###### Abstract
Convolutional Neural Network (CNN) is used in [11] to design a system for monitoring the violation of a home quarantine. This system consists of a software application, cloud database and storage, IoT node, and magnetic switch. Signature Home is proposed in [12], an automated IoT-based geofencing technique to monitor the home quarantined persons, which exploits the special characteristics of networks, such as Wi-Fi and cellular networks, to decide the in/out status of the persons. In [13], the authors proposed a methodology to track COVID-19 zones, to enhance and tighten security rules. They monitor whether a person is entering or leaving a containment area and send an alert message to the person's smartphone. A Geo-COVID approach is proposed in [14] based on a geofencing framework to manage and monitor the people's movement using GPS location. In [15], a geofencing protection system is proposed for home quarantine and monitoring objectives, where GPS and GSM modules are used with a microcontroller-based system to be used as a bracelet. IoT and blockchain technology are used in [16] to collect and analyze the physiological and geographical data for quarantined people. This proposed system provides not only a real-time tracking for quarantined people but also protects their privacy and security. An Android application is developed in [17] to inform persons within the containment zones to avoid entering these areas. This application updates the areas on a Google map to be defined as a containment zone and informs users if they enter these areas and also stores the international mobile equipment identity (IMEI) numbers of these persons to an online database. In [18], the authors propose a detection, tracking and alerting mobile application to manage the COVID-19 spread based on geofencing and ML technologies. This application can detect the presence of virus inside the persons through a ML model and provides an efficient tracking system to track the contacted persons. Then, an alerting system informs these persons with this state. However, the existing approaches for home quarantine [9; 10; 14; 15; 18] depend on the GPS parameters that do not work well and cannot be applied in indoor environments [19]. Furthermore, the offered solutions [11; 15; 16] are high-cost systems and require additional infrastructure. Some of the other areas where IoT, AI, and geofencing technologies can find a way to prevent an epidemic are workplaces and the campus environment. Correct deployment of these technologies can help beat the spread of the virus in these areas. In this paper, an efficient approach is proposed for the workplace environment to control the density of the persons during working hours. This approach will split the workplace environment into a number of geofences, where each room is considered as a geofence. Then, the workplace environment is divided into different virtual cluster areas using a dynamic user-centric clustering scheme, where each person is associated with a set of geofences, forming its own cluster. The proposed approach consists of three modules: a bracelet that will be paired with each person's smartphone using Bluetooth technology, a mobile client app, and a web management platform that helps the workplace manager with applying different control measures. These control measures monitor the movement of persons in the workplace environment, limiting the capacity of the persons in all geofences, detecting whether persons are in the associated geofences, providing the total amount of time during which the person is detected outside their cluster, and alerting and alarming services when persons violate control measures. Different ML techniques are used to generate a unique geofence digital signature (GDS) for each geofence and each building in the workplace environment. Binary classification is applied to predict whether the person is in their cluster or not.