A Cascaded Approach to Optimal Aircraft Trajectories for Persistent Data Ferrying

Carfang, Anthony J.; Frew, Eric W.; Kingston, Derek

doi:10.2514/6.2013-4558

Cited by 10 publications

(8 citation statements)

References 14 publications

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“…Then the following shows this constraint. (4) We know that each sensor has an energy budget, and energy is allocated equally to all of the complete processes for each sensor, it means at complete process t the total energy for sensing and transmission for sensor k in field n is . Therefore, the following constraint should be satisfied.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

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Resource allocation for data gathering in UAV-aided wireless sensor networks

Huang

Nishiyama

Kato

et al. 2014

2014 4th IEEE International Conference on Network Infrastructure and Digital Content

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As the development of unmanned aerial vehicle (UAV) communication in the residential and commercial areas, different from the traditional wireless sensor networks (WSNs), in this paper we consider a two-layer wireless networks for data gathering, one is sensor nodes to UAVs layer, the other one is UAVs to the outside layer. The sensor nodes sense data and transmit to UAVs, and then UAVs send data to the outside. In this paper, we only focus on the first layer, and we suppose the capacity is sufficient for UAVs to transmit their gathered data in the second layer. The sensor nodes aim to maximize the total data transmitting rate. Because of the limited resources (i.e. limited bandwidth, limited energy of each node, and so on) in the system, thus how to allocate the limited resources to maximize the total data transmitting rate is challenging, and it is necessary to propose an efficient algorithm to allocate the resources. We formulate the problem that jointly considers bandwidth allocation and energy allocation to maximize the total transmitting rate while guarantees the rate in each time slot for each sensor node. Then an optimal algorithm based on dynamic programming named DPBA is proposed. Finally, we conduct the extensive simulation to compare our proposed algorithm and the benchmark algorithm (i.e. equal resource allocation algorithm, denoted by ERAA). Simulation results show that our proposed algorithm outperforms the benchmark algorithm.

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Section: System Model and Problem Formulationmentioning

confidence: 99%

“…UAV is an aircraft without pilots, was first used in military. Recently, UAVs are used in many applications, such as monitoring traffic [3], data ferrying [4], communication enhanced [5][6][7][8][9][10][11], and collecting data [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Resource allocation for data gathering in UAV-aided wireless sensor networks

Huang

Nishiyama

Kato

et al. 2014

2014 4th IEEE International Conference on Network Infrastructure and Digital Content

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“…WPT has been widely used in portable electronic devices, implanted medical devices, smart homes, electric vehicles (EVs), and so on [9]. Applying the WPT to wireless communication networks can permit wireless powered communication networks (WPCN) to be constructed and can effectively improve the energy efficiency of the network [10,11,12,13,14,15,16,17,18,19]. Compared with the conventional WPT system with a relatively fixed energy source, UAV can dynamically move the IoT devices around, power the IoT devices, and realize the data gathering and transmitting services [20].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things

Liu

Zhou

2019

Sensors

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Most of the wireless nodes in the Internet of Things (IoT) environment face the limited energy problem and the way to provide a sustainable energy for these nodes has become an urgent challenge. In this paper, we present an unmanned aerial vehicle (UAV) to power the wireless nodes in the IoT and an investigation on the optimal resource allocation approach based on dynamic game theory. This IoT system consists of one UAV as the power source and information receiver. The wireless nodes can be powered and collected by the UAV. In order to distinguish the wireless nodes, the wireless nodes are divided into two categories based on the energy consumption. The UAV tries to power these two categories of nodes using a different power level based on the proposed approach, where the wireless nodes control the resources for information transmission. Based on Bellman dynamic programming, the optimal allocated resources for power transfer and information transmission are obtained for both the UAV and wireless nodes, respectively. In order to show the effectiveness of the proposed model and approach, we present numerical simulations.

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“…Fortunately, the explorations of unmanned aerial vehicles (UAVs) as flying wireless communication networks at various altitudes have recently made significant progress to achieve dynamic and adaptive coverage [ 4 , 5 ]. Because UAVs are flexible, portable, inexpensive, and convenient [ 6 ], they have been widely applied for various purposes [ 7 ], i.e., traffic monitoring, forest fire detection emergency event supervision [ 6 , 8 ], data ferrying [ 9 ], communication enhancement [ 10 ], data collection [ 11 ], 3D city map construction [ 12 , 13 ], and wireless connectivity provisioning (e.g., projects Loon by Google and Internet.org by Facebook) [ 14 ]. According to the Federal Aviation Administration, sales of UAVs for commercial purposes are expected to grow from 600,000 in 2016 to 2.7 million by 2020 [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Speed Control of Unmanned Aerial Vehicles for Data Collection under Internet of Things

Pan

Wen

et al. 2018

Sensors

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With the new advancements in flight control and integrated circuit (IC) technology, unmanned aerial vehicles (UAVs) have been widely used in various applications. One of the typical application scenarios is data collection for large-scale and remote sensor devices in the Internet of things (IoT). However, due to the characteristics of massive connections, access collisions in the MAC layer lead to high power consumption for both sensor devices and UAVs, and low efficiency for the data collection. In this paper, a dynamic speed control algorithm for UAVs (DSC-UAV) is proposed to maximize the data collection efficiency, while alleviating the access congestion for the UAV-based base stations. With a cellular network considered for support of the communication between sensor devices and drones, the connection establishment process was analyzed and modeled in detail. In addition, the data collection efficiency is also defined and derived. Based on the analytical models, optimal speed under different sensor device densities is obtained and verified. UAVs can dynamically adjust the speed according to the sensor device density under their coverages to keep high data collection efficiency. Finally, simulation results are also conducted to verify the accuracy of the proposed analytical models and show that the DSC-UAV outperforms others with the highest data collection efficiency, while maintaining a high successful access probability, low average access delay, low block probability, and low collision probability.

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A Cascaded Approach to Optimal Aircraft Trajectories for Persistent Data Ferrying

Cited by 10 publications

References 14 publications

Resource allocation for data gathering in UAV-aided wireless sensor networks

Resource allocation for data gathering in UAV-aided wireless sensor networks

Resource Allocation in Unmanned Aerial Vehicle (UAV)-Assisted Wireless-Powered Internet of Things

Dynamic Speed Control of Unmanned Aerial Vehicles for Data Collection under Internet of Things

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