Drone-Base-Station for Next-Generation Internet-of-Things: A Comparison of Swarm Intelligence Approaches

Dimitrios Pliatsios,Thomas Lagkas,Panagiotis Sarigiannidis,Sotirios K Goudos,Alexandros-Apostolos A Boulogeorgos,Vasileios Argyriou

doi:10.1109/ojap.2021.3133459

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.

Highlights

Driven by the proliferation of the next-generation internet of things (NG-IoT), future mobile networks will face an unprecedented increase in demand for computational and networking resources
We assume that a single DBS is hovering over the devices and we evaluate the performance of the algorithms in terms of minimizing the average pathloss under various parameters, such as the number of devices, number of search agents, number of generations, and the propagation environment
After the DBSs have been deployed in the optimal location, we present an analysis of the pathloss distribution for a single DBS providing coverage to the devices

Summary

Introduction

Driven by the proliferation of the next-generation internet of things (NG-IoT), future mobile networks will face an unprecedented increase in demand for computational and networking resources. NG-IoT is integrated into multiple applications, such as healthcare [1], [2], smart cities [3], and heavy industry [4]. These applications have increased requirements in terms of device connectivity, network capacity, and link latency. DBSs are flexible solutions that aspire to increase user throughput, improve the quality of service (QoS), and expand the coverage of mobile networks.

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Results

Conclusion