Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs

Cao, Hongchao; Liu, Yongxin; Yue, Xuejun; Wei, Zhu

doi:10.3390/s17081818

Cited by 57 publications

(35 citation statements)

References 32 publications

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“…Refs. [25][26]. This assessment can be, in part, leveraged off of 'systems analysis as applied to autonomy' concepts introduced in Refs.…”

Section: Sprites Integration With Autonomous and Robotic System-ofmentioning

confidence: 99%

Smart Precise Rotorcraft InTerconnected Emergency Services (SPRITES)

Young

2018

2018 AIAA Aerospace Sciences Meeting

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The greatest utility for small autonomous vertical lift aerial vehicles may be for public service missions, particularly those related to disaster relief and emergency response (DRER). The current work focuses on novel vertical lift aerial vehicle designs that might be well suited for search and rescue (SAR) and small package aid delivery missions. In particular, a vehicle exhibiting efficient cruise, in addition to hover and vertical takeoff and landing capability, is a key attribute for a DRER vehicle. But, correspondingly, cost, simplicity, and community acceptance are also important considerations for such vehicles in conducting their overall mission concept of operations (CONOPS).Further, it is recognized that such DRER missions will need to be supported not just by one vehicle but, instead, by an intelligent network of vehicles to fully realize their full potentiality. Finally, it should be acknowledged that advancements in efficient-cruise small autonomous vertical lift aerial vehicles, ostensibly for DRER missions, will also have a significant cross-cutting technology application to other UAV missions.

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“…Refs. [25][26]. This assessment can be, in part, leveraged off of 'systems analysis as applied to autonomy' concepts introduced in Refs.…”

Section: Sprites Integration With Autonomous and Robotic System-ofmentioning

confidence: 99%

Smart Precise Rotorcraft InTerconnected Emergency Services (SPRITES)

Young

2018

2018 AIAA Aerospace Sciences Meeting

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“…This characteristic feature of IoT allows UAVs to become an integral part of IoT infrastructure. In UAV-based IoT (UIoT), UAVs can be utilized for different purposes, such as UAV trajectory planning [ 15 ], data collection from ground IoT devices [ 16 ], data sampling and reconstruction [ 17 ], energy-efficient device discovery [ 18 ]. The usage of drones can enhance the various aspects of smart cities, such as data collection, privacy and security, public safety, disaster management, energy consumption, and quality of life.…”

Section: Introductionmentioning

confidence: 99%

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

Khisa

Moh

2020

Sensors

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The Internet of Things (IoT), which consists of a large number of small low-cost devices, has become a leading solution for smart cities, smart agriculture, smart buildings, smart grids, e-healthcare, etc. Integrating unmanned aerial vehicles (UAVs) with IoT can result in an airborne UAV-based IoT (UIoT) system and facilitate various value-added services from sky to ground. In addition to wireless sensors, various kinds of IoT devices are connected in UIoT, making the network more heterogeneous. In a UIoT system, for achieving high throughput in an energy-efficient manner, it is crucial to design an efficient medium access control (MAC) protocol because the MAC layer is responsible for coordinating access among the IoT devices in the shared wireless medium. Thus, various MAC protocols with different objectives have been reported for UIoT. However, to the best of the authors’ knowledge, no survey had been performed so far that dedicatedly covers MAC protocols for UIoT. Hence, in this study, state-of-the-art MAC protocols for UIoT are investigated. First, the communication architecture and important design considerations of MAC protocols for UIoT are examined. Subsequently, different MAC protocols for UIoT are classified, reviewed, and discussed with regard to the main ideas, innovative features, advantages, limitations, application domains, and potential future improvements. The reviewed MAC protocols are qualitatively compared with regard to various operational characteristics and system parameters. Additionally, important open research issues and challenges with recommended solutions are summarized and discussed.

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“…UAVs help in performing time-sensitive tasks [ 8 , 9 , 10 , 11 ], monitoring emergencies [ 12 ], saving energy of WSN [ 13 , 14 , 15 , 16 , 17 ], extending the lifetime of WSN network [ 14 , 18 ], connecting separate nodes and networks [ 12 , 19 ], and enabling data aggregation [ 17 , 20 ]. Measures such as optimizing the flight routes [ 9 , 12 , 14 , 15 , 16 , 19 ] and locations [ 15 , 19 , 21 ] of UAVs, designing clustering mechanisms [ 17 , 18 , 22 ], media access control mechanisms [ 10 , 18 , 23 , 24 ], and sleeping schedules of WSN [ 14 ] and the like have been adopted to achieve these targets. Assisting in locating the WSN nodes [ 25 , 26 , 27 ].…”

Section: Introductionmentioning

confidence: 99%

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

Tuan

Yang

et al. 2020

Sensors

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Unmanned Aerial Vehicles (UAVs) have been widely applied for pesticide spraying as they have high efficiency and operational flexibility. However, the pesticide droplet drift caused by wind may decrease the pesticide spraying efficiency and pollute the environment. A precision spraying system based on an airborne meteorological monitoring platform on manned agricultural aircrafts is not adaptable for. So far, there is no better solution for controlling droplet drift outside the target area caused by wind, especially by wind gusts. In this regard, a UAV trajectory adjustment system based on Wireless Sensor Network (WSN) for pesticide drift control was proposed in this research. By collecting data from ground WSN, the UAV utilizes the wind speed and wind direction as inputs to autonomously adjust its trajectory for keeping droplet deposition in the target spraying area. Two optimized algorithms, namely deep reinforcement learning and particle swarm optimization, were applied to generate the newly modified flight route. At the same time, a simplified pesticide droplet drift model that includes wind speed and wind direction as parameters was developed and adopted to simulate and compute the drift distance of pesticide droplets. Moreover, an LSTM-based wind speed prediction model and a RNN-based wind direction prediction model were established, so as to address the problem of missing the latest wind data caused by communication latency or a lack of connection with the ground nodes. Finally, experiments were carried out to test the communication latency between UAV and ground WSN, and to evaluate the proposed scheme with embedded Raspberry Pi boards in UAV for feasibility verification. Results show that the WSN-assisted UAV trajectory adjustment system is capable of providing a better performance of on-target droplet deposition for real time pesticide spraying with UAV.

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Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs

Cited by 57 publications

References 32 publications

Smart Precise Rotorcraft InTerconnected Emergency Services (SPRITES)

Smart Precise Rotorcraft InTerconnected Emergency Services (SPRITES)

Medium Access Control Protocols for the Internet of Things Based on Unmanned Aerial Vehicles: A Comparative Survey

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

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