Simulation of Obstruction Avoidance Generously Mobility (OAGM) Model using Graph-theory Technique

TL;DR

This paper introduces the OAGM model for obstacle-aware node mobility in sensor networks, utilizing graph theory to optimize pathfinding and improve performance over existing models in critical scenarios.

Contribution

The paper develops the OAGM model incorporating obstacle shapes and graph theory for shortest path calculation, enhancing mobility control in sensor networks.

Findings

OAGM outperforms MCM in simulation tests.

Graph theory effectively finds optimal paths.

OAGM improves message delivery in obstacle-rich environments.

Abstract

An Obstruction Avoidance Generously Mobility (OAGM) model has been introduced for controlling ad-hoc sensor networks and thereby operating emerging fields like military and healthcare services. According to this model, the ability to send a message to a group of users simultaneously, based solely on their geographic location, is desirable by using Mission Critical Mobility model that assumes the obstacle shapes like rectangle or square in the simulation terrain. The OAGM model is developed by grasping the critical situations of military and healthcare services by incorporating the node movement model, hierarchical node organization, placement of obstacle that affect the movement of nodes and also signal propagation. Graph theory technique is used to find the shortest path of the node movement process. The varying number of parameter sets with DSR protocol is analyzed for MCM and OAGM mobility model. The results show OAGM performance is better than MCM.