Internet of Drones: Routing Algorithms, Techniques and Challenges

Abstract

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.

Water Ecotourism Route Recommendation Model Based on an Improved Cockroach Optimization Algorithm

“…Under the condition of the fixed urban traffic system, tourists are encouraged to freely choose transportation modes. How to plan the optimal route based on the transportation modes selected by tourists on the premise of meeting their interests, minimizing exhaust emissions, and decreasing the pollution of urban ecology and water resources is the key issue of ecotourism route planning [20][21][22][23].…”

Agent swarms: cooperation and coordination under stringent communications constraint

“…For more than a decade, the availability and range of applications for Unmanned Aerial Vehicles (UAVs) or "drones" has greatly increased, and now span areas such as logistics, agriculture, remote sensing, communication, security, and defense [1,2]. In particular, UAV's appear to be useful for tasks which are too dangerous, expensive, or innaccessible by manned vehicles, and also offer the advantage of being able to use a swarm of smaller and less expensive drones, possibly with differing capabilities, in place of a single UAV.…”

“…Swarm robotics and swarm engineering are emerging fields [3] which study automated decision making and control for large groups of robots using only local communication between nearby swarm mem-bers, although much work in that field considers larger swarms (≈ 10 2 − 10 5 ) than of interest for flying drone swarms (≈ 10 − 10 2 ). There are special challenges inherent in the creation and real-time maintenance of non-centralised ad hoc networks for groups of UAVs, variously termed FANETs (Flying Ad Hoc Networks) in [2] and UAANETs (UAV Ad Hoc Networks) in [4], which have been studied in recent years and various architectures and protocols have been proposed [1,2,4]. A number of groups have developed in silico test-beds for FANETs [5] with some moving on to in robotico realisations and there has been recent interest in local algorithms for adaptive FANETs as well as consensus [6].…”

“…In this work we focus on scenarios in which the drones (hereafter agents) must act in the extreme limit of minimal information sharing. This is most easily represented as situations where communications must be restricted in order to maintain stealth, since each transmission might be detected by some adversarial listener 1 . This is the opposite of typical scenarios, where information sharing and other communications are considered as "free", so that each agent almost automatically has an excellent knowledge as to the state of the swarm, or at least some coordinating agent or supervisor has such in-formation with which to efficiently direct swarm operations.…”

“…What this means in practice is that many of the typical treatments of swarm activities as given above (e.g. [1,2]) become secondary problems to the fundamental issue [10]: i.e. how well does each agent know where the others are, so that it might in principle send a message, and what tactics should it use to maximise the accuracy of its beliefs, whilst minimising its exposure to adversarial action?…”