The Internet of Drones (IoD) is expected to revolutionize the utilization of drones in urban environments. Drone delivery applications are expected to become commonplace in people's everyday lives. Such applications will establish a network where drones will have organized airspace access, with variations in network node density based on location and time. Within this environment, the IoD network may encounter communication gaps among drones. In scenarios like drone delivery, altering a drone's route solely for message delivery within the network is impractical. We aim to deploy an auxiliary drone network to support IoD applications, similar to using auxiliary networks in vehicular networks for message delivery. This study introduces the AuxIoD method, which utilizes a genetic algorithm to position auxiliary drones strategically, bridging communication gaps within the IoD network. Compared to other strategies, our approach facilitated the delivery of approximately 30% more messages in the evaluated scenario.

Unmanned aerial vehicles (UAVs)–also known as drones or Unmanned Aircraft–have found diverse applications in various fields owing to their significant advantages, including fast mobility and rapid deployment. UAVs are crucial in aerial networks, providing increased coverage and on-demand connectivity as mobile nodes. In recent years, UAVs have made room to leverage the Internet of Things (IoT) to the sky, enhancing air-to-ground communication and pointing toward the next generation of UAV networks. As this expansion is still in its early stages, there are several aerial network terminologies, each with similarities and differences, depending on the deployment domain and the services they offer. However, studies have yet to discuss these different terminologies consistently. Key aspects have yet to be thoroughly explored, such as the anticipated requirements for deploying these networks and how they relate to the various terminologies. This work systematically analyzes the existing terminologies of UAV networks, considering their requirements and applications, shedding light on their intersections and differences. Furthermore, we present the demands for the next generation of UAV networks and discuss how they impact the design of UAV-related applications, aiding in the design of new protocols, tools, and technologies for both industry and academia. Lastly, we highlight the emerging trends and challenges associated with deploying and integrating these networks.

Shortly, the airspace will have different types of aerial networks due to the continuous growth of demands for aerial applications. As these networks compete for airspace, it is necessary to manage them efficiently. Also, these networks are expected to communicate with each other and with terrestrial networks forming hybrid aerial-terrestrial networks (HATNs). Efficient protocols for data packet delivery in HATNs are crucial for these networks to work together. In addition, cooperation between networks can be essential for different emergencies. However, communication in HATNs is challenging because of the plurality of node types. This work presents an altitude-based routing protocol for Hybrid Aerial-terrestrial Networks (ARAT). ARAT is a geocast and store-carry-forward protocol with promising performance in sparse scenarios. Simulation results show that ARAT outperforms two protocols (a baseline protocol and the GeoUAV protocol) by 50% for the packet delivery ratio metric. Also, to the best of our knowledge, ARAT is the first protocol to consider a collaboration between Aerial Networks and Internet of Drones (IoD) with the traffic of drones constantly.

Recently applications that use a single drone are evolved into an Internet of drones (IoD) ecosystem. The IoD has a complex control as it needs to coordinate the drones in the airspace, which involves mobility and communication between them. It facilitates the IoD control if drones frequently report their positions to the control base, use well-defined airways to fly, and are managed through Software-Defined Internet of Drones (SDIoD). In the shared airspace, drones are expected to exchange messages with each other. In this work, we propose PSDIoD, a Position-based routing protocol for SDIoD, which has two versions. The first, called PSDIoD-SP (Shortest Path), considers the shortest path in the airways to select the routes. The second, called PSDIoD-EA (Energy-Aware), examines the radio power consumption estimated to choose the route. Due to nodes having a limited battery, power is a crucial factor in IoD. Both of which consider the knowledge of the position of drones to route packets. This work advances the state of the art by proposing efficient routing solutions that were developed in a suitable way for the IoD environment. The results show that PSDIoD versions are better in power consumption, packet delivery ratio, delay, and overhead when compared to a baseline protocol. Furthermore, PSDIoD-EA is approximately 9% more economical in terms of power consumption when compared to PSDIoD-SP.

Internet of Drones (IoD) is an architecture that aims to enable different drones to share the same airspace. This architecture can help coordinate drone access to airspace in urban environments. Considering that the IoD is a dynamic network, it is possible to have scenarios in which drone traffic is sparse when, for instance, the network has isolated drones. In this case, the drones’ communication range does not reach any other drone. Thus, store-carry-forward protocols may be suitable for maintaining network communication. Moreover, different networks can collaborate to fill these communication gaps. In this study, we explore the collaboration between IoD and Bus Networks. Our analysis shows that maintaining a hybrid communication between drones and buses can fill the gaps in the communication between drones. The main goal of this work is to present the IoDSCF – a store-carry-forward routing protocol for joint Bus Networks and the Internet of Drones (IoD). IoDSCF takes advantage of both networks to extend the communication reachability. Our results reveal that IoDSCF presents better results in the number of delivered packets and end-to-end delay than a solution based only on communication between drones. This is a promising strategy for data communication, mainly in smart cities.

In the last few years, new scenarios have emerged for drone applications including those of risk scenarios. In this case, it is essential to have communication among drones whenever they perform tasks that need some sort of coordination, management or synchronization, for instance. In this work, we propose IoDAGR – Internet of Drones Airway-based Geocast Routing, a geocast protocol to transmit messages, such as alerts, to a specific geographical region in a drone network. IoDAGR advances the state of the art of geocast protocols for drones by proposing an efficient solution to this scenario. Simulation results revealed that IoDAGR presents promising results when compared to a baseline protocol.

The Internet of Drones (IoD) refers to a robust mobile network with well-defined airways where drones perform interoperable services, enhancing the deployment of Smart Cities. Automatic Drone Identification (ADI) is a protection mechanism to detect and avoid malicious drones, where different techniques have been used, such as acoustic signals. In this field, the sound generated by propellers and motors has particular characteristics, being a potential aspect to explore; however, this investigation is still missing. This study examines the use of rhythm-based descriptors as input features to ADI, based on the hypothesis that the acoustic signal generated by different drones has different rhythmic properties. Aiming to explore and validate our approach, we formulate an ADI methodology using rhythm-based features. We use a freely available drone audio dataset, comparing our results with a baseline study. As a result, our classification model improves 3.47% the baseline binary classification and 2.97% the multiclass classification, reaching accuracy rates of 0.9985 and 0.9591, respectively. Although the improvements are narrow, they point out that acoustic features have a great potential to enhance ADI mainly in dense drone-based environments, where drone identification is an essential task.

This paper proposes the MannAccess, a novel low cost assistive educational tool of digital image for Visually Impaired (VI), aiding the teaching-learning process of visual content. It consists of an assistive environment composed of interactive software and a refreshable pin display with a novel 3-axis pin activation mechanism, decreasing its development cost substantially. The MannAccess allows the integration with different image recognition methods using a proposed image intermediary representation. In order to accomplish evaluations, we developed a prototype of MannAccess and integrated it with MannAR, an automata image recognition method. We carried out experiments with VI students which pointed out that our tool provided proper accessibility, usability, and user experience. In addition, we accomplished a monetary cost evaluation, indicating that MannAccess had the most accessible monetary cost compared to related devices. In a nutshell, MannAccess showed that it is possible to develop a low-cost assistive technology to aid the VI visual content education, integrating different image recognition methods in a single assistive tool.