Software para planejamento de redes IoT: uma solução baseada em algoritmo genético, algoritmo de múltiplas tentativas e EPSO

Abstract

The Internet of Things (IoT) allows the ubiquitous monitoring of environments through sensors arranged in a certain area of interest. Such data collection generates unprecedented content of information that is presented to different algorithms that serve to assist in decision-making associated with urban mobility, economy, health, well-being, among others. To ensure the success of this communication chain, defined from the collection of data to the extraction of valuable decisions, it is necessary to implement an end-to-end communication. For this, the IoT makes use of Long Range communication technology (LoRa), which in turn guarantees wireless and cost-free communication between the sensors installed in the endnodes arranged in the area of interest and the data traffic aggregation points installed in the area to be monitored, ie the gateway. Although the solution is practical, there are cost minimization challenges associated with deploying the fewest number of gateways in the area to be covered, as well as the task of planning the IoT network taking into account the optimal positioning of the gateways. Given this context and to respond to the challenges imposed by the planning of IoT networks, this work aims to propose an optimizing software for planning IoT networks based on Genetic Algorithm, Evolutionary Particle Swarm Optimization (EPSO) and Multiple Attempts algorithm, in order to to minimize the number of gateways and determine the geolocation for their installation, thus aiming to guarantee the coverage of all endnodes and their respective sensors arranged in the field.