Cloud Enabled Smart Firefighting Drone Using Internet of Things

Jayapandian, N.

doi:10.1109/icssit46314.2019.8987873

Cited by 24 publications

(7 citation statements)

References 21 publications

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“…Leveraging the capabilities of remote cloud data centers such as on-demand service, resource pooling, and rapid elasticity facilitates the usage of big data and artificial intelligence with the aim of providing improved performance for both networks and applications [39]. Even though cloud computing aids in the performance of those systems, it has been shown to be vital to employ edge and fog computing concepts due to the time sensitivity of some applications, which may be observed in using base stations as edge devices [40]. Even though these paradigms helped vehicular networks to gain traction by providing virtually unlimited means for computation and storage, several new problems arose, such as which processes should be moved to the cloud or edge and when.…”

Section: Cloudsmentioning

confidence: 99%

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Herich,

Vaščák

2024

Drones

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The Internet of Vehicles (IoV) and the Internet of Flying Vehicles (IoFV) are integral components of intelligent transportation systems with the potential to revolutionize the way we move people and goods. Although both the IoV and IoFV share a common goal of improving transportation efficiency, safety, and sustainability, they possess distinct characteristics and face unique challenges. To date, the existing literature has predominantly focused on specific aspects of either the IoV or IoFV, but a comprehensive review comparing and contrasting the two domains is still lacking. This review paper aims to address this gap by providing an in-depth analysis of the key differences between the IoV and IoFV systems. The review will examine the technological components, network infrastructure, communication protocols, data management, objectives, applications, challenges, and future trends associated with both domains. Additionally, this paper will explore the potential impact of technologies such as artificial intelligence, machine learning, and blockchain. Ultimately, the paper aims to contribute to a deeper understanding of the implications and potential of these technologies, both in the context of transportation systems and beyond.

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Section: Cloudsmentioning

confidence: 99%

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Herich,

Vaščák

2024

Drones

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“…Even at the current low or medium levels of autonomy, RPAS have been adopted or proposed as a solution across a number of industries, often with the result of making jobs less costly for humans, allowing humans to focus their specialized skills and energy elsewhere, or otherwise improving mission efficiency and outcomes. Among recent applications are rice seed sowing [40], parasite and pest management [41], monitoring soil moisture [42], and general crop surveillance [43] in agriculture; crime analysis [44] and highway surveillance [45] in policing; disaster relief and hunting hurricanes [46], radiological mapping and threat assessment [47], fire detection [48] and firefighting [49], [50], reforestation [51], and mountain search and rescue missions [52] in environmental risk and preservation; and assorted other tasks like 3D mapping [53], [54], product delivery, and transportation [55], surveillance [56], navigating and mapping indoor environments [57], cleaning buildings [50], aerial imaging, large-scale area surveying [58], [59].…”

Section: Benefits and Challenges Of Current Aclmentioning

confidence: 99%

Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Aibin¹,

Aldiab

Bhavsar³

et al. 2021

IEEE Access

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In this survey, we look at the overall idea of Remotely Piloted Aircraft Systems (RPAS) and autonomous control, as well as RPAS infrastructure, levels of autonomy, and current benefits and difficulties in the field when utilizing Artificial Intelligence. While current remotely piloted aircraft systems have a manual pilot operator to provide double-layer security and safety, studies show that having RPAS with a fully autonomous vehicle at its centre could significantly improve decision-making and overall mission precision, accuracy, safety, and efficiency.

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“…Unmanned aerial vehicles (UAV) are used in both civilian and military fields. These areas include fire detection and response applications [3] [4] , search and rescue operations [5] and road construction works [6]. In the contemporary era, a plethora of missions demands UAVs to navigate over expansive regions or accomplish tasks within tight time constraints.…”

Section: Introductionmentioning

confidence: 99%

Three Dimensional Formation Control of Unmanned Aerial Vehicles in Obstacle Environments

BEKMEZ,

ARAM

2023

Balkan Journal of Electrical and Computer Engineering

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Today, the use of small unmanned aerial vehicles (UAVs) has increased due to technological advances. There has been an interest in using multiple UAVs instead of a single UAV to accomplish a given mission. This is because there are many scenarios where the capabilities of a single UAV are inadequate due to certain constraints (battery capacity, time in the air). For this reason, swarm UAV studies have increased. A swarm UAV consists of a large number of UAVs cooperating to accomplish a specific mission. This study shows three-dimensional formation control of a swarm UAV system in an obstacle environment. A centralized control architecture is used in this process. All task assignments are made from a centralized system. The Artificial potential fields method creates the formation at the target point by avoiding obstacles. The study was carried out using the Robot Operating System (ROS). The methods were tested in Webots simulation environment. Crazyflie robots were used in the experiments. In the simulation environment, square, star and v formations were first tested in two dimensions. Then, as an example of three-dimensional formation, the cubic and pyramid formations were created and observed.

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Cloud Enabled Smart Firefighting Drone Using Internet of Things

Cited by 24 publications

References 21 publications

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

The Evolution of Intelligent Transportation Systems: Analyzing the Differences and Similarities between IoV and IoFV

Survey of RPAS Autonomous Control Systems Using Artificial Intelligence

Three Dimensional Formation Control of Unmanned Aerial Vehicles in Obstacle Environments

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