@article{Pliatsios2021b,

title = {Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches},

author = {Dimitrios Pliatsios and Sotirios K. Goudos and Thomas Lagkas and Vasileios Argyriou and Alexandros Apostolos A. Boulogeorgos and Panagiotis Sarigiannidis},

url = {https://www.researchgate.net/publication/356863442_Drone-Base-Station_for_Next-Generation_Internet-of-Things_A_Comparison_of_Swarm_Intelligence_Approaches},

doi = {10.1109/OJAP.2021.3133459},

issn = {2637-6431},

year  = {2021},

date = {2021-12-07},

journal = {IEEE Open Journal of Antennas and Propagation},

abstract = {The emergence of next-generation internet-of-things (NG-IoT) applications introduces several challenges for the sixth-generation (6G) mobile networks, such as massive connectivity, increased network capacity, and extremely low-latency. To countermeasure the aforementioned challenges, ultra-dense networking has been widely identified as a possible solution. However, the dense deployment of base stations (BSs) is not always possible or cost-efficient. Drone-base-stations (DBSs) can facilitate network expansion and efficiently address the requirements of NG-IoT. In addition, due to their flexibility, they can provide on-demand connectivity in emergency scenarios or address temporary increases in network traffic. Nevertheless, the optimal placement of a DBS is not a straightforward task due to the limited energy reserves and the increased signal quality degradation in air-to-ground links. To this end, swarm intelligence approaches can be attractive solutions for determining the optimal position of the DBS in the three-dimensional (3D) space. In this work, we explore well-known swarm intelligence approaches, namely the Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf Optimization (GWO), Monarch Butterfly Optimization (MBO), Salp Swarm Algorithm (SSA), and Particle Swarm Optimization (PSO) and investigate their performance and efficiency in solving the aforementioned problem. In particular, we investigate the performance of three scenarios in the presence of different swarm intelligence approaches. Additionally, we carry out non-parametric statistical tests, namely the Friedman and Wilcoxon tests, in order to compare the different approaches.},

keywords = {Drone base station, evolutionary algorithms, mobile communications, Optimization methods, Swarm intelligence},

pubstate = {published},

tppubtype = {article}

}

@article{Sotiroudis2021b,

title = {Enhancing Machine Learning Models for Path Loss Prediction Using Image Texture Techniques},

author = {S. Sotiroudis and K. Siakavara and G. Koudouridis and P. Sarigiannidis and S. Goudos},

url = {https://www.researchgate.net/publication/352111245_Enhancing_Machine_Learning_Models_for_Path_Loss_Prediction_Using_Image_Texture_Techniques},

doi = {10.1109/LAWP.2021.3086180},

year  = {2021},

date = {2021-06-03},

journal = {IEEE Antennas and Wireless Propagation Letters},

volume = {(Early Access)},

abstract = {The performance of machine learning-based path loss models relies heavily on the data they use at their inputs. Feature engineering is therefore essential for the models success. In the work at hand, we extract a new set of input features, based on image texture. The image that we use represents the footprint of the urban built-up area, where the gray scale values of the building blocks are analogue to their heights. We extract texture information by applying the Segmentation-based Fractal Texture Analysis algorithm on the orthogonal area that is bounded between the transmitter and the receiver. To the best of our knowledge this is the first time that such a technique is applied to a path loss modeling problem in electromagnetics. The algorithm thus delivers a new set of features, based on the images texture, which eventually reveal the built-up profile of the area. These new features are injected to an already existing feature set. Comparative analysis shows that the addition of texture-based features leads to enhanced predictions, for a diverse set of transmitter heights, machine learning algorithms, and performance metrics.},

keywords = {image texture, machine learning, mobile communications, pathloss prediction},

pubstate = {published},

tppubtype = {article}

}