A Smart Congestion Control Mechanism for the Green IoT Sensor-Enabled Information-Centric Networking

Area of Science:

• Computer Science
• Networking
• Internet Architecture

Background:

• Information-Centric Networking (ICN) presents a future Internet paradigm but faces challenges in high-congestion, low-power IoT environments.
• Existing IP-based architectures are not optimized for the unique demands of sensor networks in the IoT era.

Purpose of the Study:

• To develop a smart congestion control mechanism for ICN to improve network performance and reduce energy consumption in IoT sensor networks.
• To create a green and efficient ICN-based sensor networking model.

Main Methods:

• Implemented chunk-by-chunk aggregated packets based on content popularity to reduce packet transmission overhead.
• Designed a sensor power-aware cache management strategy.
• Developed an adaptive Markov-based sensor scheduling policy with selective sensing for enhanced power savings.

Main Results:

• The proposed model demonstrated higher network performance efficiency and lower energy consumption in ICN.
• Achieved higher throughput, improved cache hit rates, and reduced Interest packet drop rates.
• Effectiveness validated using ndnSIM, a popular ICN simulator, with increasing numbers of IoT sensors.

Conclusions:

• The developed ICN model offers a viable solution for efficient and low-power sensor networking in the IoT.
• The approach successfully balances network performance enhancement with significant energy savings.
• This contributes to realizing a green and efficient future Internet architecture for IoT applications.