@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{Goudos2019105687,

title = {A Novel Design Approach for 5G Massive MIMO and NB-IoT Green Networks Using a Hybrid Jaya-Differential Evolution Algorithm},

author = { S.K. Goudos and M. Deruyck and D. Plets and L. Martens and K.E. Psannis and P. Sarigiannidis and W. Joseph},

url = {https://www.researchgate.net/publication/334778653_A_Novel_Design_Approach_for_5G_Massive_MIMO_and_NB-IoT_Green_Networks_Using_a_Hybrid_Jaya-Differential_Evolution_Algorithm},

doi = {10.1109/ACCESS.2019.2932042},

year  = {2019},

date = {2019-01-01},

journal = {IEEE Access},

volume = {7},

pages = {105687-105700},

abstract = {Our main objective is to reduce power consumption by responding to the instantaneous bit rate demand by the user for 4th Generation (4G) and 5th Generation (5G) Massive MIMO network configurations. Moreover, we present and address the problem of designing green LTE networks with the Internet of Things (IoT) nodes. We consider the new NarrowBand-IoT (NB-IoT) wireless technology that will emerge in current and future access networks. In this context, we apply emerging evolutionary algorithms in the context of green network design. We investigate three different cases to show the performance of the new proposed algorithm, namely the 4G, 5G Massive MIMO, and the NB-IoT technologies. More specifically, we investigate the Teaching-Learning-Optimization (TLBO), the Jaya algorithm, the self-adaptive differential evolution jDE algorithm, and other hybrid algorithms. We introduce a new hybrid algorithm named Jaya-jDE that uses concepts from both Jaya and jDE algorithms in an effective way. The results show that 5G Massive MIMO networks require about 50% less power consumption than the 4G ones, and the NB-IoT in-band deployment requires about 10% less power than guard-band deployment. Moreover, Jaya-jDE emerges as the best algorithm based on the results. © 2013 IEEE.},

keywords = {4G, 5G, evolutionary algorithms, green networks, hybrid networks, Massive MIMO, NB-IoT, network design, network planning, power consumption},

pubstate = {published},

tppubtype = {article}

}