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

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 aforementioned challenges, ultra-dense networking has been widely identified a possible solution. However, dense deployment base stations (BSs) is not always or cost-efficient. Drone-base-stations (DBSs) can facilitate expansion efficiently address requirements NG-IoT. In addition, due to their flexibility, they provide on-demand connectivity in emergency scenarios temporary increases traffic. Nevertheless, optimal placement DBS straightforward task limited energy reserves signal quality degradation air-to-ground links. this end, swarm intelligence approaches be attractive solutions determining position three-dimensional (3D) space. work, we explore well-known approaches, namely Cuckoo Search (CS), Elephant Herd Optimization (EHO), Grey Wolf (GWO), Monarch Butterfly (MBO), Salp Swarm Algorithm (SSA), Particle (PSO) investigate performance efficiency solving problem. particular, three presence different approaches. Additionally, carry out non-parametric statistical tests, Friedman Wilcoxon order compare