An Efficient Congestion Control Model utilizing IoT wireless sensors in Information-Centric Networks

Congestion control is one of the essential keys to enhance network efficiency so that the network can perform well even in the case of packet drop. This problem is even more challenging in Information-Centric Networking (ICN), a typical Future Internet design, which employs the packet flooding policy for forwarding the information. To diminish the high traffic load due to the huge number of packets in the era of the Internet of Things (IoT), this paper proposes an effective caching and forwarding algorithm to diminish the congestion rate of the IoT wireless sensor in ICN. The proposed network system utilizes accumulative popularity-based delay transmission time for forwarding strategy and includes the consecutive chunks-based segment caching scheme. The evaluation results using ndnSIM, a widely-used ns-3 based ICN simulator, demonstrated that the proposed system can achieve less interest packet drop rate, more cache hit rate, and higher network throughput, compared to the relevant ICN-based benchmarks. These results prove that the proposed ICN design can achieve higher network efficiency with a lower congestion rate than that of the other related ICN systems using IoT sensors.