CONTEXT-NET: A context-aware nexus-based aggregation protocol for opportunistic networks

Abstract

Opportunistic Networks enable communication in dynamic, resource-constrained environments using a store-carry-forward approach. However, challenges such as efficient data aggregation, collision avoidance, minimizing data redundancy, and trust management persist. This study proposes the Context-Aware Nexus-Based Aggregation Protocol (CONTEXT-NET), which integrates spatial, temporal, and contextual dimensions for optimized data transmission. CONTEXT-NET employs a nexus ring topology with synchronized sector-based scheduling, autoencoder-based dimensionality reduction, and a hybridized Ant Colony Optimization (ACO)-like routing algorithm for adaptive routing, ensuring minimal collisions and efficient data aggregation. A trust-based scoring system enhances security by identifying and excluding unreliable nodes. The dataset for analysis consists of a customized random dataset with diverse data types, including integers, strings, characters, booleans, and random criticality and priority bits. Experiments conducted in MATLAB demonstrate that CONTEXT-NET achieves stable throughput with a stability percentage of 94.72 %, while improving delivery probability by 6.45 %,reduces one-hop transmission delay by 28 %, end-to-end delay dropping by 7.9 % and mean overhead decreases by 5.96 % as the network scales from 50 to 100 nodes. These results confirm CONTEXT-NET’s ability to maintain consistent performance, enhance reliability, and improve efficiency in large-scale opportunistic networks. Validated across multiple application domains using a customized dataset with diverse data types and criticality levels, CONTEXT-NET emerges as a robust solution for real-world IoT and opportunistic networking applications.