Decentralized Task Allocation Method in Hierarchical IoT Systems Using Fuzzy Logic
DOI:
https://doi.org/10.20535/2786-8729.6.2025.334607Keywords:
Інформаційні системи, Інтернет речей (IoT), хмарні обчислення, туманні обчислення, крайові обчислення, осмотичні обчислення, розподіл задач, нечітка логікаAbstract
The use of fog and edge computing extends the computational capabilities of IoT systems to the network edge, contributing to the minimization of delays during task execution. Osmotic computing complements distributed computing by providing seamless integration between computational environments through dynamic migration of micro-elements across different hierarchy tiers according to current load conditions and resource availability. However, within the concept of osmotic computing, a key challenge remains the effective management of task allocation under conditions of uncertainty, dynamism, and heterogeneity of the IoT environment. The aim of this study is to improve the efficiency of resource utilization and task allocation in hierarchical IoT systems based on osmotic computing under uncertain and dynamically changing environmental conditions. The object of the study is the process of task allocation in multi-tier IoT systems that include cloud, fog, and edge computing. The subject of the study is methods and models for task allocation and computing resource management in IoT systems using the osmotic computing paradigm.
The paper presents a three-tier hierarchical management model built on cloud, fog, and edge environments, which implements a centralized-decentralized management approach. Each tier is represented by a set of computing nodes and a management system that performs local task allocation, resource state monitoring, and micro-element management. The management system of the lower tier is subordinate to the higher-tier management system in the hierarchy. A method for decentralized task allocation in hierarchical IoT systems using fuzzy logic has been developed. The allocation method includes two decision-making stages using a fuzzy inference system: determining the direction of task allocation and selecting the optimal computing node for its execution. The determination of task allocation direction is carried out based on task characteristics, and the suitability rating of computing nodes is determined considering task execution latency, resource utilization efficiency, and load balancing. The task is assigned to the node with the maximum rating. The use of fuzzy logic ensures rational decision-making under conditions of uncertainty in real-time, which is characteristic of highly heterogeneous and dynamic IoT environments.
Experimental modeling and investigation of the method were carried out using the iFogSim simulation environment. The research results show that the percentage of locally executed tasks remains virtually unchanged with different numbers of tasks, indicating stability in decision-making. Increasing the intensity of task generation leads to an increase in task computation latency due to increased load on computing nodes, while task assignment latency and response latency remain unchanged. The method demonstrated adaptability in task allocation for different types of tasks.
References
C. Cole, “IoT 2024 in review: The 10 most relevant IoT developments of the year”, IoT Analytics, Jan. 15, 2025. [Online]. Available: https://iot-analytics.com/iot-2024-review (Accessed: Apr. 27, 2025).
I. Alfonso, M. Alférez, V. Amaral, and D. Díaz, “Self-adaptive architectures in IoT systems: a systematic literature review”, Journal of Internet Services and Applications, vol. 12, 2021, pp. 1–28, https://doi.org/10.1186/s13174-021-00145-8.
O. Rolik, S. Telenyk, and E. Zharikov, “IoT and Cloud Computing: The Architecture of Microcloud-Based IoT Infrastructure Management System”, in Securing the Internet of Things: Concepts, Methodologies, Tools, and Applications, Hershey, PA, USA: IGI Global, 2020, pp. 1157–1185, https://doi.org/10.4018/978-1-5225-9866-4.ch052.
R. Villari, A. Puliafito, M. Fazio, and M. Paone, “Osmotic Computing: A New Paradigm for Edge/Cloud Integration”, IEEE Cloud Computing, vol. 3, no. 7, 2016, pp. 76–83, https://doi.org/10.1109/MCC.2016.124.
B. Neha, M. Shyamala, K. P. Rajan, M. Krishnaveni, and R. Gnanamurthy, “A Systematic Review on Osmotic Computing”, ACM Transactions on Internet of Things, vol. 3, no. 2, 2022, pp. 1–30, https://doi.org/10.1145/3488247.
A. Mahapatra, K. Mishra, R. Pradhan, and S. Majhi, “Next Generation Task Offloading Techniques in Evolving Computing Paradigms: Comparative Analysis, Current Challenges, and Future Research Perspectives”, Archives of Computational Methods in Engineering, 2023, https://doi.org/10.1007/s11831-023-10021-2.
A. Mahapatra, K. Mishra, S. K. Majhi, and R. Pradhan, “Latency-aware Internet of Things Scheduling in Heterogeneous Fog-Cloud Paradigm”, in Proceedings of the 3rd International Conference on Emerging Technology (INCET), Belgaum, India, May 27–29, 2022, IEEE, pp. 1–7, https://doi.org/10.1109/INCET54531.2022.9824613.
S. Wang, T. Zhao, and S. Pang, “Task scheduling algorithm based on improved firework algorithm in fog computing”, IEEE Access, vol. 8, 2020, pp. 32385–32394, https://doi.org/10.1109/ACCESS.2020.2973758.
T. Zheng, J. Wan, J. Zhang, and C. Jiang, “Deep Reinforcement Learning-Based Workload Scheduling for Edge Computing”, Journal of Cloud Computing: Advances, Systems and Applications, vol. 11, article 3, 2022, https://doi.org/10.1186/s13677-021-00276-0.
M. Gamal, R. Rizk, H. Mahdi, and B. E. Elnaghi, “Osmotic bio-inspired load balancing algorithm in cloud computing”, IEEE Access, vol. 7, 2019, pp. 42735–42744, https://doi.org/10.1109/ACCESS.2019.2907615.
E. H. Mamdani and S. Assilian, “An Experiment in Linguistic Synthesis with a Fuzzy Logic Controller”, International Journal of Man‑Machine Studies, vol. 7, no. 1, 1975, pp. 1–13, https://doi.org/10.1016/S0020-7373(75)80002-2.
G. J. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications. Englewood Cliffs, NJ, USA: Prentice Hall, 1995.
H.J. Zimmermann, Fuzzy Set Theory – and Its Applications, 4th ed. Dordrecht, Netherlands: Springer, 2001, https://doi.org/10.1007/978-94-010-0646-0.
A. Mahapatra, S. K. Majhi, K. Mishra, R. Pradhan, D. C. Rao, and S. K. Panda, “An Energy‑Aware Task Offloading and Load Balancing for Latency‑Sensitive IoT Applications in the Fog‑Cloud Continuum”, IEEE Access, vol. 12, 2024, pp. 14334–14349, https://doi.org/10.1109/ACCESS.2024.3357122.
C. Chakraborty, K. Mishra, S. K. Majhi, and H. K. Bhuyan, “Intelligent Latency‑Aware Tasks Prioritization and Offloading Strategy in Distributed Fog‑Cloud of Things”, IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 2099–2106, Feb. 2023, https://doi.org/10.1109/TII.2022.3173899.
W. Jin and A. Rezaeipanah, “Dynamic Task Allocation in Fog Computing Using Enhanced Fuzzy Logic Approaches”, Scientific Reports, vol. 15, no. 1, art. 18513, 2025, https://doi.org/10.1038/s41598-025-03621-4.
D. H. Abdulazeez and S. K. Askar, “A Novel Offloading Mechanism Leveraging Fuzzy Logic and Deep Reinforcement Learning to Improve IoT Application Performance in a Three‑Layer Architecture within the Fog‑Cloud Environment”, IEEE Access, vol. 12, 2024, https://doi.org/10.1109/ACCESS.2024.3376670.
S. Javanmardi, G. Sakellari, M. Shojafar, and A. M. Caruso, “Why It Does Not Work? Metaheuristic Task Allocation Approaches in Fog‑Enabled Internet of Drones”, Simulation Modelling Practice and Theory, vol. 133, 2024, art. 102913, https://doi.org/10.1016/j.simpat.2024.102913.
D. Alsadie, “Advancements in heuristic task scheduling for IoT applications in fog‑cloud computing: challenges and prospects,” PeerJ Computer Science, vol. 10, art. e2128, 2024, https://doi.org/10.7717/peerj-cs.2128.
E. Hamza, M. Bakhouya, and A. Koubâa, “Multi‑objective Fuzzy Approach to Scheduling and Offloading Workflow Tasks in Fog‑Cloud Computing”, Applied Sciences, vol. 13, no. 15, art. 8785, 2023, http://dx.doi.org/10.1016/j.simpat.2022.102687.
M. Villari, A. Puliafito, M. Fazio, S. Dustdar, R. Ranjan, and S. Bonomi, “Software Defined Membrane: Policy-Driven Edge and Internet of Things Security”, IEEE Cloud Computing, vol. 4, no. 4, 2017, pp. 92–99, https://doi.org/10.1109/MCC.2017.3791014.
R. Jain, D. M. Chiu, and W. Hawe, “A Quantitative Measure of Fairness and Discrimination for Resource Allocation in Shared Computer Systems”, arXiv preprint, arXiv:cs/9809099, 1998, https://doi.org/10.48550/arXiv.cs/9809099.
M. R. Mahmud, S. Pallewatta, M. Goudarzi, and R. Buyya, “iFogSim2: An Extended iFogSim Simulator for Mobility, Clustering, and Microservice Management in Edge and Fog Computing Environments”, arXiv preprint, arXiv:2109.05636, 2021, https://doi.org/10.48550/arXiv.2109.05636.
H. Gupta, A. V. Dastjerdi, S. K. Ghosh, and R. Buyya, “iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments”, Software: Practice and Experience, vol. 47, no. 9, pp. 1275–1296, Jun. 2017, https://doi.org/10.1002/spe.2509.
R. Mahmud and R. Buyya, “Modelling and Simulation of Fog and Edge Computing Environments Using iFogSim Toolkit”, arXiv preprint, arXiv:1812.00994, Dec. 2018, https://doi.org/10.48550/arXiv.1812.00994.
R. Andreoli, J. Zhao, T. Cucinotta, and R. Buyya, “CloudSim 7G: An Integrated Toolkit for Modeling and Simulation of Future Generation Cloud Computing Environments”, Software: Practice and Experience, vol. 53, no. 6, pp. 1041–1058, 2025, https://doi.org/10.1002/spe.3413.
M. A. Shahid, M. M. Alam, M. M. Su’ud, and K. Pratap, “A Systematic Parameter Analysis of Cloud Simulation Tools in Cloud Computing Environments”, Applied Sciences, vol. 13, no. 15, art. 8785, 2023, https://doi.org/10.3390/app13158785.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Information, Computing and Intelligent systems
This work is licensed under a Creative Commons Attribution 4.0 International License.
