LAFS: a learning-based adaptive forwarding strategy for NDN-based IoT networks

Area of Science:

• Computer networking
• Internet of Things (IoT)

Background:

• Named Data Networking (NDN) is a data-centric networking model.
• NDN's features like naming and caching are suitable for IoT environments.
• Existing NDN forwarding mechanisms require optimization for IoT constraints.

Purpose of the Study:

• To propose a Learning-based Adaptive Forwarding Strategy (LAFS) for NDN-based IoT networks.
• To enhance network performance and resource efficiency in IoT environments using NDN.

Main Methods:

• Developed LAFS, a learning-based adaptive forwarding strategy.
• Implemented LAFS within the ndnSIM network simulator.
• Compared LAFS against state-of-the-art NDN forwarding schemes.

Main Results:

• LAFS demonstrated superior performance compared to existing solutions.
• Improvements were observed in content retrieval time.
• LAFS achieved a higher request satisfactory rate and reduced energy consumption.

Conclusions:

• LAFS provides a lightweight and adaptive forwarding scheme for NDN in IoT.
• The learning-based approach enables smart collaboration among network nodes.
• LAFS is well-suited for the resource-constrained nature of IoT devices and networks.