Unmanned Aerial Vehicle Assisted Post-Disaster Communication Coverage Optimization Based on Internet of Things Big Data Analysis

Abstract

The rapid development of Internet of Things (IoT) communication devices has brought about significant convenience. However, simultaneously, the destruction of communication infrastructure in emergency situations often leads to communication disruptions and challenges in information dissemination, severely impacting rescue operations and the safety of the affected individuals. To address this challenge, IoT big data analytics and unmanned aerial vehicle (UAV) technologies have emerged as key elements in the solution. By analyzing large-scale sensor data, user behavior, and communication traffic, IoT big data analytics can provide real-time communication demand prediction and network optimization strategies, offering decision support for post-disaster communication reconstruction. Given the unique characteristics of post-disaster scenarios, this paper proposes a UAV-assisted communication coverage strategy based on IoT big data analytics. This strategy employs UAVs in a cruising manner to assist in communication by partitioning the target area into multiple cells, each satisfying the minimum data requirements for user communication. Depending on the distribution characteristics of users, flight–communication or hover-communication protocols are selectively employed to support communication. By optimizing the UAV’s flight speed and considering the coverage index, fairness index, and average energy efficiency of the mission’s target area, the Inner Spiral Cruise Communication Coverage (IS-CCC) algorithm is proposed to plan the UAV’s cruising trajectory and achieve UAV-based communication coverage. Simulation results demonstrate that this strategy can achieve energy-efficient cruising communication coverage in regions with complex user distributions, thereby reducing energy consumption in UAV-based communication.