Journal of Information Systems Engineering and Management

Journal of Information Systems Engineering and Management, 2023 - Volume 8 Issue 4, Article No: 23845
https://doi.org/10.55267/iadt.07.14044

Published Online: 30 Oct 2023

Views: 279 | Downloads: 184

The Internet of Things (IoT) has recently become a significant focus in research circles. IoT facilitates the integration of numerous physical entities with the Internet. Adhering to a standardized structure is imperative to manage the vast amount of information effectively. Although many researchers in the field of IoT have proposed various layered architectural designs, none have yet fulfilled all the requisite architectural criteria. Network congestion occurs when the volume of data packet traffic surpasses the network's handling capacity. Apart from addressing congestion issues, it is crucial to harmonize network resources like energy, bandwidth, and latency. The Quality of Service (QoS) in IoT applications chiefly depends on proficient congestion management, which is the central subject of this research. The research employs the Adaptive Neuro-Fuzzy Inference System (ANFIS) to regulate congestion, while the Membership Function (MF) undergoes adjustments through the application of the Modified Squirrel Search Algorithm (MSSA). This ANFIS amalgamates the advantages of Fuzzy Logic (FL) and Artificial Neural Networks (ANN) to form a unique framework. Utilizing ANFIS, adaptive analysis services are available to interpret complex patterns and nonlinear interactions, featuring quick learning capabilities. The MSSA aids in tweaking the Membership Function within the ANFIS model, achieving a successful global convergence rate. An adaptive method considering predator presence probability is employed to harmonize the algorithm's exploration and exploitation functionalities, further bolstered by a dimensional search approach. The simulation results demonstrate that the proposed Swarm Intelligence Adaptive Neuro-Fuzzy Inference System (SI-ANFIS) method significantly reduced traffic overhead and attained an impressive accuracy rate of 93.58%.

Internet of Things (IoT) Swarm Intelligence Fuzzy Logic Congestion Membership Function

• Ahmad, R., Wazirali, R., & Abu-Ain, T. (2022). Machine learning for wireless sensor networks security: An overview of challenges and issues. Sensors, 22(13), 4730. https://doi.org/10.3390/s22134730
• Ahmad, T., Madonski, R., Zhang, D., Huang, C., & Mujeeb, A. (2022). Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm. Renewable and Sustainable Energy Reviews, 160, 112128. https://doi.org/10.1016/j.rser.2022.112128
• Akpakwu, G. A., Hancke, G. P.,& Abu‐Mahfouz, A. M. (2020). CACC: Context‐aware congestion control approach for lightweight CoAP/UDP‐based Internet of Things traffic. Transactions on Emerging Telecommunications Technologies, 31(2), e3822. https://doi.org/10.1002/ett.3822
• Alam, S. (2023a). Security Concerns in Smart Agriculture and Blockchain-based Solution. In 2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON). IEEE, 1-6. https://doi.org/10.1109/OTCON56053.2023.10113953
• Alam, S. (2023b). The Current State of Blockchain Consensus Mechanism: Issues and Future Works. International Journal of Advanced Computer Science and Applications, 14(8). https://doi.org/10.14569/IJACSA.2023.0140810.
• Alam, S., Mohammad, O. K. J., Alfurhood, B. S., Mahaveerakannan, R., & Savitha, V. (2023). Effective sound detection system in commercial car vehicles using Msp430 launchpad development. Multimedia Tools and Applications, 1-26. https://doi.org/10.1007/s11042-023-15373-2
• Alawad, F. & Kraemer, F. A. (2022). Value of information in wireless sensor network applications and the IoT: A review. IEEE Sensors Journal, 22(10), 9228-9245. https://doi.org/10.1109/JSEN.2022.3165946
• Ancillotti, E. & Bruno, R. (2019). BDP-CoAP: Leveraging bandwidth-delay product for congestion control in CoAP. In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT). IEEE, 656-661. https://doi.org/10.1109/WF-IoT.2019.8767177
• Ancillotti, E., Bruno, R., Vallati, C., & Mingozzi, E. (2018). Design and evaluation of a rate-based congestion control mechanism in CoAP for IoT applications. In 2018 IEEE 19th International Symposium on" A World of Wireless, Mobile and Multimedia Networks"(WoWMoM). IEEE, 14-15. https://doi.org/10.1109/WoWMoM.2018.8449736
• Aqeel, I., Khormi, I. M., Khan, S. B., Shuaib, M., Almusharraf, A., Alam, S., & Alkhaldi, N. A. (2023). Load Balancing Using Artificial Intelligence for Cloud-Enabled Internet of Everything in Healthcare Domain. Sensors, 23(11), 5349. https://doi.org/10.3390/s23115349
• Bansal, S. & Kumar, D. (2020). Distance‐based congestion control mechanism for CoAP in IoT. IET Communications, 14(19), 3512-3520. https://doi.org/10.1049/iet-com.2020.0486
• Beishenalieva, A. & Yoo, S. J. (2022). Multiobjective 3-D UAV Movement Planning in Wireless Sensor Networks Using Bioinspired Swarm Intelligence. IEEE Internet of Things Journal, 10(9), 8096-8110. https://doi.org/10.1109/JIOT.2022.3231302
• Bolettieri, S., Tanganelli, G., Vallati, C., & Mingozzi, E. (2018). pCoCoA: A precise congestion control algorithm for CoAP. Ad Hoc Networks, 80, 116-129. https://doi.org/10.1016/j.adhoc.2018.06.015
• Chopra, S., Dhiman, G., Sharma, A., Shabaz, M., Shukla, P., & Arora, M. (2021). Taxonomy of adaptive neuro-fuzzy inference system in modern engineering sciences. Computational Intelligence and Neuroscience, 2021. https://doi.org/10.1155/2021/6455592
• Demir, A. K. & Abut, F. (2020). mlCoCoA: a machine learning-based congestion control for CoAP. Turkish Journal of Electrical Engineering and Computer Sciences, 28(5), 2863-2882. https://doi.org/10.3906/elk-2003-17
• Emmanuel, A. A., Awokola, J. A., Alam, S., Bharany, S., Agboola, P., Shuaib, M., & Ahmed, R. (2023). A Hybrid Framework of Blockchain and IoT Technology in the Pharmaceutical Industry: A Comprehensive Study. Mobile Information Systems, 2023. https://doi.org/10.1155/2023/3265310
• Esmaeili, H., Hakami, V., Bidgoli, B. M., & Shokouhifar, M. (2022). Application-specific clustering in wireless sensor networks using combined fuzzy firefly algorithm and random forest. Expert Systems with Applications, 210, 118365. https://doi.org/10.1016/j.eswa.2022.118365
• Ganesh, D. E. N. (2022). Analysis of wireless sensor networks through secure routing protocols using directed diffusion methods. International Journal of Wireless Network Security, 7(1), 28-35. https://doi.org/10.6084/m9.figshare.20417700
• Gashi, L., Luma, A., & Januzaj, Y. (2022). The integration of Wireless Sensor Networks, Mobile Networks and Cloud Engineering for a decision support system-A Systematic Literature Review. In 2022 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA). IEEE, 1-8. https://doi.org/10.1109/HORA55278.2022.9799924
• Hakim, G. P., Habaebi, M. H., Toha, S. F., Islam, M. R., Yusoff, S. H. B., Adesta, E. Y. T., & Anzum, R. (2022). Near Ground Pathloss Propagation Model Using Adaptive Neuro Fuzzy Inference System for Wireless Sensor Network Communication in Forest, Jungle and Open Dirt Road Environments. Sensors, 22(9), 3267. https://doi.org/10.3390/s22093267
• Hkiri, A., Karmani, M., & Machhout, M. (2022). The routing protocol for low power and lossy networks (RPL) under attack: simulation and analysis. In 2022 5th International Conference on Advanced Systems and Emergent Technologies (IC_ASET). IEEE, 143-148. https://doi.org/10.1109/IC_ASET53395.2022.9765901
• Hu, B., Tang, W., & Xie, Q. (2022). A two-factor security authentication scheme for wireless sensor networks in IoT environments. Neurocomputing, 500, 741-749. https://doi.org/10.1016/j.neucom.2022.05.099
• Janani, S. P., Jebadurai, I. J., Paulraj, G. J. L., & Jebadurai, J. (2022). Distributed Brokers in Message Queuing Telemetry Transport: A Comprehensive Review. In 2022 International Conference on Computer Communication and Informatics (ICCCI). IEEE, 1-5. https://doi.org/10.1109/ICCCI54379.2022.9740815
• Jarvinen, I., Raitahila, I., Cao, Z., & Kojo, M. (2018). FASOR retransmission timeout and congestion control mechanism for CoAP. In 2018 IEEE Global Communications Conference (GLOBECOM). IEEE, 1-7. https://doi.org/10.1109/GLOCOM.2018.8647909
• Järvinen, I., Raitahila, I., Cao, Z., & Kojo, M. (2018). Is CoAP congestion safe?. In Proceedings of the Applied Networking Research Workshop, 43-49. https://doi.org/10.1145/3232755.3232857
• Jiang, W., Li, H., Liu, Z., Wu, J., Huang, J., Shan, D., & Wang, J. (2022). Copa+: Analysis and improvement of the delay-based congestion control algorithm copa. In IEEE INFOCOM 2022-IEEE Conference on Computer Communications. IEEE, 920-929. https://doi.org/10.1109/INFOCOM48880.2022.9796913
• Khan, M. A. R., Shavkatovich, S. N., Nagpal, B., Kumar, A., Haq, M. A., Tharini, V. J., ... & Alazzam, M. B. (2022). Optimizing hybrid metaheuristic algorithm with cluster head to improve performance metrics on the IoT. Theoretical Computer Science, 927, 87-97. https://doi.org/10.1016/j.tcs.2022.05.031
• Lăzăroiu, G., Andronie, M., Iatagan, M., Geamănu, M., Ștefănescu, R., & Dijmărescu, I. (2022). Deep learning-assisted smart process planning, robotic wireless sensor networks, and geospatial big data management algorithms in the internet of manufacturing things. ISPRS International Journal of Geo-Information, 11(5), 277. https://doi.org/10.3390/ijgi11050277
• Lilhore, U. K., Khalaf, O. I., Simaiya, S., Tavera Romero, C. A., Abdulsahib, G. M., & Kumar, D. (2022). A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks. International Journal of Distributed Sensor Networks, 18(9), 15501329221117118. https://doi.org/10.1177/15501329221117118
• Luo, H., Wang, X., Xu, Z., Liu, C., & Pan, J. S. (2022). A software-defined multi-modal wireless sensor network for ocean monitoring. International Journal of Distributed Sensor Networks, 18(1), 15501477211068389. https://doi.org/10.1177/15501477211068389
• Majid, M. A. (2022). Energy-efficient adaptive clustering and routing protocol for expanding the life cycle of the IoT-based wireless sensor network. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC). IEEE, 328-336. https://doi.org/10.1109/ICCMC53470.2022.9753809
• Majid, M., Habib, S., Javed, A. R., Rizwan, M., Srivastava, G., Gadekallu, T. R., & Lin, J. C. W. (2022). Applications of wireless sensor networks and internet of things frameworks in the industry revolution 4.0: A systematic literature review. Sensors, 22(6), 2087. https://doi.org/10.3390/s22062087
• Makarem, N., Diab, W. B., Mougharbel, I., & Malouch, N. (2022). On the design of efficient congestion control for the Constrained Application Protocol in IoT. Computer Networks, 207, 108824. https://doi.org/10.1016/j.comnet.2022.108824
• Quwaider, M. & Shatnawi, Y. (2020). Neural network model as Internet of Things congestion control using PID controller and immune-hill-climbing algorithm. Simulation modelling practice and theory, 101, 102022. https://doi.org/10.1016/j.simpat.2019.102022
• Ramya, R., Srinivasan, S., Vasudevan, K., & Poonguzhali, I. (2022). Energy efficient enhanced LEACH protocol for IoT based applications in wireless sensor networks. In 2022 International Conference on Inventive Computation Technologies (ICICT). IEEE, 953-961. https://doi.org/10.1109/ICICT54344.2022.9850776
• Saleem, M., Abbas, S., Ghazal, T. M., Khan, M. A., Sahawneh, N., & Ahmad, M. (2022). Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques. Egyptian Informatics Journal, 23(3), 417-426. https://doi.org/10.1016/j.eij.2022.03.003
• Sharma, G., Sharma, H., Jain, S., & Kumar, N. (2022). Modeling Evapotranspiration in IoT based WSN for Irrigation Scheduling: An Optimized DL Approach. In GLOBECOM 2022-2022 IEEE Global Communications Conference. IEEE, 1948-1953. https://doi.org/10.1109/GLOBECOM48099.2022.10001423
• Shuaib, M., Bhatia, S., Alam, S., Masih, R. K., Alqahtani, N., Basheer, S., & Alam, M. S. (2023). An Optimized, Dynamic, and Efficient Load-Balancing Framework for Resource Management in the Internet of Things (IoT) Environment. Electronics, 12(5), 1104. https://doi.org/10.3390/electronics12051104
• Suwannapong, C. & Khunboa, C. (2019). Congestion control in CoAP observe group communication. Sensors, 19(15), 3433. https://doi.org/10.3390/s19153433
• Suwannapong, C. & Khunboa, C. (2021). EnCoCo-RED: Enhanced congestion control mechanism for CoAP observe group communication. Ad Hoc Networks, 112, 102377. https://doi.org/10.1016/j.adhoc.2020.102377
• Swarna, M. & Godhavari, T. (2021). Enhancement of CoAP based congestion control in IoT network-a novel approach. Materials Today: Proceedings, 37, 775-784. https://doi.org/10.1016/j.matpr.2020.05.817
• Tharini, V. J. & Vijayarani, S. (2020). IoT in healthcare: Ecosystem, pillars, design challenges, applications, vulnerabilities, privacy, and security concerns. In Incorporating the Internet of Things in healthcare applications and wearable devices. IGI Global, 1-22. https://doi.org/10.4018/978-1-7998-1090-2.ch001
• Yakupov, D. (2022). Overview and comparison of protocols Internet of Things: MQTT and AMQP. International Journal of Open Information Technologies, 10(9), 90-98. Retrieved from http://injoit.org/index.php/j1/article/view/1371

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.