RSS Localization Using Multistep Linearization in the Presence of Unknown Path Loss Exponent

Mei, Xiaojun; Chen, Yanzhen; Xu, Xiaofeng; Wu, Huafeng

doi:10.1109/lsens.2022.3190869

Cited by 14 publications

(6 citation statements)

References 21 publications

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“…On NVIDIA JETSON TX2, the detection speed was evaluated, and the FPS of the suggested model reached 51, which satisfies the detection requirements regularly. Next, we will examine utilizing separable convolution to minimize the number of parameters further, focus on loss to increase the accuracy, combine with knowledge of location information in different conditions [ 39 , 40 , 41 , 42 ], and try to use it in different usage scenarios, such as medical image detection [ 43 ], maritime search and rescue [ 44 ].…”

Section: Discussionmentioning

confidence: 99%

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Mei

et al. 2022

Sensors

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Ship fire is one of the greatest dangers to ship navigation safety. Nevertheless, typical detection methods have limited detection effectiveness and accuracy due to distance restrictions and ship motion. Although the issue can be addressed by image recognition algorithms based on deep learning, the computational complexity and efficiency for ship detection are tough. This paper proposes a lightweight target identification technique based on the modified YOLOv4-tiny algorithm for the precise and efficient detection of ship fires, taking into account the distinctive characteristics of ship fires and the marine environment. Initially, a multi-scale detection technique is applied to broaden the detection range and integrate deep semantic information, thereby enhancing the feature information of small targets and obscured objects and improving the detection precision. Then, the proposed algorithm employs the SE attention mechanism for inter-channel feature fusion to improve the capability of feature extraction and the precision of ship fire detection. Last but not least, picture transformation and migration learning are added to the small ship fire dataset to accelerate the convergence pace, improve the convergence effect, and reduce dataset dependence. The simulation experiments reveal that the proposed I-YOLOv4-tiny + SE model outperforms the benchmark algorithm in terms of ship fire detection accuracy and detection efficiency and that it satisfies the real-time ship fire warning criteria in demanding maritime environments.

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Section: Discussionmentioning

confidence: 99%

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Mei

et al. 2022

Sensors

Self Cite

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“…Numerous methods and algorithms have been proposed to address the node localization problem, aiming to achieve accurate and efficient localization results. We categorize the related work into three main approaches: range-based, range-free, and hybrid localization techniques [19][20][21][22][23][24][25][26][27][28].…”

Section: Related Workmentioning

confidence: 99%

“…The RSS technique based on the radio propagation path loss model [27,28] is used to estimate the distance between a free node and an anchor node. The RSS in dBm within the log-normal shadow-fading model is expressed as [18]:…”

Section: Nonlinear Least-squares Problem Formulationmentioning

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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“…Early work used variations in the Received Signal Strength Indicator (RSSI) to detect activities such as walking, running, crawling and squatting to accuracies greater than 70% [3,[5][6][7]. RSSI is a simple measure of the received signal power of an inbound Wi-Fi packet, and early work has leveraged relative changes in this signal power for detecting activities in the channel or localisation [8]. Prior work has shown that these RSSI-based localisation technologies suffer in Non Line of Sight (NLOS) conditions [9], where channel blockages degrade the signal strength substantially.…”

Section: Sensing Using Wi-fimentioning

confidence: 99%

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

Sharma,

Li,

Mishra

et al. 2024

Energies

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Wireless-based sensing of physical environments has garnered tremendous attention recently, and its applications range from intruder detection to environmental occupancy monitoring. Wi-Fi is positioned as a particularly advantageous sensing medium, due to the ubiquity of Wi-Fi-enabled devices in a more connected world. Although Wi-Fi-based sensing using Channel State Information (CSI) has shown promise, existing sensing systems commonly configure dedicated transmitters to generate packets for sensing. These dedicated transmitters substantially increase the energy requirements of Wi-Fi sensing systems, and hence there is a need for understanding how ambient transmissions from nearby Wi-Fi devices can be leveraged instead. This paper explores the potential of Wi-Fi-based sensing using CSI derived from ambient transmissions of Wi-Fi devices. We demonstrate that CSI sensing accuracy is dependent on the underlying traffic type and the Wi-Fi transceiver architecture, and that control packets yield more robust CSI than payload packets. We also show that traffic containing upload data is more suitable for human occupancy counting, using the Probability Mass Function (PMF) of CSI. We further demonstrate that multiple spatially diverse streams of Wi-Fi CSI can be combined for sensing to an accuracy of 99%. The experimental study highlights the importance of training Wi-Fi sensing systems for multiple transmission sources to improve accuracy. This research has significant implications for the development of energy-efficient Wi-Fi sensing solutions for a range of applications.

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RSS Localization Using Multistep Linearization in the Presence of Unknown Path Loss Exponent

Cited by 14 publications

References 21 publications

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

Ship Fire Detection Based on an Improved YOLO Algorithm with a Lightweight Convolutional Neural Network Model

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

Towards Energy Efficient Wireless Sensing by Leveraging Ambient Wi-Fi Traffic

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