Internet of Drones: Routing Algorithms, Techniques and Challenges

• Published in: Mathematics (Basel) Vol. 10; no. 9; p. 1488
• Main Authors: Haider, Syed Kamran, Nauman, Ali, Jamshed, Muhammad Ali, Jiang, Aimin, Batool, Sahar, Kim, Sung Won
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.05.2022
• Subjects: Algorithms; Clustering; Data collection; Drone aircraft; Drone vehicles; Drones; Energy; energy efficient; Genetic algorithms; Global positioning systems; GPS; Ground stations; Internet of Drones; Internet of Things; Mathematics; Military deployment; Nodes; Optimization; routing; Routing (telecommunications); Sensors; Traffic congestion; unmanned aerial vehicle; Unmanned aerial vehicles; Wireless communications; Wireless networks; wireless sensor network; Wireless sensor networks
• ISSN: 2227-7390; 2227-7390
• DOI: 10.3390/math10091488

In the past decades, unmanned aerial vehicles (UAVs), also known as drones, have drawn more attention in the academic domain and exploration in the research fields of wireless sensor networks (WSNs). Moreover, applications of drones aid operations related to military support, agriculture industry, and smart Internet-of-Things (IoT). Currently, the use of drone based IoT, also known as Internet-of-Drones (IoD), and their design challenges and techniques are being probed by researchers around the globe. The placement of drones (nodes) is an important consideration in a IoD environment and is closely related to the properties of IoT. Given a base station (BS), sensor nodes (SNs) and IoT devices are designed to capture the signals transmitted by the BS and make use of internet connectivity in a manner to facilitate users. Mutual benefit can be achieved by integrating drones into IoT. The drone based cluster models are not free from challenges. Routing protocols have to be substantiated by key algorithms. Drones are designed to be specific to applications, but the underlying principles are the same. Optimization algorithms are the gateway to better accuracy, performance, and reliability. This article discusses some of these optimization algorithms, include genetic algorithm (GA), bee optimization algorithm, and Chicken Swarm Optimization Clustering Algorithm (CSOCA). Finally, the routing schemes, protocols, and challenges in the context of IoD are discussed.