Smart Prediction and Trust-based Transmission in Delay-Targeted Networks for Aviation Communica-tion

Abstract

Delay Targeted Networking (DTN) facilitates communication in environments with sporadic connectivity and long delays, such as space missions and isolated locations. The rise of 5G technology has increased the demand for in-flight services, challenging aviation communication to provide reliable data through satellite systems and traditional macro-cellular networks. However, airborne communication's dynamic nature poses significant challenges, including irregular connections and variable delays. To tackle these challenges, a novel Smart Prediction and trAnsmission mechanism for delay taRgeted networK (SPARK) technique has been proposed to enhance the efficiency and reliability of DTNs in aviation communication. The proposed SPARK method includes a comprehensive node trust evaluation system, utilizing direct and indirect trust metrics to ensure network reliability. After evaluating node trustworthiness, the proposed method restricts heavy load traffic based on trustworthiness. The Prediction and Transmission Module incorporates the Cooperative Watchdog System (CWS) to dynamically update each node's reputation score. Nodes are classified into cooperative, partially cooperative, neutral, mislead, and selfish nodes. Experimental results demonstrate the effectiveness of the suggested SPARK framework utilizing evaluation parameters including delivery rate, delay, overhead, hop count, throughput, complexity, and resource utilization. The delay rate of the proposed SPARK method is 18.67%, 19.87%, and 14.45% is lower than the existing OPRNET, IDRL, and CCMA, techniques respectively. The distribution of the proposed SPARK framework attains a forwarding rate of 11% for selfish, and 9.2% for misleading based on their packet forwarding behavior.