UAV-CLOUD: A PLATFORM FOR UAV RESOURCES AND SERVICES ON THE CLOUD

UAVs - Unmanned Aerial Vehicles – have gained significant attention recently, due to the increasingly growing range of applications. However, developing collaborative UAV applications using traditional technologies in a tightly coupled design requires a great deal of development effort, time, and budget especially for heterogeneous UAVs. Moreover, monitoring and accessing UAV resources using traditional communication media suffer from several restrictions and limitations. This research aims to simplify the efforts, reduce the time, and lower the costs of developing collaborative applications for distributed heterogeneous UAVs. In addition, the research aims to provide ubiquitous UAV resources access. A platform is proposed for developing distributed UAVs. This platform provides services to simplify application development. In this approach, UAVs are integrated with the Cloud Computing paradigm to provide ubiquitous access to their resources and services. Due to the limited capabilities of UAVs, a lightweight architecture is adopted. UAV resources and services are modeled in a Resource Oriented Architecture which is a new flexible web service design pattern with loosely coupled interaction between services. Hence, they are accessed as Representational State Transfer RESTful services using HTTP. Moreover, the research proposes using a broker architecture to increase efficiency by separating responsibilities. Therefore, it separates the requester’s logic and functionalities from the provider’s. It also takes the responsibility for allocating the issued request to the available and suitable UAV(s). To test the proposed platform, I first developed the UAV resources as a payload subsystem then provided them with Internet connectivity. Then, resource identifiers and uniform interfaces were developed using the RESTful Application Programming Interfaces (APIs). I also developed the broker service along with a database containing the information of the registered UAVs and their resources. The platform system components were tested using a requester interface in a browser by issuing a request for a resource to the broker to find and request the service from a suitable UAV. The test was done for retrieving data from UAVs as well as requesting actions from them. The main contributions of this research are proposing the UAV-Cloud platform for simplifying the development of ubiquitous UAV applications and its vii perspectives, as well as a lightweight loosely coupled design for UAV resources. Another contribution is developing the broker architecture for separating responsibilities in this platform

Modular event-driven unmanned aerial vehicles control platform

Hoje em dia, os drones estão-se a tornar cada vez mais comuns nas nossas vidas diárias. Com a agilidade, acessibilidade e diversidade dos drones, eles são uma excelente plataforma para transportar dispositivos (p.ex., conjunto de sensores, câmeras, unidades computacionais de pequena dimensão). Assim sendo, são uma excelente ferramenta para tarefas como: explorar e estudar áreas perigosas, monitorizar campos de agricultura, ajudar na detecção e combate de incêndios ou vigiar multidões. Para realizar tais tarefas, ferramentas de automação e integração são essenciais, para que o desenvolvimento se concentre na própria aplicação e não nos problemas relacionados com a integração e automação do sistema do drone. Os drones atualmente disponiveis não são capazes de lidar com tais complexidades de forma tão transparente. Por exemplo, certos niveis de automação são ja possiveis, mas requerem hardware e software especificos do fornecedor; no que toca a integração, alguns já supportam SDK ou API para interagir com o drone, mas mais uma vez com a inconveniência de necessitar de conhecimento prévio sobre os sistemas dos drones. Para responder a estas necessidades, esta tese propõe uma plataforma modular de controlo baseada em eventos para abstrair os processos de automação e integração da complexidade subjacentes aos drones. Enquanto que a plataforma permite que as aplicações controlem e interajam com os drones, a sua complexidade é resolvida dentro da plataforma, simplificando o processo de integração. Além disso, com a plataforma proposta, a automação e funcionalidades do drone podem ser estendidas para estender as funcionalidades de drones mais limitados. A plataforma desenvolvida foi testada em diferentes cenários, tanto ao nível das suas funcionalidades como ao nível da analise de desempenho. Os resultados mostram que, além das funcionalidades suportadas, a plataforma consegue suportar o controlo e gestão de pelo menos até 64 drones em simultâneo sem ter modificações significativas nos atrasos de comunicação e throughput.Nowadays, drones are becoming more common in our daily lives. Since drones are agile, a ordable and diverse, they make an excellent platform to carry devices around (e.g., sensor arrays, cameras, small computers). With these capabilities, they become an excellent tool for tasks like: explore and study hazardous areas, agriculture monitoring, help on the detection and ght in res, and crowd surveillance. To perform such tasks, automation and integration tools are a must have, so that the development can focus on the application itself and not on the issues related with the integration and automation of the drone system. Current available drones are not capable of properly handling such complexities in a seamless way. For instance, some levels of automation are already possible, but require vendor speci c hardware and software; for integration, some o er SDK or API interactions, but once again with the inconvenience of requiring extensive knowledge about drone systems to implement. To address these issues, this thesis proposes a modular event-driven control platform to abstract automation and integration processes from the underlying complexities of the drones, while the platform lets the applications control and interact with the drones. The drones' complexities are resolved within the platform, therefore simplifying integration process. Moreover, with the proposed platform, drone automation and functionality can be extended across distinct brands of drones, while some may already support some features, others may not, and in that case the platform modules may intervene to extend the features of less capable drones. The developed platform has been tested in di erent scenarios, such as in terms of its functionalities and in terms of performance analysis. The results show that, besides the supported functionalities, the platform is able to handle the control and management of at last 64 simultaneous drones without signi cant changes in the communication delays and throughput.Mestrado em Engenharia Informátic

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

A Fog Computing Framework for Intrusion Detection of Energy-Based Attacks on UAV-Assisted Smart Farming

Precision agriculture and smart farming have received significant attention due to the advancements made in remote sensing technology to support agricultural efficiency. In large-scale agriculture, the role of unmanned aerial vehicles (UAVs) has increased in remote monitoring and collecting farm data at regular intervals. However, due to an open environment, UAVs can be hacked to malfunction and report false data. Due to limited battery life and flight times requiring frequent recharging, a compromised UAV wastes precious energy when performing unnecessary functions. Furthermore, it impacts other UAVs competing for charging times at the station, thus disrupting the entire data collection mechanism. In this paper, a fog computing-based smart farming framework is proposed that utilizes UAVs to gather data from IoT sensors deployed in farms and offloads it at fog sites deployed at the network edge. The framework adopts the concept of a charging token, where upon completing a trip, UAVs receive tokens from the fog node. These tokens can later be redeemed to charge the UAVs for their subsequent trips. An intrusion detection system is deployed at the fog nodes that utilize machine learning models to classify UAV behavior as malicious or benign. In the case of malicious classification, the fog node reduces the tokens, resulting in the UAV not being able to charge fully for the duration of the trip. Thus, such UAVs are automatically eliminated from the UAV pool. The results show a 99.7% accuracy in detecting intrusions. Moreover, due to token-based elimination, the system is able to conserve energy. The evaluation of CPU and memory usage benchmarks indicates that the system is capable of efficiently collecting smart-farm data, even in the presence of attacks

Towards a cloud‑based automated surveillance system using wireless technologies

Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130

SNAP : A Software-Defined & Named-Data Oriented Publish-Subscribe Framework for Emerging Wireless Application Systems

The evolution of Cyber-Physical Systems (CPSs) has given rise to an emergent class of CPSs defined by ad-hoc wireless connectivity, mobility, and resource constraints in computation, memory, communications, and battery power. These systems are expected to fulfill essential roles in critical infrastructure sectors. Vehicular Ad-Hoc Network (VANET) and a swarm of Unmanned Aerial Vehicles (UAV swarm) are examples of such systems. The significant utility of these systems, coupled with their economic viability, is a crucial indicator of their anticipated growth in the future. Typically, the tasks assigned to these systems have strict Quality-of-Service (QoS) requirements and require sensing, perception, and analysis of a substantial amount of data. To fulfill these QoS requirements, the system requires network connectivity, data dissemination, and data analysis methods that can operate well within a system\u27s limitations. Traditional Internet protocols and methods for network connectivity and data dissemination are typically designed for well-engineering cyber systems and do not comprehensively support this new breed of emerging systems. The imminent growth of these CPSs presents an opportunity to develop broadly applicable methods that can meet the stated system requirements for a diverse range of systems and integrate these systems with the Internet. These methods could potentially be standardized to achieve interoperability among various systems of the future. This work presents a solution that can fulfill the communication and data dissemination requirements of a broad class of emergent CPSs. The two main contributions of this work are the Application System (APPSYS) system abstraction, and a complementary communications framework called the Software-Defined NAmed-data enabled Publish-Subscribe (SNAP) communication framework. An APPSYS is a new breed of Internet application representing the mobile and resource-constrained CPSs supporting data-intensive and QoS-sensitive safety-critical tasks, referred to as the APPSYS\u27s mission. The functioning of the APPSYS is closely aligned with the needs of the mission. The standard APPSYS architecture is distributed and partitions the system into multiple clusters where each cluster is a hierarchical sub-network. The SNAP communication framework within the APPSYS utilized principles of Information-Centric Networking (ICN) through the publish-subscribe communication paradigm. It further extends the role of brokers within the publish-subscribe paradigm to create a distributed software-defined control plane. The SNAP framework leverages the APPSYS design characteristics to provide flexible and robust communication and dynamic and distributed control-plane decision-making that successfully allows the APPSYS to meet the communication requirements of data-oriented and QoS-sensitive missions. In this work, we present the design, implementation, and performance evaluation of an APPSYS through an exemplar UAV swarm APPSYS. We evaluate the benefits offered by the APPSYS design and the SNAP communication framework in meeting the dynamically changed requirements of a data-intensive and QoS-sensitive Coordinated Search and Tracking (CSAT) mission operating in a UAV swarm APPSYS on the battlefield. Results from the performance evaluation demonstrate that the UAV swarm APPSYS successfully monitors and mitigates network impairment impacting a mission\u27s QoS to support the mission\u27s QoS requirements

Ffau—framework for fully autonomous uavs

Nr. 024539 (POCI-01-0247-FEDER-024539) under grant agreement No 783221 UID/EEA/00066/2019Unmanned Aerial Vehicles (UAVs), although hardly a new technology, have recently gained a prominent role in many industries being widely used not only among enthusiastic consumers, but also in high demanding professional situations, and will have a massive societal impact over the coming years. However, the operation of UAVs is fraught with serious safety risks, such as collisions with dynamic obstacles (birds, other UAVs, or randomly thrown objects). These collision scenarios are complex to analyze in real-time, sometimes being computationally impossible to solve with existing State of the Art (SoA) algorithms, making the use of UAVs an operational hazard and therefore significantly reducing their commercial applicability in urban environments. In this work, a conceptual framework for both stand-alone and swarm (networked) UAVs is introduced, with a focus on the architectural requirements of the collision avoidance subsystem to achieve acceptable levels of safety and reliability. The SoA principles for collision avoidance against stationary objects are reviewed and a novel approach is described, using deep learning techniques to solve the computational intensive problem of real-time collision avoidance with dynamic objects. The proposed framework includes a web-interface allowing the full control of UAVs as remote clients with a supervisor cloud-based platform. The feasibility of the proposed approach was demonstrated through experimental tests using a UAV, developed from scratch using the proposed framework. Test flight results are presented for an autonomous UAV monitored from multiple countries across the world.publishersversionpublishe