Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Annepu, Visalakshi; Sona, Deepika Rani; Ravikumar, C. V.; Bagadi, Kala Praveen; Alibakhshikenari, Mohammad; Althuwayb, Ayman A.; Alali, Bader; Virdee, Bal S.; Pau, Giovanni; Dayoub, Iyad; See, Chan Hwang; Falcone, Francisco

doi:10.1109/access.2022.3230661

Cited by 26 publications

(9 citation statements)

References 90 publications

(87 reference statements)

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“…Unlike traditional computing approaches that rely on precise logic and mathematical models, soft computing techniques provide approximate solutions that can efectively handle uncertain and incomplete data. Due to their merits, soft computing techniques are efectively applied to numerous engineering problems [6][7][8][9][10]. Fire detection systems have grabbed signifcant attention from researchers during the past few decades.…”

Section: Integration With Iot and Smartmentioning

confidence: 99%

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Vaegae,

Annepu,

Bagadi

et al. 2024

Journal of Optimization

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As smart cities expand rapidly, the demand for strong fire protection has become even more essential. A significant challenge lies in ensuring that fire detection systems integrate seamlessly into our modern infrastructures. Leveraging multisensor systems can yield reliable data on potential fires. Particularly in smart buildings, the effectiveness of multisensor fire detection algorithms becomes paramount. This research introduces a fuzzy logic-driven method that harnesses the power of smoke, flame, and temperature sensors. While smoke, flames, and elevated temperatures are primary fire indicators, they can manifest concurrently or sequentially. We have amalgamated various fuzzy logic rules (IF-THEN structures) to gauge the intensity of fires. When a fire ignites, the sensors spring into action, identifying its source and promptly notifying users via the Internet and GSM modems. Moreover, they relay the fire’s precise geographic coordinates to fire departments. The fire’s status will be consistently updated on a dedicated online portal. It was observed from our results that the system proficiently sends fire alerts to residents, and the fire status undergoes regular updates at 45-second intervals. This refreshing is initiated by the identification of a designated percentage of smoke and flame. These outcomes validate the system’s effectiveness in enhancing the precision and responsiveness of fire detection capabilities.

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Section: Integration With Iot and Smartmentioning

confidence: 99%

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Vaegae,

Annepu,

Bagadi

et al. 2024

Journal of Optimization

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“…These sensors can measure or detect physical quantities, like humidity, temperature, or pressure, and they can transmit this data over short distances. Only when we know in advance where the wireless sensor nodes are located within the network can we make use of the sensed data [3], [4].…”

Section: Introduction and Overviewmentioning

confidence: 99%

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Abdulhussein Abdulzahra,

Kadhum M. Al-Qurabat

2024

IJCDS

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UAVs (unmanned aerial vehicles) and WSNs (wireless sensor networks) are now two well-established technologies for monitoring, target tracking, event detection, and remote sensing. Typically, WSN is made up of thousands or even millions of tiny, battery-operated devices that measure, gather, and send information from their surroundings to a base station or sink. Within the realm of wireless positioning and communication, UAVs have garnered a lot of interest because of their remarkable mobility and simplistic deployment to tackle the problems of imprecise sensor placement, inadequate infrastructure coverage, and the massive quantity of sensing data that WSN collects. A crucial prerequisite for many position-based WSN applications is node location, or localization. The use of UAVs for localization is more preferable than permanent terrestrial anchor nodes due to their high accuracy and minimal implementation complexity. The possible interference or signal block in such an operating environment, however, might cause the Global Positioning System (GPS) to become ineffective or unobtainable. In these conditions, the need for innovative UAV-based sensor node location technologies has become essential. Radio frequency (RF)-based localization techniques are reviewed in the current paper. We examine the available RF features for localization and look into the current approaches that work well for unmanned vehicles. The most recent research on RF-based UAV localization is reviewed, along with potential avenues for future investigation.

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“…Such localization accuracy effectively minimizes water waste, mitigates the risk of over-irrigation, and facilitates customized irrigation scheduling. By estimating the coordinates of the unknown nodes based on their range measurements to anchor nodes with known positions, the overall network performance can be improved, resulting in more effective resource allocation and optimized irrigation strategies [18].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Smart Irrigation Efficiency: A New WSN-Based Localization Method for Water Conservation

Hassan,

Alharbi,

Oshaba

et al. 2024

Water

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The shortage of water stands as a global challenge, prompting considerable focus on the management of water consumption and irrigation. The suggestion is to introduce a smart irrigation system based on wireless sensor networks (WSNs) aimed at minimizing water consumption while maintaining the quality of agricultural crops. In WSNs deployed in smart irrigation, accurately determining the locations of sensor nodes is crucial for efficient monitoring and control. However, in many cases, the exact positions of certain sensor nodes may be unknown. To address this challenge, this paper presents a new localization method for localizing unknown sensor nodes in WSN-based smart irrigation systems using estimated range measurements. The proposed method can accurately determine the positions of unknown nodes, even when they are located at a distance from anchors. It utilizes the Levenberg–Marquardt (LM) optimization algorithm to solve a nonlinear least-squares problem and minimize the error in estimating the unknown node locations. By leveraging the known positions of a subset of sensor nodes and the inexact distance measurements between pairs of nodes, the localization problem is transformed into a nonlinear optimization problem. To validate the effectiveness of the proposed method, extensive simulations and experiments were conducted. The results demonstrate that the proposed method achieves accurate localization of the unknown sensor nodes. Specifically, it achieves 19% and 58% improvement in estimation accuracy when compared to distance vector-hop (DV-Hop) and semidefinite relaxation-LM (SDR-LM) algorithms, respectively. Additionally, the method exhibits robustness against measurement noise and scalability for large-scale networks. Ultimately, integrating the proposed localization method into the smart irrigation system has the potential to achieve approximately 28% reduction in water consumption.

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Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Cited by 26 publications

References 90 publications

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Multisensor Fuzzy Logic Approach for Enhanced Fire Detection in Smart Cities

Exploring Radio Frequency-Based UAV Localization Techniques: A Comprehensive Review

Enhancing Smart Irrigation Efficiency: A New WSN-Based Localization Method for Water Conservation

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