Article Sidebar

Download
PDF
Statistic
Read Counter : 0 Download : 0

Main Article Content

Abstract

The rapid proliferation of IoT devices in metropolitan environments poses critical challenges for heterogeneous device management under minimal centralized control. This paper presents DCRO, a Distributed Coalition-based Resource Orchestration framework enabling IoT devices to self-organize into dynamic coalitions for cooperative resource management. Unlike traditional hierarchical approaches that suffer from scalability bottlenecks, DCRO integrates three core components: a Self-Organizing Device Clustering Algorithm (SODCA) that adapts to topology changes without global coordination; a Game-Theoretic Coalition Formation Mechanism (GT-CFM) that drives fair resource allocation through Shapley value-based negotiation; and a Lightweight Hierarchical Consensus Protocol (LHCP) coupled with a Merkle-DAG security architecture that ensures tamper-resistant coordination without blockchain overhead. Experiments across three metropolitan testbeds demonstrate 26.2% latency reduction and 31.4% energy savings over centralized baselines, only 11.3% throughput degradation under continuous fault injection, and stable coalition convergence at 5,000 devices within 15 iterations.

Keywords

Internet of Things (IoT) Coalition formation Distributed security (Merkle DAG) Edge computing

Article Details

How to Cite
Liu, S., & Chakkaravarthy, M. (2026). Distributed coalition-based resource orchestration for heterogeneous IoT devices in metropolitan smart cities. Future Technology, 5(3), 45–56. Retrieved from https://fupubco.com/futech/article/view/886
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX
Bookmark and Share

References

  1. M. Trigka, E. Dritsas, Edge and cloud computing in smart cities, Future Internet 17(3) (2025) 118. https://doi.org/10.3390/fi17030118
  2. S. Gokulakrishnan, J. Gnanasekar, Peer-toPeer convoluted fault recognition to conquer Single-Point stoppage in Cloud systems, International Journal of Pure and Applied Mathematics 116(21) (2017) 559-577. https://www.researchgate.net/publication/321127306_Peer-to peer_convoluted_fault_recognition_to_conquer_single-point_stoppage_in_Cloud_systems
  3. L. Zhao, J. Wang, J. Liu, N. Kato, Optimal edge resource allocation in IoT-based smart cities, IEEE Network 33(2) (2019) 30-35. DOI: 10.1109/MNET.2019.1800221
  4. Y. Zhang, F. Lyu, P. Yang, W. Wu, J. Gao, IoT intelligence empowered by end-edge-cloud orchestration, China Communications 19(7) (2022) 152-156. DOI: 10.23919/JCC.2022.9837843
  5. N. Kherraf, H.A. Alameddine, S. Sharafeddine, C.M. Assi, A. Ghrayeb, Optimized provisioning of edge computing resources with heterogeneous workload in IoT networks, IEEE Transactions on Network and Service Management 16(2) (2019) 459-474. DOI: 10.1109/TNSM.2019.2894955
  6. C. Chi, Y. Wang, X. Tong, M. Siddula, Z. Cai, Game theory in Internet of Things: A survey, IEEE Internet of Things Journal 9(14) (2021) 12125-12146. DOI: 10.1109/JIOT.2021.3133669
  7. A. Shahraki, A. Taherkordi, Ø. Haugen, F. Eliassen, A survey and future directions on clustering: From WSNs to IoT and modern networking paradigms, IEEE Transactions on Network and Service Management 18(2) (2020) 2242-2274. DOI: 10.1109/TNSM.2020.3035315
  8. S. Shamshirband, J.H. Joloudari, S.K. Shirkharkolaie, S. Mojrian, F. Rahmani, S. Mostafavi, Z. Mansor, Game theory and evolutionary optimization approaches applied to resource allocation problems in computing environments: A survey, Mathematical Biosciences and Engineering 18(6) (2021) 9190-9232. doi: 10.3934/mbe.2021453
  9. S. Durand, K. Khawam, D. Quadri, S. Lahoud, S. Martin, Cross Device Distributed Federated Learning Coalition Formation Game for Constrained IoT, IEEE Internet of Things Journal (2025). DOI: 10.1109/JIOT.2025.3584417
  10. C.-C. Lin, Y. Chiang, H.-Y. Wei, Multi-service edge computing management with multi-stage coalition game task offloading, IEEE Transactions on Network and Service Management 21(3) (2024) 3278-3291. DOI: 10.1109/TNSM.2024.3358414
  11. R. Guo, Z. Guo, Z. Lin, W. Jiang, A hierarchical byzantine fault tolerance consensus protocol for the internet of things, High-Confidence Computing 4(3) (2024) 100196. https://doi.org/10.1016/j.hcc.2023.100196
  12. E.U. Haque, W. Abbasi, A. Almogren, J. Choi, A. Altameem, A.U. Rehman, H. Hamam, Performance enhancement in blockchain based IoT data sharing using lightweight consensus algorithm, Scientific reports 14(1) (2024) 26561. https://doi.org/10.1038/s41598-024-77706-x
  13. R. Du, Z. Wang, J. Shen, Certificateless data integrity auditing with sparse Merkle trees for the cloud-edge environment, Scientific Reports 15(1) (2025) 39202. https://doi.org/10.1038/s41598-025-14041-9
  14. L. Cui, C. Xu, S. Yang, J.Z. Huang, J. Li, X. Wang, Z. Ming, N. Lu, Joint optimization of energy consumption and latency in mobile edge computing for Internet of Things, IEEE Internet of Things Journal 6(3) (2018) 4791-4803. DOI: 10.1109/JIOT.2018.2869226
  15. W. Zhang, H. Ou, Reinforcement learning based multi objective task scheduling for energy efficient and cost effective cloud edge computing, Scientific Reports 15(1) (2025) 41716. https://doi.org/10.1038/s41598-025-25666-1
  16. N.M. Dankolo, N.H.M. Radzi, N.H. Mustaffa, N.I. Arshad, M. Nasser, D. Gabi, M.N. Yusuf, Optimizing resource allocation for IoT applications in the edge cloud continuum using hybrid metaheuristic algorithms, Scientific reports 15(1) (2025) 14409. https://doi.org/10.1038/s41598-025-97648-2
  17. S.A. Memon, D. Andriukaitis, D. Navikas, V. Markevicius, A. Valinevicius, M. Zilys, M. Prauzek, J. Konecny, P. Brida, Z. Li, Centralized and Distributed Controller Placement in Terms of Scalability and Fault Tolerance: A Review, 2025 29th International Conference on Methods and Models in Automation and Robotics (MMAR), IEEE, 2025, pp. 449-454. DOI: 10.1109/MMAR65820.2025.11150828
  18. Z. Ali, A. Mahmood, S. Khatoon, W. Alhakami, S.S. Ullah, J. Iqbal, S. Hussain, A generic Internet of Things (IoT) middleware for smart city applications, Sustainability 15(1) (2022) 743. https://doi.org/10.3390/su15010743
  19. A. Casteigts, P. Flocchini, W. Quattrociocchi, N. Santoro, Time-varying graphs and dynamic networks, International Journal of Parallel, Emergent and Distributed Systems 27(5) (2012) 387-408. https://doi.org/10.1080/17445760.2012.668546
  20. S. Mayukha, R. Vadivel, Optimizing resource allocation in intelligent communication networks: Fundamentals and challenges, Machine Learning for Radio Resource Management and Optimization in 5G and Beyond, CRC Press2025, pp. 15-39. https://www.taylorfrancis.com/chapters/edit/10.1201/9781003514336-2/optimizing-resource-allocation-intelligent-communication-networks-mayukha-vadivel
  21. S. Hudda, K. Haribabu, A review on WSN based resource constrained smart IoT systems, Discover Internet of things 5(1) (2025) 56. https://doi.org/10.1007/s43926-025-00152-2
  22. N.C. Luong, Z. Sui, D. Van Le, J. Cao, B. Ma, N.D. Hai, R. Zhang, V. Van Quang, D. Niyato, S. Feng, Incentive Mechanism Design for Resource Management in Satellite Networks: A Comprehensive Survey, IEEE Internet of Things Journal 13(3) (2025) 3938-3964. DOI: 10.1109/JIOT.2025.3637167
  23. M.H. Amini, J. Mohammadi, S. Kar, Distributed holistic framework for smart city infrastructures: Tale of interdependent electrified transportation network and power grid, Ieee Access 7 (2019) 157535-157554. DOI: 10.1109/JIOT.2025.3637167
  24. J.M. Zolezzi, H. Rudnick, Transmission cost allocation by cooperative games and coalition formation, IEEE Transactions on power systems 17(4) (2003) 1008-1015. DOI: 10.1109/TPWRS.2002.804941
  25. L. Qin, Y. Zhu, S. Liu, X. Zhang, Y. Zhao, The Shapley value in data science: advances in computation, extensions, and applications, Mathematics 13(10) (2025) 1581. https://doi.org/10.3390/math13101581
  26. R. Massin, C.J. Le Martret, P. Ciblat, A coalition formation game for distributed node clustering in mobile ad hoc networks, IEEE Transactions on Wireless Communications 16(6) (2017) 3940-3952. DOI: 10.1109/TWC.2017.2690419