A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Pinto, Milena F.; Marcato, André L. M.; Melo, Aurélio G.; Honório, Leonardo de Mello; Urdiales, Cristina

doi:10.1155/2019/7497924

Cited by 47 publications

(32 citation statements)

References 47 publications

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“…The first layer consists of a basic perceptual system, such as an IMU and GPS processing, flight control loops [ 35 ], communication [ 37 ], payload evaluation [ 38 ], image acquisition, basic collision avoidance [ 39 ], and low-level navigation (point-to-point) [ 40 ] which are responsible for the reactive behavior. It has hard real-time requirements and it runs embedded at the aircraft’s flight control unit (FCU).…”

Section: The Aerial Robotics Cognitive Architecturementioning

confidence: 99%

A Robotic Cognitive Architecture for Slope and Dam Inspections

Pinto

Honório

Melo

et al. 2020

Sensors

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Big construction enterprises, such as electrical power generation dams and mining slopes, demand continuous visual inspections. The sizes of these structures and the necessary level of detail in each mission requires a conflicting set of multi-objective goals, such as performance, quality, and safety. It is challenging for human operators, or simple autonomous path-following drones, to process all this information, and thus, it is common that a mission must be repeated several times until it succeeds. This paper deals with this problem by developing a new cognitive architecture based on a collaborative environment between the unmanned aerial vehicles (UAVs) and other agents focusing on optimizing the data gathering, information processing, and decision-making. The proposed architecture breaks the problem into independent units ranging from sensors and actuators up to high-level intelligence processes. It organizes the structures into data and information; each agent may request an individual behavior from the system. To deal with conflicting behaviors, a supervisory agent analyzes all requests and defines the final planning. This architecture enables real-time decision-making with intelligent social behavior among the agents. Thus, it is possible to process and make decisions about the best way to accomplish the mission. To present the methodology, slope inspection scenarios are shown.

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Section: The Aerial Robotics Cognitive Architecturementioning

confidence: 99%

A Robotic Cognitive Architecture for Slope and Dam Inspections

Pinto

Honório

Melo

et al. 2020

Sensors

Self Cite

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“…The fog computing paradigm applied to UAV-based large-scale monitoring systems is analyzed, which assumes that the path planning, flight coordination and task planning are carried out locally at the fog coordinator level [74]. This is reflected in comparison to the usage of a cloud platform, which potentially offers more computing resources and knowledge with increased data and command latency.…”

Section: Collaborative Operation In Uav–wsn Applicationsmentioning

confidence: 99%

A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring

Popescu

Stoican

Stâmâtescu

et al. 2019

Sensors

110

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Integrated systems based on wireless sensor networks (WSNs) and unmanned aerial vehicles (UAVs) with electric propulsion are emerging as state-of-the-art solutions for large scale monitoring. Main advances stemming both from complex system architectures as well as powerful embedded computing and communication platforms, advanced sensing and networking protocols have been leveraged to prove the viability of this concept. The design of suitable algorithms for data processing, communication and control across previously disparate domains has thus currently become an intensive area of interdisciplinary research. The paper was focused on the collaborative aspects of UAV–WSN systems and the reference papers were analyzed from this point of view, on each functional module. The paper offers a timely review of recent advances in this area of critical interest with focus on a comparative perspective across multiple recent theoretical and applied contributions. A systematic approach is carried out in order to structure a unitary from conceptual design towards key implementation aspects. Focus areas are identified and discussed such as distributed data processing algorithms, hierarchical multi-protocol networking aspects and high level WSN–constrained UAV-control. Application references are highlighted in various domains such as environmental, agriculture, emergency situations and homeland security. Finally, a research agenda is outlined to advance the field towards tangible economic and social impact.

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“…Reference [25] proposes a fog-cloud computing architecture for unmanned aerial vehicles and carried out the corresponding computing performance analysis. A utility-aware data transmission mechanism for delay tolerant networks is proposed in [26], which considers the internal properties of nodes and external contacts.…”

Section: Related Workmentioning

confidence: 99%

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

et al. 2019

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In recent years, unmanned surface vehicles (USVs) have made important advances in civil, maritime, and military applications. With the continuous improvement of autonomy, the increasing complexity of tasks, and the emergence of various types of advanced sensors, higher requirements are imposed on the computing performance of USV clusters, especially for latency sensitive tasks. However, during the execution of marine operations, due to the relative movement of the USV cluster nodes and the network topology of the cluster, the wireless channel states are changing rapidly, and the computing resources of cluster nodes may be available or unavailable at any time. It is difficult to accurately predict in advance. Therefore, we propose an optimized offloading mechanism based on the classic multi-armed bandit (MAB) theory. This mechanism enables USV cluster nodes to dynamically make offloading decisions by learning the potential computing performance of their neighboring team nodes to minimize average computation task offloading delay. It is an optimized algorithm named Adaptive Upper Confidence Boundary (AUCB) algorithm, and corresponding simulations are designed to evaluate the performance. The algorithm enables the USV cluster to effectively adapt to the marine vehicular fog computing networks, balancing the trade-off between exploration and exploitation (EE). The simulation results show that the proposed algorithm can quickly converge to the optimal computation task offloading combination strategy under heavy and light input data loads.

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A Framework for Analyzing Fog-Cloud Computing Cooperation Applied to Information Processing of UAVs

Cited by 47 publications

References 47 publications

A Robotic Cognitive Architecture for Slope and Dam Inspections

A Robotic Cognitive Architecture for Slope and Dam Inspections

A Survey of Collaborative UAV–WSN Systems for Efficient Monitoring

Learning-Based Task Offloading for Marine Fog-Cloud Computing Networks of USV Cluster

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