Efficient Aerial Data Collection with UAV in Large-Scale Wireless Sensor Networks

Chen, YangQuan; Ma, Fei; Yan, Junhui; De, Debraj; Das, Sajal K.

doi:10.1155/2015/286080

Cited by 78 publications

(55 citation statements)

References 26 publications

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“…The proposed UAV trajectory planners are based on Genetics Algorithm, Rapidly-exploring Random Trees and Optimal Rapidly-exploring Random Trees. The authors in [239] design a basic framework for aerial data collection, which includes the following five components: deployment of networks, nodes positioning, anchor points searching, fast path planning for UAV, and data collection from network. They identify the key challenges in each component and propose efficient solutions.…”

Section: ) Deployment Strategies To Collect Data Using Uavsmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

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The use of unmanned aerial vehicles (UAVs) is growing rapidly across many civil application domains including realtime monitoring, providing wireless coverage, remote sensing, search and rescue, delivery of goods, security and surveillance, precision agriculture, and civil infrastructure inspection. Smart UAVs are the next big revolution in UAV technology promising to provide new opportunities in different applications, especially in civil infrastructure in terms of reduced risks and lower cost. Civil infrastructure is expected to dominate the more that $45 Billion market value of UAV usage. In this survey, we present UAV civil applications and their challenges. We also discuss current research trends and provide future insights for potential UAV uses. Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges. Based on our review of the recent literature, we discuss open research challenges and draw high-level insights on how these challenges might be approached.

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Section: ) Deployment Strategies To Collect Data Using Uavsmentioning

confidence: 99%

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

et al. 2019

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“…Different than [26], the proposed approach forms clusters and the UAV visits the elected cluster heads. Similar to the previous approach, the proposed clustering method and path planning algorithm are dependent on the node positions, and the UAV must know the location of the CHs before the mission starts.…”

Section: Motivation and Related Workmentioning

confidence: 99%

Analyzing the Effects of UAV Mobility Patterns on Data Collection in Wireless Sensor Networks

Rashed

Soytürk

2017

Sensors

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Sensor nodes in a Wireless Sensor Network (WSN) can be dispersed over a remote sensing area (e.g., the regions that are hardly accessed by human beings). In such kinds of networks, data collection becomes one of the major issues. Getting connected to each sensor node and retrieving the information in time introduces new challenges. Mobile sink usage-especially Unmanned Aerial Vehicles (UAVs)-is the most convenient approach to covering the area and accessing each sensor node in such a large-scale WSN. However, the operation of the UAV depends on some parameters, such as endurance time, altitude, speed, radio type in use, and the path. In this paper, we explore various UAV mobility patterns that follow different paths to sweep the operation area in order to seek the best area coverage with the maximum number of covered nodes in the least amount of time needed by the mobile sink. We also introduce a new metric to formulate the tradeoff between maximizing the covered nodes and minimizing the operation time when choosing the appropriate mobility pattern. A realistic simulation environment is used in order to compare and evaluate the performance of the system. We present the performance results for the explored UAV mobility patterns. The results are very useful to present the tradeoff between maximizing the covered nodes and minimizing the operation time to choose the appropriate mobility pattern.

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“…Using this method, it is possible to extend the battery life of wireless sensors, which has also been validated in several field trials conducted by Bellavista in Seville, Spain. The authors referenced in [8] proposed an architecture for flight data acquisition that was also based on data collection by a UAS. It included five important parts: a wireless network layout, a wireless sensor location, a search of connected data points, fast UAS path planning, and data collection through a wireless network.…”

Section: Related Workmentioning

confidence: 99%

Intelligent Environmental Sensing With an Unmanned Aerial System in a Wireless Sensor Network

Chen

Huang

2017

International Journal on Smart Sensing and Intelligent Systems

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-This paper proposes a novel environmental monitoring mechanism to integrate recentlyestablished development of an Unmanned Aerial System (UAS) with WSNs for remote monitoring.The high mobility of UASs can solve the limitations associated with using WSNs in hazardous areas. In this paper, the WSN node, the Wireless Environmental Monitoring Station (WEMS), is based onZigBee protocol for long-duration monitoring. Furthermore, to ensure the integrity of collected environmental data, an algorithm is designed in WEMS for verification. Finally, a detailed analysis of packet transmission efficiency based on ranges of flight distance is proposed to examine the effect of environmental monitoring.

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Efficient Aerial Data Collection with UAV in Large-Scale Wireless Sensor Networks

Cited by 78 publications

References 26 publications

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Analyzing the Effects of UAV Mobility Patterns on Data Collection in Wireless Sensor Networks

Intelligent Environmental Sensing With an Unmanned Aerial System in a Wireless Sensor Network

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