ABSTRACT Efficient placement of drone base stations (DBSs) in Internet of Things (IoT) networks plays a vital role in enhancing coverage and minimising signal degradation, particularly in complex urban environments. This paper presents a multi‐objective optimisation framework based on the Strength Pareto Evolutionary Algorithm 2 (SPEA2) to simultaneously maximise user coverage and minimise average path loss. The proposed model integrates realistic 3D urban propagation characteristics and supports flexible deployment across multiple urban environments—urban, dense urban, high‐rise, and suburban. Furthermore, this study leverages SPEA2 to derive optimal three‐dimensional (3D) DBS placements by exploiting its strong non‐dominated sorting and density estimation mechanisms, ensuring robust handling of the conflicting objectives of path‐loss minimisation and coverage maximisation. A comprehensive simulation analysis is conducted, including sensitivity evaluation of key parameters such as user density and threshold signal levels. Results reveal a clear trade‐off between coverage and path loss and demonstrate the model's capacity to generate Pareto‐optimal DBS configurations that suit diverse application scenarios, including emergency response and energy‐constrained deployments. Simulation results show that the optimised SPEA2‐based configuration achieves a minimum average path loss of 86.9 dB with 100 search agents in urban scenarios and provides up to 100% coverage when deploying four or more DBSs at a threshold level of 110 dB. These quantitative results demonstrate the explicit contribution of SPEA2 in enhancing DBS deployment efficiency and resolving the trade‐offs inherent in practical IoT environments.
Fahem et al. (2026) studied this question.