In this work is presented a hybrid bioinspired optimization technique that associates a General Regression Neural Network (GRNN) with the Multiobjective Bat Algorithm (MOBA), for the design and synthesis of the Frequency Selective Surfaces (FSS), aiming its application in data communication systems by diffusion of millimeter waves, specifically, in the IEEE 802.15.3c standard. The designed device consists of planar arrangements of metallizations (patches), diamond-shaped, arranged over a RO4003 substrate. The FSS proposed in this study presents an operation with ultra-wide band characteristics, its patch designed to cover the range of 40.0 GHz at 70.0 GHz, i.e., 30.0 GHz bandwidth and 60.0 GHz resonance. The upper and lower cutoff frequencies, referring to the transmission coefficient's scattering matrix (dB), were obtained at the cutoff threshold at-10dB, to control the bandwidth of the device.

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One of the key technologies in smart cities is the use of next generation networks such as 5G networks. Mainly because this new mobile technology offers massive connections in densely populated areas in smart cities, thus playing a crucial role for numerous subscribers anytime and anywhere. Indeed, all the most important infrastructure to promote a connected world is being related to next generation networks. Specifically, the small cells transmitters is one of the 5G technologies more relevant to provide more connections and to attend the high demand in smart cities. In this article, a smart small cell positioning is proposed in the context of a smart city. The work proposal aims to do this through the development of a hybrid clustering algorithm with meta-heuristic optimizations to serve users, with real data, of a region satisfying coverage criteria. Furthermore, the problem to be solved will be the best location of the small cells, with the minimization of attenuation between the base stations and its users. The possibilities of using multi-objective optimization algorithms based on bioinspired computing, such as Flower Pollination and Cuckoo Search, will be verified. It will also be analyzed by simulation which power values would allow the continuity of the service with emphasis on three 5G spectrums used around the world: 700 MHz, 2.3 GHz and 3.5 GHz.

This paper aims to provide a metaheuristic approach to drone array optimization applied to coverage area maximization of wireless communication systems, with unmanned aerial vehicle (UAV) base stations, in the context of suburban, lightly to densely wooded environments present in cities of the Amazon region. For this purpose, a low-power wireless area network (LPWAN) was analyzed and applied. LPWAN are systems designed to work with low data rates but keep, or even enhance, the extensive area coverage provided by high-powered networks. The type of LPWAN chosen is LoRa, which operates at an unlicensed spectrum of 915 MHz and requires users to connect to gateways in order to relay information to a central server; in this case, each drone in the array has a LoRa module installed to serve as a non-fixated gateway. In order to classify and optimize the best positioning for the UAVs in the array, three concomitant bioinspired computing (BIC) methods were chosen: cuckoo search (CS), flower pollination algorithm (FPA), and genetic algorithm (GA). Positioning optimization results are then simulated and presented via MATLAB for a high-range IoT-LoRa network. An empirically adjusted propagation model with measurements carried out on a university campus was developed to obtain a propagation model in forested environments for LoRa spreading factors (SF) of 8, 9, 10, and 11. Finally, a comparison was drawn between drone positioning simulation results for a theoretical propagation model for UAVs and the model found by the measurements.