Dynamic Retransmission Count Prediction (DRCP) Algorithm for FANET Using Machine Learning Techniques

Due to miniaturization in electronic devices and cost reduction, Flying Adhoc NETwork (FANET) plays a predominant role in military and civil application domains. To reap the real benefits of FANET, congestion among the highly dynamic devices must be effectively controlled. The traditional Transmission Control Protocol (TCP) and its variants such as TCP Tahoe, TCO Reno, are not suitable for FANET due to the frequent changes in mobility, trajectory localization, traffic and node density. These techniques fix the same retransmission count irrespective of the current network condition. It unnecessarily increases the number of floating packets in the network which triggers further congestion. To mitigate this problem, Dynamic Retransmission Count Prediction (DRCP) algorithm is proposed in this paper to dynamically predict the retransmission count when the timeout timer expires. It utilizes the regression technique to predict the throughput and packet loss rate from the current network condition. With these predicted values and Machine Learning (ML) techniques, the proposed DRCP algorithm predicts the optimal number of retransmission count. The comprehensive simulation of the proposed DRCP with various regression and ML techniques is performed under varying network scenarios. The obtained results such as throughput, jitter, packet loss rate, and delay are compared with other TCP variants. From these analyses, it is shown that the proposed DRCP algorithm outperforms TCP Tahoe with static retransmission count and TCP.