Sparking Innovation in a Crisis: An IoT Sensor Location-Based Early Warning System for Pandemic Control

Abstract

The COVID-19 pandemic has recently brought attention to several acute human needs. However, it has also demonstrated how crisis can spark innovation in the context of information asymmetry and uncertainty. It has raised the importance of early warning systems (EWS) to prevent similar future events and prepare governments, organizations, and individuals in advance. Such efforts in innovating under uncertainty resemble the process entrepreneurs face when creating new ventures and launching new products and services. Moreover, the increasing adoption of mobile technologies associated with the surging Internet of Things (IoT) devices and applications provides cues to a more extensive discussion about the adoption of sensor networks and applied artificial intelligence algorithms to benefit society and improve its relationship with data. Implementing IoT sensor location-based networks to help reduce community-transmitted infections can be a cost-effective solution that adds to broader pandemic control warning systems. In this paper, a novel predictive location-based early warning system is proposed. The system is able to measure people’s density, people flow, and behavior in specific areas of indoor and outdoor environments. Its implementation has been tested in a real public scenario, showing the capacity to operate flawlessly in real-time, thus addressing the needs of a trusted EWS for governments and organizations to manage event-led situations.

Keywords

• early warning system
• sensors
• sensor data fusion
• internet of things
• artificial intelligence
• pandemic control