scholarly journals Energy-Efficient On-Platform Target Classification for Electric Air Transportation Systems

Electricity ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 110-123
Author(s):  
Nicholas A. Speranza ◽  
Christopher J. Rave ◽  
Yong Pei
Keyword(s):  
Energy Consumption ◽  
Real Time ◽  
Energy Efficient ◽  
Air Transportation ◽  
Transportation Systems ◽  
Unmanned Aircraft ◽  
Experimental Methodology ◽  
Target Classification ◽  
Additional Energy ◽  
Air Transportation Systems

Due to the predicted rise of Unmanned Aircraft Systems (UAS) in commercial, civil, and military operations, there is a desire to make UASs more energy efficient so they can proliferate with ease of deployment and maximal life per charge. To address current limitations, a three-tiered approach is investigated to mitigate Unmanned Aerial Vehicle (UAV) hover time, reduce network datalink transmission to a ground station, and provide a real-time framework for Sense-and-Avoidance (SAA) target classification. An energy-efficient UAS architecture framework is presented, and a corresponding SAA prototype is developed using commercial hardware to validate the proposed architecture using an experimental methodology. The proposed architecture utilizes classical computer vision methods within the Detection Subsystem coupled with deeply learned Convolutional Neural Networks (CNN) within the Classification Subsystem. Real-time operations of three frames per second are realized enabling UAV hover time and associated energy consumption during SAA processing to be effectively eliminated. Additional energy improvements are not addressed in the scope of this work. Inference accuracy is improved by 19% over baseline COTS models and current non-adaptive, single-stage SAA architectures. Overall, by pushing SAA processing to the edge of the sensors, network offload transmissions and reductions in processing time and energy consumption are feasible and realistic in future battery-powered electric air transportation systems.

scholarly journals MULTI-AIRPORT SYSTEMS AS A GLOBAL TOURISM PHENOMENON: A CRITICAL REVIEW AND A NEW CONCEPT

2019 ◽  
Vol 6 (1) ◽  
pp. 01-11
Author(s):  
Dr. Farouk Abdelnabi Hassanein Attaalla
Keyword(s):  
Critical Review ◽  
Air Transportation ◽  
Transportation Systems ◽  
Primary Data ◽  
Basic Tool ◽  
International Experiences ◽  
Global Tourism ◽  
Different Dimensions ◽  
Air Transportation Systems ◽  
Shed Light

Purpose:The main objective of the present study is to shed light on the different dimensions and international experiences of the multi-airport system including the Egyptian experience in this regard. Methodology:The methodology of the study depends on the researcher's own critical review based on his scientific background on the phenomenon of multi-airport systems through survey in secondary and primary data. Findings:Finally, the study presented a new comprehensive concept of the multi-airport system. The importance and originality of the current research is to ameliorate the concept of multiple-airport system in light of displaying some of international experiences. Implications:The transition from single-airport to multi-airport systems is going to be a basic tool by which air transportation systems will be able to meet future demand. There are many experiences related to the failure and success of managing the multi-airport systems worldwide.

scholarly journals High Confidence Networked Control for Next Generation Air Transportation Systems

2014 ◽  
Vol 59 (12) ◽  
pp. 3357-3372 ◽  
Author(s):  
Pangun Park ◽  
Harshad Khadilkar ◽  
Hamsa Balakrishnan ◽  
Claire J. Tomlin
Keyword(s):  
Air Transportation ◽  
Transportation Systems ◽  
Networked Control ◽  
Next Generation ◽  
High Confidence ◽  
Air Transportation Systems

scholarly journals Energy Efficient and Real-Time Remote Sensing in AI-Powered Drone

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bongjae Kim ◽  
Jinman Jung ◽  
Hong Min ◽  
Junyoung Heo
Keyword(s):  
Remote Sensing ◽  
Energy Consumption ◽  
Data Analysis ◽  
Real Time ◽  
Energy Efficient ◽  
Urban Areas ◽  
Computation Offloading ◽  
Time Constraints ◽  
Time Data ◽  
Real Time Data

Remote sensing using drones has the advantage of being able to quickly monitor large areas such as rivers, oceans, mountains, and urban areas. In the case of applications dealing with large sensing data, it is not possible to send data from a drone to the server online, so it must be copied to the server offline after the end of the flight. However, online transmission is essential for applications that require real-time data analysis. The existing computation offloading scheme enables online transmission by processing large amounts of data in a drone and transferring it to the server, but without consideration for real-time constraints. We propose a novel computation offloading scheme which considers real-time constraints while minimizing the energy consumption of drones. Experimental results showed that the proposed scheme satisfied real-time constraints compared to the existing computation offloading scheme. Furthermore, the proposed technique showed that real-time constraints were satisfied even in situations where delays occurred on the server due to the processing of requests from multiple drones.

scholarly journals Energy-aware mobile edge computing for low-latency visual data processing

2017 ◽  
Author(s):  
◽  
Huy Trinh
Keyword(s):  
Decision Making ◽  
Energy Consumption ◽  
Data Processing ◽  
Real Time ◽  
Energy Efficient ◽  
User Preferences ◽  
Low Latency ◽  
Mobile Edge Computing ◽  
Incident Response ◽  
Visual Data

New paradigms such as Mobile Edge Computing (MEC) are becoming feasible for use in e.g., real-time decision-making during disaster incident response to handle the data deluge occurring in the network edge. However, MEC deployments today lack flexible IoT device data handling such as e.g., handling user preferences for real-time versus energy-efficient processing. Moreover, MEC can also benefit from a policy based edge routing to handle sustained performance levels with efficient energy consumption. In this thesis, we study the potential of MEC to address application issues related to energy management on constrained IoT devices with limited power sources, while also providing low-latency processing of visual data being generated at high resolutions. Using a facial recognition application that is important in disaster incident response scenarios, we propose a novel 'offload decision-making' algorithm that analyzes the tradeoffs in computing policies to offload visual data processing (i.e., to an edge cloud or a core cloud) at low-to-high workloads. This algorithm also analyzes the impact on energy consumption in the decision-making under different visual data consumption requirements (i.e., users with thick clients or thin clients). To address the processing-throughput versus energy-efficiency tradeoffs, we propose a ‘Sustainable Policy-based Intelligence-Driven Edge Routing' (SPIDER) algorithm that uses machine learning within Mobile Ad hoc Networks (MANETs). This algorithm improves the geographic routing baseline performance (i.e., minimizes impact of local minima) for performance sustainability, and enables easy/flexible policy specification. We evaluate our proposed algorithms by conducting experiments on a realistic edge and core cloud testbed, and recreate disaster scenes of tornado damages (occurred in Joplin, MO in 2011) within simulations. From our empirical results obtained from experiments with a facial recognition application in the GENI Cloud testbed, we show how MEC can provide flexibility to users who desire energy conservation over low-latency or vice versa in the visual data processing. Our NS-3 based simulation results show that our routing approach is more sustainable in terms of throughput, more energy-efficient and flexible than existing solutions to handle diverse user preferences under high node mobility and severe node failure conditions.

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