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.
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