Chasing the Efficient Distributed Leader Election for Edge Computing Networking From Pollution Measuring

Monitoring particulate matter in cities with high population density or in areas with nearby industries (e.g., pollutants or toxins) is essential in these cities and is a necessary response to the increasing rates of diseases associated with air quality. In this context, and framed within the study of Wireless Sensor Networks (WSN), the idea of implementing a sensor network capable of constant monitoring of “particulate matter" arises, which, through the use of AI, can generate predictions of the behavior of this material, to generate a work plan with the corresponding authorities. This research aims to propose a distributed architecture design for a WSN, where the efficient use of sensor node resources (energy, messages, computation, among others) of the “particulate matter” of the environment of study. A distributed leader selection algorithm will send the data from WSN to Edge Computing (EC) to process the collected data and then send the processed information to cloud computing (CC).