Towards the use of unmanned aerial systems for providing sustainable services in smart cities

La sostenibilidad está en el centro de muchos campos de aplicación en los que el uso de los sistemas aéreos no tripulados (SUA) es cada vez más importante (por ejemplo, la agricultura, la detección y predicción de incendios, la vigilancia ambiental, la cartografía, etc.). Sin embargo, su uso y evolución están muy condicionados por el campo de aplicación específico para el que están diseñados y, por lo tanto, no pueden ser fácilmente reutilizados entre los diferentes campos de aplicación. Desde este punto de vista, al no ser polivalentes, podemos decir que no son totalmente sostenibles. Teniendo esto en cuenta, el objetivo de este trabajo es doble: por un lado, identificar el conjunto de características que debe proporcionar un UAS para ser considerado sostenible y demostrar que no hay ningún UAS que satisfaga todas estas características; por otra parte, presentar una arquitectura abierta y sostenible de los UAS que pueda utilizarse para construir UAS a petición para proporcionar las características necesarias en cada campo de aplicación. Dado que esta arquitectura se basa principalmente en la adaptabilidad del software y el hardware, contribuye a la sostenibilidad técnica de las ciudades.Sustainability is at the heart of many application fields where the use of Unmanned Aerial Systems (UAS) is becoming more and more important (e.g., agriculture, fire detection and prediction, environmental surveillance, mapping, etc.). However, their usage and evolution are highly conditioned by the specific application field they are designed for, and thus, they cannot be easily reused among different application fields. From this point of view, being that they are not multipurpose, we can say that they are not fully sustainable. Bearing this in mind, the objective of this paper is two-fold: on the one hand, to identify the whole set of features that must be provided by a UAS to be considered sustainable and to show that there is no UAS satisfying all these features; on the other hand, to present an open and sustainable UAS architecture that may be used to build UAS on demand to provide the features needed in each application field. Since this architecture is mainly based on software and hardware adaptability, it contributes to the technical sustainability of cities.• Ministerio de Economía y Competitividad y Fondos FEDER. Proyecto TIN2015-69957-R (I+D+i) • Junta de Extremadura y Fondo Europeo de Desarrollo Regional. Ayuda GR15098 y IB16055 • Parcialmente financiado por Interreg V-A España-Portugal (POCTEP) 2014-2020 program. Proyecto 0045-4IE-4-PpeerReviewe

Accuracy Assessment of Low Cost UAV Based City Modelling for Urban Planning

This paper presents an Unmanned Aerial Vehicles (UAV) based 3D city modelling approach to be used in managing and planning urban areas. While the urban growth is rapidly increasing in many places of the world, the conventional techniques do not respond to the changing environment simultaneously. For effective planning, high-resolution remote sensing is a tool for the production of 3D digital city models. In this study, it is aimed at designing the remote sensing by UAV through urban terrain. Using all the information produced from UAV imagery, high-accurate 3D city models are obtained. The analysis of XYZ data of the derived from 3D model using UAV photogrammetry revealed similar products as the terrestrial surveys which are commonly used for the last development plans and city maps. The experimental results show the effectiveness of the UAV-based 3D city modelling. The assessed accuracy of the UAV photogrammetry proved that urban planners can use it as the main tool of data collection for boundary mapping, changes monitoring and topographical surveying instead of GPS/GNSS surveying

Supporting UAVs with Edge Computing: A Review of Opportunities and Challenges

Over the last years, Unmanned Aerial Vehicles (UAVs) have seen significant advancements in sensor capabilities and computational abilities, allowing for efficient autonomous navigation and visual tracking applications. However, the demand for computationally complex tasks has increased faster than advances in battery technology. This opens up possibilities for improvements using edge computing. In edge computing, edge servers can achieve lower latency responses compared to traditional cloud servers through strategic geographic deployments. Furthermore, these servers can maintain superior computational performance compared to UAVs, as they are not limited by battery constraints. Combining these technologies by aiding UAVs with edge servers, research finds measurable improvements in task completion speed, energy efficiency, and reliability across multiple applications and industries. This systematic literature review aims to analyze the current state of research and collect, select, and extract the key areas where UAV activities can be supported and improved through edge computing

Task Allocation among Connected Devices: Requirements, Approaches and Challenges

Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-of-the-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities’ provisioning

A survey of urban drive-by sensing: An optimization perspective

Pervasive and mobile sensing is an integral part of smart transport and smart city applications. Vehicle-based mobile sensing, or drive-by sensing (DS), is gaining popularity in both academic research and field practice. The DS paradigm has an inherent transport component, as the spatial-temporal distribution of the sensors are closely related to the mobility patterns of their hosts, which may include third-party (e.g. taxis, buses) or for-hire (e.g. unmanned aerial vehicles and dedicated vehicles) vehicles. It is therefore essential to understand, assess and optimize the sensing power of vehicle fleets under a wide range of urban sensing scenarios. To this end, this paper offers an optimization-oriented summary of recent literature by presenting a four-step discussion, namely (1) quantifying the sensing quality (objective); (2) assessing the sensing power of various fleets (strategic); (3) sensor deployment (strategic/tactical); and (4) vehicle maneuvers (tactical/operational). By compiling research findings and practical insights in this way, this review article not only highlights the optimization aspect of drive-by sensing, but also serves as a practical guide for configuring and deploying vehicle-based urban sensing systems.Comment: 24 pages, 3 figures, 4 table

A Comprehensive Review of AI-enabled Unmanned Aerial Vehicle: Trends, Vision , and Challenges

In recent years, the combination of artificial intelligence (AI) and unmanned aerial vehicles (UAVs) has brought about advancements in various areas. This comprehensive analysis explores the changing landscape of AI-powered UAVs and friendly computing in their applications. It covers emerging trends, futuristic visions, and the inherent challenges that come with this relationship. The study examines how AI plays a role in enabling navigation, detecting and tracking objects, monitoring wildlife, enhancing precision agriculture, facilitating rescue operations, conducting surveillance activities, and establishing communication among UAVs using environmentally conscious computing techniques. By delving into the interaction between AI and UAVs, this analysis highlights the potential for these technologies to revolutionise industries such as agriculture, surveillance practices, disaster management strategies, and more. While envisioning possibilities, it also takes a look at ethical considerations, safety concerns, regulatory frameworks to be established, and the responsible deployment of AI-enhanced UAV systems. By consolidating insights from research endeavours in this field, this review provides an understanding of the evolving landscape of AI-powered UAVs while setting the stage for further exploration in this transformative domain