Predictive Tool for Planning the Scaling and Interference of Wireless Sensor Networks

The Internet of Things has introduced a new era of devices capable of sensing different parameters from an environment. Unfortunately, these sensors use multiple standards and are not always designed to work together in a Wireless Body Area Network (WBAN). We studied various applications of the combination of these devices to try and establish an ideal environment which reduces interference and improves data delivery reliability. In this paper we propose a graphical network simulator capable of scaling multiple scenarios, which was tested with a real environment, simulating a WBAN, using five nodes in a ZigBee network where one of them acted as the coordinator and with an approximate overall error rate of ±13.5% in performance metrics such as Packet Delivery Ratio, Link Quality Indicator and Received Signal Strength Indication. Finally, we were able to extrapolate the number of nodes to predict the scalability and the interference influence on a network.