With the recent technological advancement in providing exploratory and navigational aids to individuals, especially people with visual impairments, in an indoor area, usage of different kind of markers at Point-of-Interest (POI) has turned out to be one of the efficient ways to cater these needs. Most commonly, these markers are usually Low Energy Bluetooth beacons. These are deployed and configured to provide exploratory aid and navigation instructions. Generally, these beacon nodes are translated into an indoor space graph. Picking out the beacon location strategically and translating this to form a topological graph is a onetime process for any indoor space. However, these tasks could get more tedious and respective for a complex floor plans and can lead to errors. This work presents a study on different stages of beacon infrastructure deployment and a web tool developed to perform these activities. The tool generates the information and posts it to the database that can be downloaded by the smart phone for way finding purpose. The web tool allows building admin to mark beacon on a floor plan and create a graph using these points. Evaluation highlights the difference in time and accuracy for multiple floor plans tested by several users. These evaluation metrics are compared with IBeaconMap [1], a similar tool based on MAT-LAB which automatically determines POI on a floor plan using computer vision and machine learning techniques. The work helps in classifying the beacon deployment process into categories that could be simplified or automated in future.