Selection of Appropriate UAV-Integrated with Mobile Edge Computing for Ensuring Safety and Security of Smart Cities

Abstract

Recently, unmanned aerial vehicles (UAVs), commonly known as drones, have gained popularity due to their unique capabilities. Mobile edge computing (MEC) has emerged as a key platform for many smart systems, primarily because it offers lower latency compared to centralized cloud computing. As a result, the integration of UAVs with MEC has become essential and is increasingly utilized across various sectors, particularly in enhancing the safety and security of smart cities. A robust safety and security system, along with effective crowd control, are critical concerns for smart cities. The integration of Unmanned Aerial Vehicles (UAVs) with MEC significantly improves the safety and security of these urban environments. This research focuses on selecting the most suitable UAVs integrated with mobile edge computing to enhance the protection and safety of smart cities. Given the numerous options available, we employed the multi-criteria decision-making (MCDM) method to facilitate the selection of the appropriate UAV. A detailed explanation of the Interval-Valued Neutrosophic MEREC-EDAS method is given, highlighting the use of neutrosophic data to effectively represent uncertain and ambiguous information in decision-making. To illustrate the application of the Interval-Valued Neutrosophic MEREC-EDAS method, a case study assessing the optimal UAV for safety and security in smart cities is presented. The case study illustrates the viability and efficacy of the suggested assessment procedure. Furthermore, we carried out weighted and sensitivity analyses of the alternatives. For comparisons, we also utilized the OWCM, ENTROPY, WENSLO, CRITIC, RAM, MARICA and ARAS methods. We believe this study will assist local authorities in taking corrective action to foster community growth.