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

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.Comment: Accepted for publication in IEEE Communications Surveys and Tutorial

Advancements in Forest Fire Prevention: A Comprehensive Survey

Nowadays, the challenges related to technological and environmental development are becoming increasingly complex. Among the environmentally significant issues, wildfires pose a serious threat to the global ecosystem. The damages inflicted upon forests are manifold, leading not only to the destruction of terrestrial ecosystems but also to climate changes. Consequently, reducing their impact on both people and nature requires the adoption of effective approaches for prevention, early warning, and well-coordinated interventions. This document presents an analysis of the evolution of various technologies used in the detection, monitoring, and prevention of forest fires from past years to the present. It highlights the strengths, limitations, and future developments in this field. Forest fires have emerged as a critical environmental concern due to their devastating effects on ecosystems and the potential repercussions on the climate. Understanding the evolution of technology in addressing this issue is essential to formulate more effective strategies for mitigating and preventing wildfires

A framework for autonomous mission and guidance control of unmanned aerial vehicles based on computer vision techniques

A computação visual é uma área do conhecimento que estuda o desenvolvimento de sistemas artificiais capazes de detectar e desenvolver a percepção do meio ambiente através de informações de imagem ou dados multidimensionais. A percepção visual e a manipulação são combinadas em sistemas robóticos através de duas etapas "olhar"e depois "movimentar-se", gerando um laço de controle de feedback visual. Neste contexto, existe um interesse crescimente no uso dessas técnicas em veículos aéreos não tripulados (VANTs), também conhecidos como drones. Essas técnicas são aplicadas para posicionar o drone em modo de vôo autônomo, ou para realizar a detecção de regiões para vigilância aérea ou pontos de interesse. Os sistemas de computação visual geralmente tomam três passos em sua operação, que são: aquisição de dados em forma numérica, processamento de dados e análise de dados. A etapa de aquisição de dados é geralmente realizada por câmeras e sensores de proximidade. Após a aquisição de dados, o computador embarcado realiza o processamento de dados executando algoritmos com técnicas de medição (variáveis, índice e coeficientes), detecção (padrões, objetos ou áreas) ou monitoramento (pessoas, veículos ou animais). Os dados processados são analisados e convertidos em comandos de decisão para o controle para o sistema robótico autônomo Visando realizar a integração dos sistemas de computação visual com as diferentes plataformas de VANTs, este trabalho propõe o desenvolvimento de um framework para controle de missão e guiamento de VANTs baseado em visão computacional. O framework é responsável por gerenciar, codificar, decodificar e interpretar comandos trocados entre as controladoras de voo e os algoritmos de computação visual. Como estudo de caso, foram desenvolvidos dois algoritmos destinados à aplicação em agricultura de precisão. O primeiro algoritmo realiza o cálculo de um coeficiente de reflectância visando a aplicação auto-regulada e eficiente de agroquímicos, e o segundo realiza a identificação das linhas de plantas para realizar o guiamento dos VANTs sobre a plantação. O desempenho do framework e dos algoritmos propostos foi avaliado e comparado com o estado da arte, obtendo resultados satisfatórios na implementação no hardware embarcado.Cumputer Vision is an area of knowledge that studies the development of artificial systems capable of detecting and developing the perception of the environment through image information or multidimensional data. Nowadays, vision systems are widely integrated into robotic systems. Visual perception and manipulation are combined in two steps "look" and then "move", generating a visual feedback control loop. In this context, there is a growing interest in using computer vision techniques in unmanned aerial vehicles (UAVs), also known as drones. These techniques are applied to position the drone in autonomous flight mode, or to perform the detection of regions for aerial surveillance or points of interest. Computer vision systems generally take three steps to the operation, which are: data acquisition in numerical form, data processing and data analysis. The data acquisition step is usually performed by cameras or proximity sensors. After data acquisition, the embedded computer performs data processing by performing algorithms with measurement techniques (variables, index and coefficients), detection (patterns, objects or area) or monitoring (people, vehicles or animals). The resulting processed data is analyzed and then converted into decision commands that serve as control inputs for the autonomous robotic system In order to integrate the visual computing systems with the different UAVs platforms, this work proposes the development of a framework for mission control and guidance of UAVs based on computer vision. The framework is responsible for managing, encoding, decoding, and interpreting commands exchanged between flight controllers and visual computing algorithms. As a case study, two algorithms were developed to provide autonomy to UAVs intended for application in precision agriculture. The first algorithm performs the calculation of a reflectance coefficient used to perform the punctual, self-regulated and efficient application of agrochemicals. The second algorithm performs the identification of crop lines to perform the guidance of the UAVs on the plantation. The performance of the proposed framework and proposed algorithms was evaluated and compared with the state of the art, obtaining satisfactory results in the implementation of embedded hardware

Preparing for Future Forest Fires: Emerging Technologies and Innovations

Forest fires are part of the global ecosystems occurring for a long time in earth history.  These forest fires are part of the processes which establish the ecosystems and directly influence plant species composition within the ecosystems. However, the anthropogenic effect has changed this relationship causing an increasing number of forest fires Human activities have also changed world climate and future climate is expected to increase in temperature with dire consequences on the earth environment. These changes will profoundly impact on the earth’s socio-economic and human well-being. One of the effects of higher global temperature is increasing forest fires occurrences with stronger intensities.  There is a need to develop innovation and new technologies to manage these future fires. This paper aims to review various innovations and new technologies that can be used for the whole spectrum of forest fire management, from forest fire prediction to forest restoration of burnt areas. Emerging technologies such as geospatial technologies, the Internet of Things (IoT), Artificial Intelligence, 5G & enhanced connectivity, the Internet of Behaviors (IoB), virtual and augmented reality, and robotics are discussed and potential applications to forest fire management are discussed. Adaptation of these technologies is vital in the effective management of future forest fires. Key words: Climate Change, Future Fires, InnovationsKebakaran hutan merupakan bagian dari ekosistem global yang terjadi sejak lama dalam sejarah bumi. Kebakaran hutan ini merupakan bagian dari proses yang membentuk ekosistem dan secara langsung mempengaruhi komposisi spesies tumbuhan di dalam ekosistem. Namun, efek antropogenik telah mengubah hubungan ini yang menyebabkan peningkatan jumlah kebakaran hutan Aktivitas manusia juga telah mengubah iklim dunia dan iklim di masa depan diperkirakan akan meningkatkan suhu dengan konsekuensi yang mengerikan pada lingkungan bumi. Perubahan ini akan sangat berdampak pada sosial ekonomi bumi dan kesejahteraan manusia. Salah satu dampak dari peningkatan suhu global adalah meningkatnya kejadian kebakaran hutan dengan intensitas yang lebih kuat. Ada kebutuhan untuk mengembangkan inovasi dan teknologi baru untuk mengelola kebakaran di masa depan ini. Tulisan ini bertujuan untuk mengkaji berbagai inovasi dan teknologi baru yang dapat digunakan untuk seluruh spektrum penanggulangan kebakaran hutan, mulai dari prediksi kebakaran hutan hingga restorasi hutan pada kawasan yang terbakar. Teknologi yang muncul seperti teknologi geospasial, Internet of Things (IoT), Artificial Intelligence, 5G & konektivitas yang ditingkatkan, Internet of Behaviors (IoB), virtual dan augmented reality, dan robotika dibahas dan aplikasi potensial untuk manajemen kebakaran hutan dibahas. Adaptasi teknologi ini sangat penting dalam pengelolaan kebakaran hutan yang efektif di masa depan. Kata kunci: Perubahan Iklim, Kebakaran di Masa Depan, Inovas