Post-disaster Communication Coverage Optimization Assisted by Unmanned Aerial Vehicles Using Internet of Things Big Data Analysis

Fenign Lexon^*
*Correspondence: Fenign Lexon, Department of Computer Science, King Saud University, Riyadh 11437, Saudi Arabia, Email:

Introduction

In the aftermath of disasters, effective communication is critical for coordinating rescue efforts, providing aid, and ensuring the safety of affected populations. However, traditional communication infrastructure often gets damaged or becomes inaccessible during such events, hampering relief operations. To address this challenge, leveraging emerging technologies like Unmanned Aerial Vehicles (UAVs) and the Internet of Things (IoT) can significantly enhance post-disaster communication coverage. This paper explores how UAVs, equipped with IoT sensors and utilizing big data analysis, can optimize communication coverage in disaster-stricken areas [1].

Description

In disaster-affected regions, communication infrastructure may suffer extensive damage, leading to disrupted or entirely severed connectivity. This breakdown inhibits coordination among response teams, impedes the dissemination of critical information, and hampers efforts to assess the situation accurately. Traditional methods such as satellite communication or ground-based networks often prove insufficient due to their limited coverage or susceptibility to damage. Therefore, innovative approaches are needed to ensure effective communication restoration and support relief efforts swiftly. UAVs, commonly known as drones, offer unparalleled advantages in post-disaster scenarios. Their ability to swiftly navigate through affected areas, coupled with advancements in payload capabilities, makes them ideal platforms for deploying communication equipment. UAVs can carry various communication technologies, including cellular repeaters, Wi-Fi hotspots, or even satellite uplinks, enabling them to establish temporary communication networks in hard-to-reach or inaccessible locations. Furthermore, UAVs equipped with cameras and sensors can perform reconnaissance missions, gathering valuable data on the extent of damage and identifying areas with the most urgent communication needs [2,3].

The IoT paradigm involves interconnected devices equipped with sensors, capable of collecting and transmitting data over the internet. In the context of post-disaster communication, IoT sensors can be deployed via UAVs to gather real-time information about the environment, such as air quality, temperature, or structural integrity. This data provides valuable insights for prioritizing communication restoration efforts and ensuring the safety of rescue personnel and survivors. Additionally, IoT sensors can monitor the performance of communication networks deployed by UAVs, enabling proactive maintenance and optimization [4].

The massive volume of data collected by IoT sensors requires advanced analytics techniques to derive actionable insights. Big data analysis algorithms can process this data in real-time, identifying patterns, anomalies and optimizing communication coverage strategies accordingly. By leveraging machine learning algorithms, communication networks deployed by UAVs can adapt dynamically to changing conditions, ensuring optimal performance and reliability. Furthermore, big data analytics can facilitate predictive modeling, enabling proactive planning for future disasters based on historical data and trends [5].

Conclusion

In conclusion, the integration of UAVs, IoT sensors, and big data analytics offers a powerful solution for enhancing post-disaster communication coverage. By leveraging the agility of UAVs, the data-gathering capabilities of IoT sensors, and the analytical power of big data algorithms, relief organizations can establish resilient communication networks in disaster-affected areas rapidly. This approach not only improves coordination among response teams but also enhances situational awareness, enabling more effective allocation of resources and ultimately saving lives. As technology continues to evolve, further advancements in this field hold the potential to revolutionize disaster response and mitigate the impact of future catastrophes.

Acknowledgement

None.

Conflict of Interest

None.

References

1. Wang, Xiaojie, Zhaolong Ning, Song Guo and Miaowen Wen, et al. "Dynamic UAV deployment for differentiated services: A multi-agent imitation learning based approach." IEEE Trans Mob Comput 22 (2021): 2131-2146.

Google Scholar, Crossref, Indexed at

2. Ning, Zhaolong, Yuxuan Yang, Xiaojie Wang and Lei Guo, et al. "Dynamic computation offloading and server deployment for UAV-enabled multi-access edge computing." IEEE Trans Mob Comput 22 (2021): 2628-2644.

Google Scholar, Crossref, Indexed at

3. Wang, Yinlu, Ming Chen, Cunhua Pan and Kezhi Wang, et al. "Joint optimization of UAV trajectory and sensor uploading powers for UAV-assisted data collection in wireless sensor networks." IEEE Internet of Things J 9 (2021): 11214-11226.

Google Scholar, Crossref, Indexed at

4. Ning, Zhaolong, Peiran Dong, Xiangjie Kong and Feng Xia. "A cooperative partial computation offloading scheme for mobile edge computing enabled Internet of Things." IEEE Internet of Things J 6 (2018): 4804-4814.

Google Scholar, Crossref, Indexed at

5. Ning, Zhaolong, Handi Chen, Edith CH Ngai and Xiaojie Wang, et al. "Lightweight imitation learning for real-time cooperative service migration." IEEE Trans Mob Comput (2023).

Google Scholar, Crossref