Optimizing Resources and Increasing the Coverage of Internet-of-Things (IoT) Networks: An Approach Based on LoRaWAN

A resource optimization methodology is proposed for application in long range wide area networks (LoRaWANs). Using variable neighborhood search (VNS) and a minimum-cost spanning tree algorithm, it reduces the implementation and the maintenance costs of such low power networks. Performance evaluations were conducted in LoRaWANs with LoRa repeaters to increase coverage, in scenario where the number and the location of the repeaters are determined by the VNS metaheuristic. Parameters such as spread factor ([Formula: see text]), bandwidth and transmission power were adjusted to minimize the network’s total energy per useful bit ([Formula: see text]) and the total data collection time. The importance of the [Formula: see text] in the trade-off between ([Formula: see text]) and time on-air is evaluated, considering a device scaling factor. Simulation results, obtained after model adjustments with experimental data, show that, in networks with few associated devices, there is a preference for small values of [Formula: see text] aiming at reduction of [Formula: see text]. The usage of large [Formula: see text] ’s becomes relevant when reach extensions are required. The results also demonstrate that, for networks with high number of nodes, the scaling of devices over time become relevant in the fitness function, forcing an equal distribution of time slots per [Formula: see text] to avoid discrepancies in the time data collection.