The COVID-19 pandemic brought drastic changes to daily routines and abiding by the guideline for social distancing is necessary to prevent the spread of the virus while easing back to normality. While camera-based social distancing solutions are widely available, they do not preserve the privacy of the user. To the best of our knowledge, there is no practical privacy-preserved social distancing technology in the market. This research employs IoT and Deep Learning to implement an indoor privacy-preserved social distancing wireless sensor network-based system that uses 8×8 low-resolution infrared sensors (AMG8833) to promote social distancing within companies and organizations. This research uses four top view wireless sensor nodes to collect a total of 6,606 low-resolution infrared images, creating a dataset that covers 200 cases of varying numbers of people in diverse locations. The YOLOv4-tiny model achieved a mAP@0.5 of 95.4% and an inference time of 5.16ms when trained on the collected and labeled dataset to detect people. Thus, we conclude that our proposed novel social distancing system is as effective as other solutions that use high-resolution images while maintaining privacy.