 104107 (2025). (PMID: 10.1016/j.ipm.2025.104107)
Din, I. U. et al. LightTrust: Lightweight trust management for edge devices in industrial internet of things. IEEE Internet Things J. 10(4), 2776–2783 (2021). (PMID: 10.1109/JIOT.2021.3081422)
Wang, K. & Tan, C. W. Reverse Engineering Segment Routing Policies and Link Costs With Inverse Reinforcement Learning and EM. IEEE Trans. Mach. Learn. Commun. Netw. 3, 1014–1029. https://doi.org/10.1109/TMLCN.2025.3598739 (2025). (PMID: 10.1109/TMLCN.2025.3598739)
Yao, Y., Xiao, W., Miao, P., Chen, G., Yang, H., Chae, C. & Wong, K. UAV-relay-aided secure maritime networks coexisting with satellite networks: robust beamforming and trajectory optimization. IEEE Trans. on Wirel. Commun. 1 https://doi.org/10.1109/TWC.2025.3596136 (2025).
Mao, Y., You, C., Zhang, J., Huang, K. & Letaief, K. B. A survey on mobile edge computing: The communication perspective. IEEE Commun. Surv. Tutor. 19(4), 2322–2358 (2017). (PMID: 10.1109/COMST.2017.2745201)
Abbas, Y. et al. Drone-based public surveillance using 3D point clouds and neuro-fuzzy classifier. Comput. Mater. Contin. 82(3), 4759–4776. https://doi.org/10.32604/cmc.2025.059224 (2025). (PMID: 10.32604/cmc.2025.059224)
Manning, C. D., Raghavan, P. & Schütze, H. Introduction to Information Retrieval (Cambridge University Press, 2008). (PMID: 10.1017/CBO9780511809071)
Kairouz, P. et al. Advances and open problems in federated learning. Found. Trends® Mach. Learn. 14(1–2), 1–210 (2021).
Cheon, J. H., Kim, A., Kim, M. & Song, Y. Homomorphic encryption for arithmetic of approximate numbers. In ASIACRYPT 409–437 (2017).
He, W., Tan, J., Wang, R., Liu, Z., Luo, X., Hu, H. & Zhang, H. A deep reinforcement learning approach to time delay differential game deception resource deployment. IEEE Trans. Dependable Secure Comput. 1–16 https://doi.org/10.1109/TDSC.2025.3620151 (2025).
Ren, Y. et al. Is cooperative always better? Multi-agent reinforcement learning with explicit neighborhood backtracking for network-wide traffic signal control. Transp. Res. Part C Emerg. Technol. 179, 105265. https://doi.org/10.1016/j.trc.2025.105265 (2025). (PMID: 10.1016/j.trc.2025.105265)
Sun, G., Liao, D., Zhao, D., Xu, Z. & Yu, H. Live migration for multiple correlated virtual machines in cloud-based data centers. IEEE Trans. Serv. Comput. 11(2), 279–291. https://doi.org/10.1109/TSC.2015.2477825 (2018). (PMID: 10.1109/TSC.2015.2477825)
Zhu, B., Zhao, N., Niu, B., Zong, G., Zhao, X. Distributed Adaptive Optimized Sliding-Mode Time-Varying Formation Control With Prescribed-Time Performance Constraints for Nonlinear Heterogeneous Multiagent Systems, IEEE INTERNET OF THINGS JOURNAL, https://doi.org/10.1109/JIOT.2025.3626164 (2025).
Zhu, H., Xu, J., Liu, S. & Jin, Y. Federated learning on non-IID data: A survey. Neurocomputing 465, 371–390 (2021). (PMID: 10.1016/j.neucom.2021.07.098)
Xu, H., Zhao, N., Xu, N., Niu, B., Zhao, X. Reinforcement learning-based dynamic event-triggered prescribed performance control for nonlinear systems with input delay. Int. J. Syst. Sci., https://doi.org/10.1080/00207721.2025.2557528 (2025).
Chen, P., Luo, L., Guo, D., Wu, J., Chi, K., Yan, C.,... Dong, X. QoS-oriented Task Offloading in NOMA-based Multi-UAV Cooperative MEC Systems. IEEE Transactions on Wireless Communications, 1, https://doi.org/10.1109/TWC.2025.3593884 (2025).
Sun, G., Zhang, Y., Liao, D., Yu, H., Du, X.,... Guizani, M. Bus-Trajectory-Based Street-Centric Routing for Message Delivery in Urban Vehicular Ad Hoc Networks. IEEE Transactions on Vehicular Technology, 67(8), 7550–7563. https://doi.org/10.1109/TVT.2018.2828651 (2018).
Almudayni, Z., Soh, B., Samra, H. & Li, A. Energy inefficiency in IoT networks: causes, impact, and a strategic framework for sustainable optimisation. Electronics 14(1), 159 (2025). (PMID: 10.3390/electronics14010159)
Contributed Indexing:
Keywords: Context-aware computing; Energy-aware routing; Fog–Cloud collaboration; Hybrid IoT networks; Reinforcement learning; Secure data transmission; Trust management
Entry Date(s):
Date Created: 20251220 Latest Revision: 20260122
Update Code:
20260122
PubMed Central ID:
PMC12816647
DOI:
10.1038/s41598-025-32010-0
PMID:
41422288
Database:
MEDLINE
Weitere Informationen
The rapid convergence of Fog, Cloud, and Internet of Things (IoT) technologies has introduced a new era of distributed intelligence and real-time data processing. However, ensuring secure, reliable, and energy-efficient communication across heterogeneous and resource-constrained nodes remains a fundamental challenge. This paper introduces a novel framework entitled Federated Learning-Based Trust and Energy-Aware Routing (FL-TEAR), designed to enhance routing performance in hybrid Fog-Cloud-IoT environments through collaborative intelligence, adaptive trust management, and dynamic energy optimization. The FL-TEAR system replaces static trust evaluation with a federated learning paradigm, allowing IoT and fog nodes to cooperatively train a global trust-energy model without exposing raw data. Trust scores are continuously refined based on behavioral patterns, communication reliability, and residual energy, while routing paths are selected using a composite fitness function integrating trustworthiness, energy availability, latency, and link stability. The hierarchical architecture, spanning IoT, fog, and cloud layers, reduces communication overhead, supports scalability, and preserves privacy. Simulation results confirm that FL-TEAR significantly outperforms state-of-the-art baselines such as E-ODMA (Energy-Efficient On-Demand Multipath Adaptive) + AOMDV (Ad hoc On-Demand Multipath Distance Vector), TAGA (Trust-Aware Geographic Routing Algorithm), and EigenTrust, achieving approximately 23% higher trust accuracy, 23% lower energy consumption, approximately 13% greater packet delivery ratio, and 37% lower delay. These findings demonstrate that federated learning can effectively balance security, sustainability, and quality of service (QoS) in large-scale IoT ecosystems, establishing FL-TEAR as a viable pathway toward intelligent, secure, and energy-efficient next-generation networks.
(© 2025. The Author(s).)
Declarations. Competing interests: The authors declare no competing interests.