AI-Driven Adaptive Routing Algorithms for Securing Next-Generation IoT Mesh Networks
Keywords:
AI, adaptive routing, IoT, mesh networksAbstract
The rapid evolution of Internet of Things (IoT) devices and their deployment in large-scale networks has created a pressing need for robust security mechanisms. IoT mesh networks, which allow devices to communicate directly with each other, are becoming increasingly popular due to their scalability and reliability. However, these networks face significant security challenges, particularly in terms of protecting data integrity, ensuring secure communication, and preventing malicious activities. Traditional routing algorithms struggle to address the dynamic and diverse threats present in IoT environments. This paper presents an AI-driven approach to adaptive routing algorithms for securing next-generation IoT mesh networks. By leveraging artificial intelligence (AI) and machine learning (ML), these algorithms can adapt in real-time to network conditions, identify vulnerabilities, and mitigate potential security risks. The proposed solutions incorporate anomaly detection, intrusion prevention, and secure data routing to enhance the overall security and efficiency of IoT mesh networks. Through a combination of reinforcement learning (RL), deep learning, and AI-driven routing protocols, these algorithms offer a promising solution to securing the dynamic, decentralized nature of IoT mesh networks.
Downloads
References
Smith, J., & Thompson, R. (2020). Machine learning-based routing algorithms for secure IoT networks. Journal of Network Security, 28(3), 125-139.
Liu, S., & Zhang, Q. (2019). AI-driven approaches for enhancing the security of IoT systems. International Journal of Cybersecurity, 15(4), 67-82.
Kumar, A., & Patel, S. (2021). Reinforcement learning in IoT networks: A survey and future directions. IEEE Internet of Things Journal, 8(7), 4739-4754.
Zhang, H., & Lee, M. (2020). A deep learning approach to anomaly detection in IoT networks. Sensors, 20(12), 3456.
Xie, Y., & Zhang, Y. (2022). Adaptive routing protocols for secure IoT mesh networks. Wireless Communications and Mobile Computing, 2022, 1-15.
Shen, B., & Hu, Z. (2021). A deep reinforcement learning-based approach for routing in IoT mesh networks. Future Internet, 13(6), 167.
S. Kumari, “Kanban-Driven Digital Transformation for Cloud-Based Platforms: Leveraging AI to Optimize Resource Allocation, Task Prioritization, and Workflow Automation”, J. of Artificial Int. Research and App., vol. 1, no. 1, pp. 568–586, Jan. 2021
Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.
Madupati, Bhanuprakash. "Blockchain in Day-to-Day Life: Transformative Applications and Implementation." Available at SSRN 5118207 (2021).
S. Kumari, “Digital Transformation Frameworks for Legacy Enterprises: Integrating AI and Cloud Computing to Revolutionize Business Models and Operational Efficiency ”, Journal of AI-Assisted Scientific Discovery, vol. 1, no. 1, pp. 186–204, Jan. 2021
Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.
S. Kumari, “Kanban and AI for Efficient Digital Transformation: Optimizing Process Automation, Task Management, and Cross-Departmental Collaboration in Agile Enterprises”, Blockchain Tech. & Distributed Sys., vol. 1, no. 1, pp. 39–56, Mar. 2021
Madupati, Bhanuprakash. "Kubernetes: Advanced Deployment Strategies-* Technical Perspective." (2021).
