Self-Assured Opportunistic Routing Algorithm for Ad Hoc Networks

Abstract

This paper proposes a novel self-assured opportunistic routing algorithm for ad hoc networks, aiming to enhance the reliability and efficiency of communication in dynamic and resource-constrained environments. The algorithm leverages local observations, historical data, and adaptive learning mechanisms to enable nodes to make informed routing decisions autonomously. During the initialization phase, nodes initialize routing tables and self-assurance parameters based on network parameters and configuration settings. The route selection strategy involves evaluating potential relay nodes, computing reliability metrics, and selecting the most reliable relay node for packet transmission. Adaptive learning and decision-making mechanisms allow nodes to continuously update routing tables and self-assurance parameters based on feedback from successful and failed delivery attempts. Furthermore, the algorithm dynamically adapts its routing strategies to handle changes in network conditions, including node mobility, topology changes, and traffic patterns. Simulation results demonstrate that the proposed algorithm achieves higher packet delivery ratios and lower message loss rates compared to existing opportunistic routing approaches while minimizing overhead and latency.