The Internet of Drones (IoD) has become a hot research topic in academia, industry, and management in current years due to its wide potential applications, such as aerial photography, civilian, and military. This paper presents a comprehensive survey of IoD and its applications, deployments, and integration. We focused in this review on two main sides; IoD Applications include smart cities surveillance, cloud and fog frameworks, unmanned aerial vehicles, wireless sensor networks, networks, mobile computing, and business paradigms; integration of IoD includes privacy protection, security authentication, neural network, blockchain, and optimization based-method. A discussion highlights the hot research topics and problems to help researchers interested in this area in their future works. The keywords that have been used in this paper are Internet of Drones.

One of the most crucial aspects of image segmentation is multilevel thresholding. However, multilevel thresholding becomes increasingly more computationally complex as the number of thresholds grows. In order to address this defect, this paper proposes a new multilevel thresholding approach based on the Evolutionary Arithmetic Optimization Algorithm (AOA). The arithmetic operators in science were the inspiration for AOA. DAOA is the proposed approach, which employs the Differential Evolution technique to enhance the AOA local research. The proposed algorithm is applied to the multilevel thresholding problem, using Kapur’s measure between class variance functions. The suggested DAOA is used to evaluate images, using eight standard test images from two different groups: nature and CT COVID-19 images. Peak signal-to-noise ratio (PSNR) and structural similarity index test (SSIM) are standard evaluation measures used to determine the accuracy of segmented images. The proposed DAOA method’s efficiency is evaluated and compared to other multilevel thresholding methods. The findings are presented with a number of different threshold values (i.e., 2, 3, 4, 5, and 6). According to the experimental results, the proposed DAOA process is better and produces higher-quality solutions than other comparative approaches. Moreover, it achieved better-segmented images, PSNR, and SSIM values. In addition, the proposed DAOA is ranked the first method in all test cases.

Space exploration refers to constructing a map with the aid of sensor data. This exploration is achieved utilizing a group of robots in an obstacle cluttered environment and distributing tasks amongst these robot(s). The robotic configuration is equipped with sensors to acquire data from the surroundings and to ensure collision-free motion. This paper presents a framework for the design of a Hybrid Stochastic Optimizer (HSO) for multi-robot space exploration. The proposed algorithm augments deterministic Coordinated Multi-Robot Exploration (CME) and stochastic Arithmetic Optimization (AO) techniques for maximizing the utility. The framework initially utilizes deterministic CME to ascertain the cost and utility values of adjacent cells around robot(s). The overall solution accuracy is then improved utilizing the Arithmetic Optimization algorithm. The proposed utilization of hybrid is interpreted that the algorithm starts with deterministic technique and continues off with stochastic method until the required improved solution with the desired accuracy is achieved. The effectiveness of the proposed Hybrid Stochastic Optimizer is ascertained by training the multi-robotic framework in various complexity maps. The results efficacy is then demonstrated by comparing the results of the HSO algorithm with those achieved from two contemporary techniques namely conventional CME and hybrid CME with whale optimizer. Results demonstrate that the proposed HSO algorithm significantly improved the exploration parameters by enhancing the explored area and reducing the search time.