DynaPATH: Dynamic Learning Based Indoor Navigation for VIP in IoT Based Environments

Traditionally, pathfinding is solved using classical search algorithms such as the Dijkstra's, A*, probabilistic roadmaps and jump point search. These algorithms are still more practical in a familiar environment that has minimum changes. However, these generated navigation path may lose their appropriateness as they cannot handle dynamic changes in the complex environment, restricting independent living of visually impaired people. Nowadays, a network of smart physical devices called Internet of Things has become a foundation infrastructure for indoor navigation and pathfinding. Although, variations in the environment are identified and stored by sensors, there is absence of a reasonable system that adapts to the variable circumstances and learns to react to the changes. In this paper, we introduce a learning based autonomous system DynaPATH that classifies events of dynamic environments and adapts to the changes. We have performed simulation in order to evaluate the effectiveness of our approach. The results of our approach are compared with performance of different pathfinding algorithms for VIP people. The simulation results display strong conclusion that our proposed system has high stability and is VIP friendly for navigation in a complex environment.