UAV Path Optimization for Precision Agriculture Wireless Sensor Networks

Unmanned aerial vehicles (UAVs), commonly known as drones, have been progressively prevalent due to their capability to operate quickly and their vast range of applications in a variety of real-world circumstances. The utilization of UAVs in precision farming has lately gained a lot of attention from the scientific community. This study addresses with the assistance of drones in the precision agricultural area. This paper makes significant contributions by analyzing communication protocols and applying them to the challenge of commanding a fleet of drones to protect crops from parasite infestations. In this research, the effectiveness of nine powerful deep neural network models is measured for the detection of plant diseases using diverse methodologies. These deep neural networks are adapted to the immediate situation using transfer learning and deep extraction of features approaches. The presented study takes into account the used pretrained deep learning model for extracting features and fine-tuning. The deep feature extraction characteristics are subsequently categorized using support vector machines (SVMs) and extreme learning machines (ELMs). For measuring performance, the precision, sensitivities, specific, and F1-score are all evaluated. Deep feature extraction and SVM/ELM classification generated better outcomes than transfer learning, according to the analysis result. Furthermore, the analysis of the various methodologies tries to assess their effectiveness and costs. The different approaches, for example, confront difficulties such as investigating the region in the shortest possible time feasible, while eliminating the same region being searched by more drones, detecting parasites, and stopping their spread by applying the appropriate number of pesticides. Simulation models are a significant aid to researchers in conducting to evaluate these technologies and creating specific tactics and coordinating procedures capable of effectively supporting farms and achieving the aim. The main objective of this paper is to compare the search techniques of two distinct methods of parasitic to identify performance.

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“…e subdivisions that followed go through domain adaptation and deep extraction of features in more depth [24].…”

Section: Data Collectionmentioning

confidence: 99%

Application of IoT-Based Drones in Precision Agriculture for Pest Control

Refaai

Dattu

Gireesh³

et al. 2022

Advances in Materials Science and Engineering

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“…e experimental results show the communication quality of UAV-WSN compared with that of static WSN; the average link consumption of plots 1, 2, and 3 is reduced by about 10%, 27%, and 14%, and the average packet loss rate is reduced by about 24%, 68%, and 29%, respectively. G. Just et al [19] using the management concept of time slot and the thought of combining no-fly list, put forward a kind of effective detection and reconstruction algorithm of sensor node activation time, the path planning of unmanned aerial vehicle (UAV) to synchronize with the activation of each sensor node cycle, thereby reducing the UAV's flight time, reducing the power consumption of the node, and shortening the UAV's flight path. However, after the node timeslot of this model is reconfigured, the UAV routing needs to be reprogrammed, which increases the energy cost of the node and UAV.…”

Section: Introductionmentioning

confidence: 99%

Large-Area Saline-Alkali-Tolerant Rice Growth Environment Monitoring System Based on LoRa+UAV

Xie

Wang

2022

Advances in Materials Science and Engineering

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A wireless sensor network (WSN) monitoring system for saline-alkali-tolerant rice based on long-range + unmanned aerial vehicle (LoRa + UAV) has been proposed in this paper to meet the requirements of difficult deployment, low scalability, and poor network reliability of traditional farmland environmental monitoring systems. The system automatically forms a double-layer star topology network through the terminal node and central node (intranet), central node, and UAV mobile node (extranet). Data transmission is carried out through LoRa technology, routing planning is carried out for UAV mobile receiving nodes at different flight altitudes, and an early warning mechanism for UAV energy is added. It effectively prevents the UAV from destroying the communication capability of the external network due to energy exhaustion during operation. The system transmits information remotely to the server of the management center through a 5G network, which analyzes and processes the data and publishes it on the Web. The system has been tested in the field, and the reliable communication range of the node is 1∼4 km under the saline-alkali-tolerant rice environment, and the service life of the terminal node can reach more than 3 months. During continuous operation, the maximum packet loss rate of the internal and external integrated networks is 1.45%. The system can stably and real-time monitor saline-alkali-tolerant rice environmental information, and the error rate is less than 10%, which can meet the performance requirements of the saline-alkali-tolerant rice environmental monitoring systems, such as low power consumption, wide coverage, stability, and reliability, and has a good engineering significance.

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“…On the other hand, the relationship that has been established today between WSNs and unmanned aerial vehicles (UAVs) is undeniable [ 36 , 37 , 38 ]. In [ 36 ], a comprehensive review is provided of the main applications of WSNs, UAVs, and monitoring technologies.…”

Section: Related Workmentioning

confidence: 99%

Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks

López-Vilos

Valencia

Souza

et al. 2023

Sensors

Self Cite

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The Internet of Things (IoT) is a key technology to interconnect the real and digital worlds, enabling the development of smart cities and services. The timely collection of data is essential for IoT services. In scenarios such as agriculture, industry, transportation, public safety, and health, wireless sensor networks (WSNs) play a fundamental role in fulfilling this task. However, WSNs are commonly deployed in sensitive and remote environments, thus facing the challenge of jamming attacks. Therefore, these networks need to have the ability to detect such attacks and adopt countermeasures to guarantee connectivity and operation. In this work, we propose a novel clustering-based self-healing strategy to overcome jamming attacks, in which we denominate fairness cooperation with power allocation (FCPA). The proposed strategy, aware of the presence of the jammer, clusters the network and designates a cluster head that acts as a sink node to collect information from its cluster. Then, the most convenient routes to overcome the jamming are identified and the transmit power is adjusted to the minimum value required to guarantee the reliability of each link. Finally, through the weighted use of the relays, the lifetime of each subnetwork is extended. To show the impact of each capability of FCPA, we compare it with multiple benchmarks that only partially possess these capabilities. In the proposal evaluation, we consider a WSN composed of 64 static nodes distributed in a square area. Meanwhile, to assess the impact of the jamming attack, we consider seven different locations of the attacker. All experiments started with each node’s battery full and stopped after one of these batteries was depleted. In these scenarios, FCPA outperforms all other strategies by more than 50% of the information transmitted, due to the efficient use of relay power, through the weighted balance of cooperative routes. On average, FCPA permits 967,961 kb of information transmitted and 63% of residual energy, as energy efficiency, from all the analyzed scenarios. Additionally, the proposed clustering-based self-healing strategy adapts to the change of jammer location, outperforming the rest of the strategies in terms of information transmitted and energy efficiency in all evaluated scenarios.

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UAV Path Optimization for Precision Agriculture Wireless Sensor Networks

Cited by 12 publications

References 35 publications

Application of IoT-Based Drones in Precision Agriculture for Pest Control

Application of IoT-Based Drones in Precision Agriculture for Pest Control

Large-Area Saline-Alkali-Tolerant Rice Growth Environment Monitoring System Based on LoRa+UAV

Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs under Jamming Attacks

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