Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Annepu, Visalakshi; Anbazhagan, Rajesh

doi:10.1002/dac.4173

Cited by 14 publications

(15 citation statements)

References 38 publications

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“…Many applications and networking protocols need an appropriate sensor node position because the QoS is dependent on the accuracy of localization. Annepu and Anbazhagan (2020) proposed UAV‐aided localization over conventional fixed land anchor locations to improve localization accuracy and decrease implementation costs. In the UAV localization, the UAV flying height was initially optimized, and the least square problem for node localization is proposed using this optimum height.…”

Section: Reported Work In the State‐of‐the‐artmentioning

confidence: 99%

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

Khan

Gupta

2022

Journal of Field Robotics

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In recent years, Unmanned Aerial Vehicles (UAVs) have gained significant interest in research groups due to the wide range of applications, such as disaster surveillance and monitoring, rescue operations, military, civil, and so forth. UAVs are most often used to fulfill both user's services and requirements such as wireless communication facilities to end-users, as a relay node to extend the coverage of the core network, and so forth. UAVs are versatile in design and can cover larger areas, contrary to the Tethered Balloon and Loon Balloon systems. Generally in any natural or humanmade disaster, there is a high potential risk of damage to buildings, transport systems, communication systems, and basic services. During heavy disasters like landslides, forest fires, floods, earthquakes, and so forth, the conventional terrestrial communication system gets destroyed, and people face many problems. In this case, UAVs prove to offer a better solution to provide fast, cost-effective, easy to deploy, and secure wireless communication to the victims. But there are some issues like interference between UAVs and other base stations, coordination between UAVs, Quality of Service requirements, Size, Weight, and Power limitation, delay, coverage, positioning of UAVs, and so forth. This study article mainly highlighted these issues and try to present the recent developments of the state-of-the-art to overcome these issues. In UAV communications, with an increasing emphasis on how UAVs can be integrated with different technologies, such as the Internet of Things, Wireless Sensor Network, Heterogeneous Network, and Cloud computing. The primary aim of this article is to examine how UAVs can assist survivors in floods, earthquakes, tsunamis, or in any natural or human-made disaster situations, either in the present or soon. Also, it focused on various applications of UAVs in disaster management (DM). It underlines the significance of UAVs in DM and their advantages. It also focuses on the various issues and challenges faced by the UAV-based infrastructure and security issues and gives future directions.

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Section: Reported Work In the State‐of‐the‐artmentioning

confidence: 99%

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

Khan

Gupta

2022

Journal of Field Robotics

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“…In addition, the algorithm being complex makes the training speed of nodes slow. In Reference 35 author uses an extreme learning algorithm instead of MLP for better results as the training speed of the nodes is improved. This model also suffers from shortcomings of log normal shadow fading model.…”

Section: Related Workmentioning

confidence: 99%

Localization in mobile wireless sensor networks using drones

Kaushik

Lobiyal

2021

Trans Emerging Tel Tech

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Localization is one of the most important aspects of wireless sensor networks.Most of the localization algorithms proposed in the literature, work for static networks. However, the performance of these protocols in the mobile environment still needs to be investigated. Localization in a mobile environment may pose challenges of accuracy and computational complexity due to mobility of the nodes. In this article, we have proposed an algorithm for localization in mobile wireless sensor networks using drones (LMWSND). A drone-based algorithm is proposed to simulate the moving trajectory of the drones. The proposed drone-based algorithm uses the technique of received signal strength between the drone and unknown sensor nodes to find the distance between them. The distance thus obtained is used for localization of the unknown nodes. A new method is used to solve the system of distance equations to restrict the propagation of error. Furthermore, the mathematical analysis of the propagation error in LMWSND proves the superiority of our proposed algorithm in comparison to other algorithms. The results obtained through simulations further strengthen our conclusion that the proposed algorithm reduces the propagation of error.We have also proposed another algorithm to choose the nearby beacon points for better utilization of the network resources and to reduce the computational efforts.

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“…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 ]. Performing wireless charging for the WSN nodes [ 28 , 29 , 30 ].…”

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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Implementation of an efficient extreme learning machine for node localization in unmanned aerial vehicle assisted wireless sensor networks

Cited by 14 publications

References 38 publications

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

Emerging UAV technology for disaster detection, mitigation, response, and preparedness

Localization in mobile wireless sensor networks using drones

WSN-Assisted UAV Trajectory Adjustment for Pesticide Drift Control

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