Multi-Drone Edge Intelligence and SAR Smart Wearable Devices for Emergency Communication

Author:

Alsamhi Saeed Hamood1ORCID,Almalki Faris A.2ORCID,AL-Dois Hatem3ORCID,Shvetsov Alexey V.45ORCID,Ansari Mohammad Samar6ORCID,Hawbani Ammar7ORCID,Gupta Sachin Kumar8ORCID,Lee Brian1ORCID

Affiliation:

1. Athlone Institute of Technology, Athlone, Ireland

2. Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia

3. Department of Electrical Engineering, IBB University, Ibb, Yemen

4. North-Eastern Federal University, Yakutsk, Russia

5. Vladivostok State University of Economics and Service, Vladivostok, Russia

6. Aligarh Muslim University, India

7. University of Science and Technology of China, Hefei, China

8. School of Electronics and Communication Engineering, Shri Mata Vaishno Devi University, Katra, India

Abstract

Disasters, either manmade or natural, call for rapid and timely actions. Due to disaster, all of the communication infrastructures are destroyed, and there is no way for connection between people in disaster and others outside the disaster range. Drone technology is the critical technology for delivering communication services and guiding people and monitoring the unwanted effects of a disaster. The collaboration of advanced technologies can reduce life losses, save people’s lives, and manage the disaster crisis. The network performance of collaboration between the Internet of Things (IoT) and drone edge intelligence can help gather and process data, extend the wireless coverage area, deliver medical emergencies, provide real-time information about the emergency, and gather data from areas that are impossible for humans to reach. In this paper, we focus on the network performance for efficient collaboration of drone edge intelligence and smart wearable devices for disaster management. We focus mainly on network connectivity parameters for improving real-time data sharing between the drone edge intelligence and smart wearable devices. The relevant parameters that are considered in this study include delay, throughput, and the load from drone edge intelligence. It is further shown that network performance can have significant improvement when the abovementioned parameters are correctly optimised, and the improved performance can significantly improve the guiding/coordinating of search and rescue (SAR) teams effectively and efficiently.

Funder

Taif University

Publisher

Hindawi Limited

Subject

Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems

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