The explosive growth of Internet of Things (IoT) devices has generated considerable data in diverse networks. This poses serious challenges in collecting timely information and managing frequency resources optimally. In particular, unauthorized access, measurement constraints, and variable channel conditions cause interference, performance degradation, and security compromise, especially in distributed IoT systems. This research presents a comprehensive system for improving data collection in heterogeneous IoT networks. Using complex mathematical models and machine learning algorithms, the system aims to increase the efficiency of frequency resource utilization and reduce interference in network access. A Q-based reinforcement learning method is designed along with an intelligent MAC protocol. Simulation results show that this method increases channel utilization efficiency by 25%, reduces interference probability by 30% compared to traditional methods such as ALOHA, and provides a flexible and scalable solution for frequency resource management. The performance of the proposed system is significantly better than traditional methods, increasing channel utilization efficiency by 25% and reducing the probability of interference by 30%. The system's self-learning capability enables effective frequency resource management even in complex and dense environments. This research presents an innovative method for data collection in IoT networks that combines machine learning and mathematical modeling, providing a secure and scalable solution for the next generation of heterogeneous networks. This system paves the way for designing more stable and efficient networks in various fields, including smart cities and industries.
Fu X, Wang T, Pace P, Aloi G, Fortino G. Low-AoI Data Collection for UAV-Assisted IoT With Dynamic Geohazard Importance Levels. IEEE Internet of Things Journal. 2025;12(11):18279–302.
2.
Revathi ST, Gayathri A, Sathya A, Santhiya M. ECC based Authentication Approach for Secure Communication in IoT Application. Journal of Internet Services and Information Security. 2023;13(4):88–103.
3.
Xu H, Chen X, Huang X, Min G, Chen Y. Uncertainty-aware scheduling for effective data collection from environmental IoT devices through LEO satellites. Future Generation Computer Systems. 2025;166:107656.
4.
Talebifard P, Leung V. Context-Aware Mobility Management in Heterogeneous Network Environments. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2011;2(2):19–32.
5.
Algabroun H, Håkansson L. Parametric Machine Learning-Based Adaptive Sampling Algorithm for Efficient IoT Data Collection in Environmental Monitoring. Journal of Network and Systems Management. 2024;33(1).
6.
Surekha S, Sindhu S, Arvinth N. Bibliometric Study: Natural and Engineering Sciences. Natural and Engineering Sciences. 2024;9(2):376–85.
7.
Galaverni M, Oddi G, Preite L, Belli L, Davoli L, Marchioni I, et al. An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes. Computers and Electronics in Agriculture. 2025;229:109660.
8.
Ghosh TK. Bibliometric Investigation on Research Productivity in Physics, Chemistry and Mathematics in the Indian Institute of Technology (IIT) Kharagpur during 2001-2020. Indian Journal of Information Sources and Services. 2021;11(1):47–57.
9.
Sowe S, Kimata T, Dong M, Zettsu K. Managing heterogeneous sensor data on a big data platform: IoT services for data-intensive science. 2014;295–300.
10.
Singh Palash P, Dhurvey P. ANALYSIS OF FLYASH AGGREGATE BEHAVIOR IN GEOPOLYMER CONCRETE BEAMS USING METHOD OF INITIAL FUNCTIONS (MATHEMATICAL PROGRAMMING). Archives for Technical Sciences. 2024;31(2):168–74.
11.
Singh S, Garg D, Manju, Malik A. A novel cluster head selection algorithm based IoT enabled heterogeneous WSNs distributed architecture for smart city. Microprocessors and Microsystems. 2023;101:104892.
12.
Papadopoulos G, Christodoulou M. Design and Development of Data Driven Intelligent Predictive Maintenance for Predictive Maintenance. Association Journal of Interdisciplinary Technics in Engineering Mechanics. 2024;(2):10–8.
13.
Ali I, Ahmedy I, Gani A, Munir MU, Anisi MH. Data Collection in Studies on Internet of Things (IoT), Wireless Sensor Networks (WSNs), and Sensor Cloud (SC): Similarities and Differences. IEEE Access. 2022;10:33909–31.
14.
Veerappan S. The Role of Digital Ecosystems in Digital Transformation: A Study of How Firms Collaborate and Compete. Global Perspectives in Management. 2023;(1):78–89.
15.
Roy S, Ghosh P, Ghosh N, Das SK. Transcriptional Regulatory Network Topology with Applications to Bio-inspired Networking: A Survey. ACM Computing Surveys. 2021;54(8):1–36.
16.
Kumar K, Kumar D, Praveena B. Image Recoginition CUM Finger Print Analysis Using Internet of Things. International Journal of Advances in Engineering and Emerging Technology. 2019;10(1):26–35.
17.
Duran K, Ozdem M, Gursu K, Canberk B. Q-CSM: Q-Learning-based Cognitive Service Management in Heterogeneous IoT Networks. 2024 IEEE 10th World Forum on Internet of Things (WF-IoT). IEEE; 2024. p. 713–8.
18.
Gunalan N, Kavin Kumar R, Saravanan B, Surya S, Saran Sujai T. Investing Data Flow Issue by using Rayleigh Model in Cloud Computing. International Academic Journal of Innovative Research. 2023;10(1):8–13.
19.
Xiao H, Zhuang Y, Xu C, Wang W, Zhang H, Ding R, et al. Transcoding-Enabled Cloud–Edge–Terminal Collaborative Video Caching in Heterogeneous IoT Networks: An Online Learning Approach With Time-Varying Information. IEEE Internet of Things Journal. 2024;11(1):296–310.
20.
Mohammed AH. Channel Bonding Effects of the IEEE802.11n Standard on the WLANs Performance. International Academic Journal of Science and Engineering. 2024;11(1):213–20.
21.
Diyan M, Nathali Silva B, Han J, Cao Z, Han K. Intelligent Internet of Things gateway supporting heterogeneous energy data management and processing. Transactions on Emerging Telecommunications Technologies. 2020;33(2).
22.
Wang R, Gu C, He S, Shi Z, Meng W. An interoperable and flat Industrial Internet of Things architecture for low latency data collection in manufacturing systems. Journal of Systems Architecture. 2022;129:102631.
23.
Wei Z, Zhu M, Zhang N, Wang L, Zou Y, Meng Z, et al. UAV-Assisted Data Collection for Internet of Things: A Survey. IEEE Internet of Things Journal. 2022;9(17):15460–83.
24.
Moon J, Kum S, Lee S. A Heterogeneous IoT Data Analysis Framework with Collaboration of Edge-Cloud Computing: Focusing on Indoor PM10 and PM2.5 Status Prediction. Sensors. 2019;19(14):3038.
25.
Yao B, Liu X, Zhang WJ, Chen XE, Zhang XM, Yao M, et al. Applying Graph theory to the Internet of Things. 2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing. IEEE; 2013. p. 2354–61.
26.
Srinidhi NN, Dilip Kumar SD, Venugopal KR. Network optimizations in the Internet of Things: A review. Engineering Science and Technology, an International Journal. 2019;22(1):1–21.
27.
Li Y, Xie S, Wan Z, Lv H, Song H, Lv Z. Graph-powered learning methods in the Internet of Things: A survey. Machine Learning with Applications. 2023;11:100441.
28.
Abba Ari AA, Aziz HA, Njoya AN, Aboubakar M, Djedouboum AC, Thiare O, et al. Data collection in IoT networks: Architecture, solutions, protocols and challenges. IET Wireless Sensor Systems. 2024;14(4):85–110.
29.
Yang Y, Hong Y, Fan X, Li D, Chen Z. Joint Optimization of Data Collection for Multi-UAV-and-IRS-Assisted IoT in Urban Scenarios. Drones. 2025;9(2):121.
30.
Wang B, Yang C, Ma J. UKVLDP: Utility-Optimized Local Differential Privacy Mechanism for Key–Value IoT Data Collection. IEEE Internet of Things Journal. 2025;12(11):17964–76.
31.
Ranjan R, Anwit R, Kumar P. Energy efficient and sustainable mobile data collection in internet of things: a variable dimension SSO-based approach. The Journal of Supercomputing. 2024;81(1).
32.
Jeong YS, Kim DR, Shin SS. Efficient data management techniques based on hierarchical IoT privacy using block chains in cloud environments. The Journal of Supercomputing. 2021;77(9):9810–26.
33.
Baek K, Ko IY. MultiFedRL: Efficient Training of Service Agents for Heterogeneous Internet of Things Environments. IEEE Internet of Things Journal. 2025;12(11):16748–60.
34.
Aboubakar M, Kellil M, Roux P. A review of IoT network management: Current status and perspectives. Journal of King Saud University - Computer and Information Sciences. 2022;34(7):4163–76.
35.
D’Emidio M, Delfaraz E, Di Stefano G, Frittella G, Vittoria E. Route Planning Algorithms for Fleets of Connected Vehicles: State of the Art, Implementation, and Deployment. Applied Sciences. 2024;14(7):2884.
36.
Li D, Xu S, Li Y. Massive heterogeneous data collecting in UAV‐assisted wireless IoT networks. IET Communications. 2023;17(14):1706–20.
37.
Xia X, Esmat HH, Lorenzo B, Goeckel D. UAV-Enabled Covert Cross-Technology Communication in Heterogeneous IoT Networks. 2024 IEEE 100th Vehicular Technology Conference (VTC2024-Fall). IEEE; 2024. p. 1–7.
38.
Noaman M, Khan MS, Abrar MF, Ali S, Alvi A, Saleem MA. Challenges in Integration of Heterogeneous Internet of Things. Scientific Programming. 2022;2022:1–14.
The statements, opinions and data contained in the journal are solely those of the individual authors and contributors and not of the publisher and the editor(s). We stay neutral with regard to jurisdictional claims in published maps and institutional affiliations.
,
