Crowd-based positioning of UAVs as Access Points

Unmanned Aerial Vehicles (UAVs) can be a cost saving and easy to deploy solution to implement a temporary network infrastructure. They can act as access points in scenarios such as emergency situations, special events, or specific area monitoring. Two main deployment families can be found in the literature. The first one, the location-based family, is based on the fundamental assumption that the network user positions are known. We do believe that this could not suit the most general scenarios. On the other hand, the location-independent family can not be as efficient as the first one. The main idea in this paper is to introduce a new crowd-based family, based on a probabilistic knowledge of user positions. We then propose a self-deployment method built on a Coulomb's law analogy where users and UAVs act as electrical charges. Short range interactions are implemented through network sensing, while long range ones use a crowd-based approach. Some numerical results are depicted, showing the performance of this self-deploying mechanism as well as a comparison with a well-known clustering algorithm

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

Agricultural Object Detection with You Look Only Once (YOLO) Algorithm: A Bibliometric and Systematic Literature Review

Vision is a major component in several digital technologies and tools used in agriculture. The object detector, You Look Only Once (YOLO), has gained popularity in agriculture in a relatively short span due to its state-of-the-art performance. YOLO offers real-time detection with good accuracy and is implemented in various agricultural tasks, including monitoring, surveillance, sensing, automation, and robotics. The research and application of YOLO in agriculture are accelerating rapidly but are fragmented and multidisciplinary. Moreover, the performance characteristics (i.e., accuracy, speed, computation) of the object detector influence the rate of technology implementation and adoption in agriculture. Thus, the study aims to collect extensive literature to document and critically evaluate the advances and application of YOLO for agricultural object recognition. First, we conducted a bibliometric review of 257 articles to understand the scholarly landscape of YOLO in agricultural domain. Secondly, we conducted a systematic review of 30 articles to identify current knowledge, gaps, and modifications in YOLO for specific agricultural tasks. The study critically assesses and summarizes the information on YOLO's end-to-end learning approach, including data acquisition, processing, network modification, integration, and deployment. We also discussed task-specific YOLO algorithm modification and integration to meet the agricultural object or environment-specific challenges. In general, YOLO-integrated digital tools and technologies show the potential for real-time, automated monitoring, surveillance, and object handling to reduce labor, production cost, and environmental impact while maximizing resource efficiency. The study provides detailed documentation and significantly advances the existing knowledge on applying YOLO in agriculture, which can greatly benefit the scientific community

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out

Deployment and navigation of aerial drones for sensing and interacting applications

Existing research recognises the critical role played by Unmanned Aerial Vehicles (UAVs) (also referred to as drones) to numerous civilian applications. Typical drone applications include surveillance, wireless communication, agriculture, among many others. One of the biggest challenges is to determine the deployment and navigation of the drones to benefit the most for different applications. Many research questions have been raised about this topic. For example, drone-enabled wildlife monitoring has received much attention in recent years. Unfortunately, this approach results in significant disturbance to different species of wild animals. Moreover, with the capability of rapidly moving communication supply towards demand when required, the drone equipped with a base station, i.e., drone-cell, is becoming a promising solution for providing cellular networks to victims and rescue teams in disaster-affected areas. However, few studies have investigated the optimal deployments of multiple drone-cells with limited backhaul communication distances. In addition, the use of autonomous drones as flying interactors for many real-life applications has not been sufficiently discussed. With superior maneuverability, drone-enabled autonomous aerial interacting can potentially be used on shark attack prevention and animal herding. Nevertheless, previous studies of autonomous drones have not dealt with such applications in much detail. This thesis explores the solutions to all the mentioned research questions, with a particular focus on the deployment and navigation of the drones. First, we provide one of the first investigations into reducing the negative impacts of wildlife monitoring drones by navigation control. Second, we study the optimal placement of a group of drone-cells with limited backhaul communication ranges, aims to maximise the number of served users. Third, we propose a novel method named ‘drone shark shield’, which uses communicating autonomous drones to intervene and prevent shark attacks for protecting swimmers and surfers. Lastly, we introduce one of the first autonomous drone herding systems for mustering a large number of farm animals efficiently. Simulations have been conducted to verify the effectiveness of the proposed approaches. We believe that our findings in this thesis shed new light on the fundamental benefits of autonomous civilian drones

Computer vision in target pursuit using a UAV

Research in target pursuit using Unmanned Aerial Vehicle (UAV) has gained attention in recent years, this is primarily due to decrease in cost and increase in demand of small UAVs in many sectors. In computer vision, target pursuit is a complex problem as it involves the solving of many sub-problems which are typically concerned with the detection, tracking and following of the object of interest. At present, the majority of related existing methods are developed using computer simulation with the assumption of ideal environmental factors, while the remaining few practical methods are mainly developed to track and follow simple objects that contain monochromatic colours with very little texture variances. Current research in this topic is lacking of practical vision based approaches. Thus the aim of this research is to fill the gap by developing a real-time algorithm capable of following a person continuously given only a photo input. As this research considers the whole procedure as an autonomous system, therefore the drone is activated automatically upon receiving a photo of a person through Wi-Fi. This means that the whole system can be triggered by simply emailing a single photo from any device anywhere. This is done by first implementing image fetching to automatically connect to WIFI, download the image and decode it. Then, human detection is performed to extract the template from the upper body of the person, the intended target is acquired using both human detection and template matching. Finally, target pursuit is achieved by tracking the template continuously while sending the motion commands to the drone. In the target pursuit system, the detection is mainly accomplished using a proposed human detection method that is capable of detecting, extracting and segmenting the human body figure robustly from the background without prior training. This involves detecting face, head and shoulder separately, mainly using gradient maps. While the tracking is mainly accomplished using a proposed generic and non-learning template matching method, this involves combining intensity template matching with colour histogram model and employing a three-tier system for template management. A flight controller is also developed, it supports three types of controls: keyboard, mouse and text messages. Furthermore, the drone is programmed with three different modes: standby, sentry and search. To improve the detection and tracking of colour objects, this research has also proposed several colour related methods. One of them is a colour model for colour detection which consists of three colour components: hue, purity and brightness. Hue represents the colour angle, purity represents the colourfulness and brightness represents intensity. It can be represented in three different geometric shapes: sphere, hemisphere and cylinder, each of these shapes also contains two variations. Experimental results have shown that the target pursuit algorithm is capable of identifying and following the target person robustly given only a photo input. This can be evidenced by the live tracking and mapping of the intended targets with different clothing in both indoor and outdoor environments. Additionally, the various methods developed in this research could enhance the performance of practical vision based applications especially in detecting and tracking of objects

Applications

Volume 3 describes how resource-aware machine learning methods and techniques are used to successfully solve real-world problems. The book provides numerous specific application examples: in health and medicine for risk modelling, diagnosis, and treatment selection for diseases in electronics, steel production and milling for quality control during manufacturing processes in traffic, logistics for smart cities and for mobile communications

A new FANET simulator for managing drone networks and providing dynamic connectivity

In the last decade, the attention on unmanned aerial vehicles has rapidly grown, due to their ability to help in many human activities. Among their widespread benefits, one of the most important uses regards the possibility of distributing wireless connectivity to many users in a specific coverage area. In this study, we focus our attention on these new kinds of networks, called flying ad-hoc networks. As stated in the literature, they are suitable for all emergency situations where the traditional networking paradigm may have many issues or difficulties to be implemented. The use of a software simulator can give important help to the scientific community in the choice of the right UAV/drone parameters in many different situations. In particular, in this work, we focus our main attention on the new ways of area covering and human mobility behaviors with the introduction of a UAV/drone behavior model to take into account also drones energetic issues. A deep campaign of simulations was carried out to evaluate the goodness of the proposed simulator illustrating how it works