Yang Biao, Xiong Xuanrui, Liu He, Jia Yumei, Gao Yunli, Tolba Amr, Zhang Xingguo
School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
School of Software, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel). 2023 Jul 29;23(15):6795. doi: 10.3390/s23156795.
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.
物联网（IoT）通信设备的快速发展带来了极大的便利。然而，与此同时，紧急情况下通信基础设施的破坏往往会导致通信中断以及信息传播方面的挑战，严重影响救援行动和受灾人员的安全。为应对这一挑战，物联网大数据分析和无人机（UAV）技术已成为解决方案的关键要素。通过分析大规模传感器数据、用户行为和通信流量，物联网大数据分析可以提供实时通信需求预测和网络优化策略，为灾后通信重建提供决策支持。鉴于灾后场景的独特特征，本文提出了一种基于物联网大数据分析的无人机辅助通信覆盖策略。该策略采用无人机巡航方式，通过将目标区域划分为多个小区来辅助通信，每个小区满足用户通信的最小数据需求。根据用户的分布特征，选择性地采用飞行通信或悬停通信协议来支持通信。通过优化无人机的飞行速度，并考虑任务目标区域的覆盖指数、公平指数和平均能量效率，提出了内螺旋巡航通信覆盖（IS-CCC）算法来规划无人机的巡航轨迹，实现基于无人机的通信覆盖。仿真结果表明，该策略能够在用户分布复杂的区域实现节能巡航通信覆盖，从而降低基于无人机通信的能耗。
