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Journal articles on the topic 'Magnetic anomaly detection sensor'

Author: Grafiati

Published: 21 April 2022

Last updated: 18 July 2025

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1

Hu, Bo Zhou, Meng Chun Pan, Peng Jiang, Wu Gang Tian, Jia Fei Hu, and Jing Hua Hu. "The Research on Magnetic Target Detection Technology Based on Wireless Sensor Network." Applied Mechanics and Materials 644-650 (September 2014): 1213–17. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1213.

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In the conditions of magnetic dipole model, this paper proposed forward a centroid localization algorithm on magnetic anomaly target based on wireless sensor network node which distribution are random and the improved the weighted centroid localization algorithm based on magnetic induction intensity. According to the fluctuation of magnetic field intensity which detected by magnetic sensors, that can detect the existence of magnetic anomaly target and its location. Established an experimental system of the wireless sensor network for magnetic anomaly detection whose core designs including the

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Tianhan, Yang, and Li Shizhong. "The Impact of Magnet Structures on Target Magnetic Anomaly Detection." Journal of Physics: Conference Series 2891, no. 12 (2024): 122009. https://doi.org/10.1088/1742-6596/2891/12/122009.

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Abstract To address the limitation of short detection ranges in conventional magnetic sensors, excitation magnetic fields can be used to enhance the target’s magnetic anomaly and thereby extend the detection range. By arranging permanent magnets to create excitation magnetic fields around the magnetic sensor, targets are magnetized, producing significant magnetic anomalies that can be detected by the sensor. Four different magnet configurations were tested in simulations to compare the response characteristics of the magnetic sensor using a typical iron rod as the target. Based on these simula

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Li, Hangcheng, Jiaming Luo, Jiajun Zhang, et al. "Determinants of Maximum Magnetic Anomaly Detection Distance." Sensors 24, no. 12 (2024): 4028. http://dx.doi.org/10.3390/s24124028.

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The maximum detection distance is usually the primary concern of magnetic anomaly detection (MAD). Intuition tells us that larger object size, stronger magnetization and finer measurement resolution guarantee a further detectable distance. However, the quantitative relationship between detection distance and the above determinants is seldom studied. In this work, unmanned aerial vehicle-based MAD field experiments are conducted on cargo vessels and NdFeB magnets as typical magnetic objects to give a set of visualized magnetic field flux density images. Isometric finite element models are estab

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Liu, Yong, Yu Fang Zhang, and Hong Yi. "The New Magnetic Survey Method for Underwater Pipeline Detection." Applied Mechanics and Materials 239-240 (December 2012): 338–43. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.338.

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Magnetic survey has been applied to detect underwater pipeline location, tracking and depth more and more nowadays. In order to detect such smaller targets, besides the application of high-precision magnetometer detector, an effective method especial for underwater pipeline is needed. This paper is to research the magnetic survey method for detecting the underwater pipeline. In terms of its magnetic anomaly characteristic, magnetic sensor array and magnetic anomaly gradient method which can eliminate geomagnetic diurnal variation are adopted to detect the location, tracking and depth of underw

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Ege, Yavuz, Mustafa Çoramık, Murat Kabadayı, et al. "Anomaly detection with low magnetic flux: A fluxgate sensor network application." Measurement 81 (March 2016): 43–56. http://dx.doi.org/10.1016/j.measurement.2015.12.004.

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Page, Brian R., Reeve Lambert, Nina Mahmoudian, David H. Newby, Elizabeth L. Foley, and Thomas W. Kornack. "Compact Quantum Magnetometer System on an Agile Underwater Glider." Sensors 21, no. 4 (2021): 1092. http://dx.doi.org/10.3390/s21041092.

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This paper presents results from the integration of a compact quantum magnetometer system and an agile underwater glider for magnetic survey. A highly maneuverable underwater glider, ROUGHIE, was customized to carry an increased payload and reduce the vehicle’s magnetic signature. A sensor suite composed of a vector and scalar magnetometer was mounted in an external boom at the rear of the vehicle. The combined system was deployed in a constrained pool environment to detect seeded magnetic targets and create a magnetic map of the test area. Presented is a systematic magnetic disturbance reduct

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Ge, Jiahao, Jinwu Xiang, and Daochun Li. "Integrated Low Electromagnetic Interference Design Method for Small, Fixed-Wing UAVs for Magnetic Anomaly Detection." Drones 8, no. 8 (2024): 347. http://dx.doi.org/10.3390/drones8080347.

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Unmanned aerial vehicles (UAVs) equipped with magnetic airborne detectors (MADs) represent a new combination for underground or undersea magnetic anomaly detection. The electromagnetic interference (EMI) generated by a UAV platform affects the acquisition of weak magnetic signals by the MADs, which brings unique conceptual design difficulties. This paper proposes a systematic and integrated low-EMI design method for small, fixed-wing UAVs. First, the EMI at the MAD is analyzed. Second, sensor layout optimization for a single UAV is carried out, and the criteria for the sensor layout are given.

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Yang, Ruiping, Hongpeng Wang, Huan Liu, Wang Luo, Jian Ge, and Haobin Dong. "A new digital single-axis fluxgate magnetometer according to the cobalt-based amorphous effects." Review of Scientific Instruments 93, no. 3 (2022): 035104. http://dx.doi.org/10.1063/5.0084376.

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Fluxgate sensors are currently widely used for weak magnetic field measurement because of their relatively great performance, such as resolution, power consumption, and measurement of vector magnetic fields directly. Since the analog fluxgate sensor has some drawbacks, e.g., it would be influenced by the noise of the analog circuit. Hence, in recent years, the analog circuit is gradually inclined to be realized by digital processing in which the software parameter adjustment is employed to replace the analog components, which can greatly improve the flexibility of the design. This paper propos

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Shen, Ying, Jiazeng Wang, Jiedong Shi, Shuxiang Zhao, and Junqi Gao. "Interpretation of signature waveform characteristics for magnetic anomaly detection using tunneling magnetoresistive sensor." Journal of Magnetism and Magnetic Materials 484 (August 2019): 164–71. http://dx.doi.org/10.1016/j.jmmm.2019.04.016.

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Naghibi, Javad, Kamyar Mehran, and Martin P. Foster. "High-Frequency Non-Invasive Magnetic Field-Based Condition Monitoring of SiC Power MOSFET Modules." Energies 14, no. 20 (2021): 6720. http://dx.doi.org/10.3390/en14206720.

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Current distribution anomaly can be used to indicate the onset of package-related failures modes in Silicon Carbide power MOSFET modules. In this paper, we propose to obtain the wire bond’s magnetic field profile using an array of Tunnel Magneto-Resistance (TMR) sensors, and characterise the small changes in the current density distribution to find the onset of the wire bond degradation processes, including wire bond lift-off, wire bond cracking, and wire bond fracture. We propose a novel condition monitoring technique where a non-galvanic high-bandwidth sensing and a reliability model monitor

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Parlak, Berke Ogulcan, and Huseyin Ayhan Yavasoglu. "A Comprehensive Analysis of In-Line Inspection Tools and Technologies for Steel Oil and Gas Pipelines." Sustainability 15, no. 3 (2023): 2783. http://dx.doi.org/10.3390/su15032783.

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The transportation of oil and gas through pipelines is an integral aspect of the global energy infrastructure. It is crucial to ensure the safety and integrity of these pipelines, and one way to do so is by utilizing an inspection tool called a smart pig. This paper reviews various smart pigs used in steel oil and gas pipelines and classifies them according to pipeline structure, anomaly-detection capability, working principles, and application areas. The advantages and limitations of each sensor technology that can be used with the smart pig for in-line inspection (ILI) are discussed. In this

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You, Xiuzhi, Junqian Zhang, Bingyang Chen, et al. "Magnetic Moment Estimation Algorithm Based on Convolutional Neural Network." Applied Sciences 15, no. 5 (2025): 2653. https://doi.org/10.3390/app15052653.

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Estimating the magnetic moment of a magnetic target is a key aspect of magnetic anomaly detection when accurately locating the target. Current methods primarily rely on vector magnetic field signals or gradient signals, which face challenges such as steering errors and the high costs associated with sensor arrays. This paper proposes a magnetic moment estimation algorithm that combines a scalar magnetic field sensor and the three components of the local geomagnetic field with a convolutional neural network (CNN). The simulation results demonstrate that the proposed algorithm performs well in n

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Fang, Xinwei, and Iain Bate. "An Improved Sensor Calibration with Anomaly Detection and Removal." Sensors and Actuators B: Chemical 307 (March 2020): 127428. http://dx.doi.org/10.1016/j.snb.2019.127428.

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Charles, Ogunkoya Olubunmi. "Advances in Magnetic Gradiometry for Aeromagnetic Surveys: Principles, Applications, and Future Directions – A Comprehensive Review." Asian Journal of Science, Technology, Engineering, and Art 3, no. 3 (2025): 743–66. https://doi.org/10.58578/ajstea.v3i3.5603.

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Magnetic gradiometry has revolutionized aeromagnetic surveys, offering high-resolution mapping of subsurface structures and mineral deposits. This review explores the principles, instrumentation, data processing methods, and applications of the technique in geophysical exploration. Recent advancements in sensor technology, particularly the development of superconducting quantum interference devices (SQUIDs), have facilitated the implementation of full tensor magnetic gradiometry (FTMG), enabling higher-resolution subsurface characterization. The integration of these systems with unmanned aeria

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Lytvyn, V. V., I. R. Peleshchak, V. A. Vysotska, V. M. Motyka, and R. M. Peleshchak. "A Passive Approach to Mine Detection and Classification Utilizing Magnetic Fields and Convolutional Neural Networks." Bionics of Intelligence 1, no. 100 (2024): 8–14. http://dx.doi.org/10.30837/bi.2024.1(100).02.

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Active detectors are widely used today for mine detection, providing high accuracy. However, their operating principle can pose a danger, as it has the potential to trigger the detonation of hidden mines. This study introduces an innovative method for detecting and classifying mines composed of different materials (metallic, semi-metallic, plastic) through the use of a convolutional neural network. The network analyzes anomalies derived from a uniquely designed feature matrix, achieving notable accuracy in classification. The classification process is based on a feature input vector that inclu

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Mozumder, Md Ariful Islam, Tagne Poupi Theodore Armand, Rashadul Islam Sumon, Shah Muhammad Imtiyaj Uddin, and Hee-Cheol Kim. "Automated Pipeline for Robust Cat Activity Detection Based on Deep Learning and Wearable Sensor Data." Sensors 24, no. 23 (2024): 7436. http://dx.doi.org/10.3390/s24237436.

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The health, safety, and well-being of household pets such as cats has become a challenging task in previous years. To estimate a cat’s behavior, objective observations of both the frequency and variability of specific behavior traits are required, which might be difficult to come by in a cat’s ordinary life. There is very little research on cat activity and cat disease analysis based on real-time data. Although previous studies have made progress, several key questions still need addressing: What types of data are best suited for accurately detecting activity patterns? Where should sensors be

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Noriega, Gerardo. "UAV-based magnetometry — Practical considerations, performance measures, and application to magnetic anomaly detection." Leading Edge 41, no. 7 (2022): 472–80. http://dx.doi.org/10.1190/tle41070472.1.

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Interest in ubiquitous low-cost unmanned aerial vehicles (UAVs) for use in aeromagnetic surveying has grown dramatically over the past decade. While their appeal is alluring, caution is called for as high-quality airborne magnetometry requires diligent system design and performance qualification. This paper discusses considerations and trade-offs in UAV-based magnetometry, standard measures to qualify performance, and application to magnetic anomaly detection (MAD). The apparent simplicity of towed-bird installations needs careful consideration. Logistical complexities, stability, and safety i

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Grillo, Luis O. S., Carlos A. C. Wengerkievicz, Nelson J. Batistela, Patrick Kuo-Peng, and Luciano M. de Freitas. "A Method for Statistical Processing of Magnetic Field Sensor Signals for Non-Invasive Condition Monitoring of Synchronous Generators." Sensors 22, no. 22 (2022): 8631. http://dx.doi.org/10.3390/s22228631.

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Condition monitoring of synchronous generators through non-invasive methods is widely requested by maintenance teams for not interfering the machine operation. Among the techniques used, external magnetic field monitoring is a recent strategy with great potential for detecting incipient faults. In this context, this paper proposes the application of a simple strategy with low computational cost to process data of external magnetic field time derivative signals for the purposes of condition monitoring and fault detection in synchronous machines. The information of interest is extracted from cha

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Xu, Wei, Zhiyong Guo, Zhongxiang Liu, Peng Zhang, and Yanxi Zhou. "Optimization of magnetic anomaly detection with single-axis sensor for pig locating in low latitude areas." Petroleum 5, no. 4 (2019): 417–23. http://dx.doi.org/10.1016/j.petlm.2019.01.001.

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Pant, Bharat B., Lucky Withanawasam, Mike Bohlinger, Mark Larson, and Bruce W. Ohme. "High-Temperature Anisotropic Magnetoresistive (AMR) Sensors." Journal of Microelectronics and Electronic Packaging 12, no. 4 (2015): 205–11. http://dx.doi.org/10.4071/imaps.481.

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Magnetic field sensors are employed in downhole oil and gas well drilling applications for azimuth sensing, orientation/rotation sensing, and magnetic anomaly detection. As the wells get deeper there is demand from industry to increase the operating temperature from ~175°C to ~225°C and higher. We have extended the operating regimen of silicon-based anisotropic magnetoresistive sensors to higher temperatures to address this demand. The low-frequency minimum detectable field of these sensors monotonically increases with increasing temperature. At room temperature it is 2.2 μG/√Hz@1 Hz reaching

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Rudd, Jonathan, Glenn Chubak, Hugo Larnier, et al. "Commercial operation of a SQUID-based airborne magnetic gradiometer." Leading Edge 41, no. 7 (2022): 486–92. http://dx.doi.org/10.1190/tle41070486.1.

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Gradient measurement of the magnetic field vector, especially full-tensor magnetic gradiometers (FTMGs), provides various advantages over gradient components derived from measurements of the total magnetic intensity (TMI). These advantages include higher spatial resolution, directional information, and thus more detailed anomaly delineation and a significantly better-constrained solution space for magnetic inversion and interpretation. However, the airborne application of FTMG instruments requires exceptionally high sensor resolution and low levels of motion noise to maximize these advantages

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Huang, Hoaping, and I. J. Won. "Automated anomaly picking from broadband electromagnetic data in an unexploded ordnance (UXO) survey." GEOPHYSICS 68, no. 6 (2003): 1870–76. http://dx.doi.org/10.1190/1.1635039.

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We present automated anomaly‐picking methods for detecting unexploded ordnance (UXO) from broadband electromagnetic (EM) data. Using data consisting of in‐phase and quadrature responses at multiple (typically 10) frequencies, a detector function attempts to detect all metal objects but to suppress false alarms caused by geology, variations in sensor height, and sensor motions in the earth's magnetic field. Promising detector functions considered here are (1) the sum of all quadrature responses, Qsum, (2) the sum of all differences among the in‐phase or quadrature components, Ispread or Qspread

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Pant, Bharat B., Lucky Withanawasam, Mike Bohlinger, Mark Larson, and Bruce W. Ohme. "High Temperature Anisotropic Magnetoresistive (AMR) Sensors." Additional Conferences (Device Packaging, HiTEC, HiTEN, and CICMT) 2015, HiTEN (2015): 000236–43. http://dx.doi.org/10.4071/hiten-session7-paper7_1.

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Magnetic field sensors are employed in down-hole oil, gas, and geothermal well-drilling applications for azimuth sensing, orientation/rotation sensing, and magnetic anomaly detection. Key requirements of these applications include high measurement accuracy in the near-DC frequency regime, high-operating temperatures, high mechanical shock and vibration, and severe size constraints. Silicon manufacturing processes enable the development of rugged components with small size compatible with assembly processes used for adjacent electronics in hermetically sealed hybrid and/or ceramic packages. Sil

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Siva Kumar, A., S. Raja, N. Pritha, Havaldar Raviraj, R. Babitha Lincy, and J. Jency Rubia. "An adaptive transformer model for anomaly detection in wireless sensor networks in real-time." Measurement: Sensors 25 (February 2023): 100625. http://dx.doi.org/10.1016/j.measen.2022.100625.

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Li, Linfeng, Weidong Liu, Le Li, Huifeng Jiao, Junqi Qu, and Gongwu Sun. "Compensation of Optical Pump Magnetometer Using the Improved Mayfly Optimization Algorithm." Journal of Marine Science and Engineering 10, no. 12 (2022): 1982. http://dx.doi.org/10.3390/jmse10121982.

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In order to solve the problem that the cesium optical pump magnetometer is disturbed by the carrier’s interference magnetic field during magnetic field anomaly detection, an interference magnetic field compensation method based on an improved mayfly optimization algorithm (IMOA) was proposed in this paper. First, by combining the measurement results of the attitude sensor with the geomagnetic inclination and magnetic declination in the locality, the measurement results of the optical pump magnetometer can be decomposed into the component values under the three axes of the carrier coordinate sy

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LAHAIE, PIERRE, J. R. SIMARD, J. MCFEE, et al. "SPECTRAL PROCESSING OF LASER-INDUCED FLUORESCENCE FROM THREATENING BIOLOGICAL AEROSOLS." International Journal of High Speed Electronics and Systems 18, no. 02 (2008): 429–43. http://dx.doi.org/10.1142/s0129156408005461.

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The stand-off detection classification by laser induced fluorescence is the objective of the Biosense project. The sensor will perform the monitoring of a defined area around its location using an elastic lidar detector for particles cloud. The detection of cloud will trigger fluorescence probing of the cloud. To perform this task the area fluorescence background will be monitored in order to evaluate if a return signal changed. Using a simple signal model built with experimental data, we designed a detection and monitoring procedure for the fluorescence at a single location. Signal simulation

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Ashwini B. Abhale and S. S. Manivannan. "Supervised Machine Learning Classification Algorithmic Approach for Finding Anomaly Type of Intrusion Detection in Wireless Sensor Network." Optical Memory and Neural Networks 29, no. 3 (2020): 244–56. http://dx.doi.org/10.3103/s1060992x20030029.

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Kang, Chong, Liming Fan, Quan Zheng, Xiyuan Kang, Jian Zhou, and Xiaojun Zhang. "Experimental Study on the Localization of Moving Object by Total Geomagnetic Field." Journal of Sensors 2017 (2017): 1–7. http://dx.doi.org/10.1155/2017/7948930.

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In the method of target localization based on magnetic anomalies, the scheme of vector field localization and experimental research are significant. Because more information of magnetic field can be measured by vector sensors, the position of the target can be directly calculated by the equations. However, the vector magnetic anomaly generated by the target is difficult to measure. And the detection range is small due to the low sensitivity of vector sensors. A method for target localization based on the total geomagnetic field is proposed. Its advantages are that the measurement of total magn

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MANOLAKIS, DIMITRIS, MICHAEL ROSSACCI, ERIN O'DONNELL, and FRANCIS M. D'AMICO. "SIGNAL PROCESSING ALGORITHMS FOR STARING SINGLE PIXEL HYPERSPECTRAL SENSORS." International Journal of High Speed Electronics and Systems 18, no. 03 (2008): 661–73. http://dx.doi.org/10.1142/s0129156408005655.

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Remote sensing of chemical warfare agents (CWA) with stand-off hyperspectral sensors has a wide range of civilian and military applications. These sensors exploit the spectral changes in the ambient photon flux produced thermal emission or absorption after passage through a region containing the CWA cloud. In this work we focus on (a) staring single-pixel sensors that sample their field of view at regular intervals of time to produce a time series of spectra and (b) scanning single or multiple pixel sensors that sample their FOV as they scan. The main objective of signal processing algorithms

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Fang, Xinwei, and Iain Bate. "Corrigendum to “An improved sensor calibration with anomaly detection and removal” [Sens. Actuators, B 307 15 March (2020) 127428]." Sensors and Actuators B: Chemical 317 (August 2020): 128239. http://dx.doi.org/10.1016/j.snb.2020.128239.

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Fang, Xinwei, and Iain Bate. "Corrigendum to “An Improved Sensor Calibration with Anomaly Detection and Removal” [Sens. Actuators B: Chem. 307 (15 March) (2020) 127428]." Sensors and Actuators B: Chemical 320 (October 2020): 128207. http://dx.doi.org/10.1016/j.snb.2020.128207.

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Miklusis, Donatas, Vytautas Markevicius, Dangirutis Navikas, et al. "Erroneous Vehicle Velocity Estimation Correction Using Anisotropic Magnetoresistive (AMR) Sensors." Sensors 22, no. 21 (2022): 8269. http://dx.doi.org/10.3390/s22218269.

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Magnetic field sensors installed in the road infrastructure can be used for autonomous traffic flow parametrization. Although the main goal of such a measuring system is the recognition of the class of vehicle and classification, velocity is the essential parameter for further calculation and it must be estimated with high reliability. In-field test campaigns, during actual traffic conditions, showed that commonly accepted velocity estimation methods occasionally produce highly erroneous results. For anomaly detection, we propose a criterion and two different correction algorithms. Non-linear

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Chen, Ziyun, Rui Chen, Tingyu Deng, et al. "Magnetic Anomaly Detection Using Three-Axis Magnetoelectric Sensors Based on the Hybridization of Particle Swarm Optimization and Simulated Annealing Algorithm." IEEE Sensors Journal 22, no. 4 (2022): 3686–94. http://dx.doi.org/10.1109/jsen.2021.3139116.

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Circi, Simone, Cinzia Ingallina, Silvia Vista, et al. "A Multi-Methodological Protocol to Characterize PDO Olive Oils." Metabolites 8, no. 3 (2018): 43. http://dx.doi.org/10.3390/metabo8030043.

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An analytical approach including Panel Test, Isotope Ratio Mass Spectrometry (IRMS) and Nuclear Magnetic Resonance (NMR) spectroscopy was proposed to characterize Italian “Colline Pontine” PDO olive oils (40 samples) of two consecutive crop years. Our approach has evidenced the high quality of these olive oils. Only 6 of 40 olive oils samples were defined as “defective” by the official Panel Test due to the detection of negative sensory attributes. The low variability of isotopic data monitored by IRMS confirmed that the olive oil samples all came from a limited geographical area. NMR spectra

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Rasuli, Sistan, Kjell Buehler, Kerstin Witte-Bodnar, Volker Naumann, Stephan Schoenfelder, and Klemens Ilse. "Magnetic Field Analysis for State of Health Monitoring of PEM Electrolyzer Stacks." ECS Meeting Abstracts MA2024-02, no. 45 (2024): 3096. https://doi.org/10.1149/ma2024-02453096mtgabs.

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The expected increase in the number of installed PEM electrolyzers during their industrialization in the upcoming years highlights the need for suitable non-destructive diagnostic methods for state of health monitoring and anomaly detection to ensure reliability and durability while reducing lifetime costs. The inner electrical current density distribution significantly impacts the efficiency and lifetime of a PEM electrolyzer stack. During operation, various degradation phenomena and inhomogeneities, including inhomogeneous water and cooling management, starvation effects, contact pressure, a

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Zhang, Zhiliang, Feng Zhao, Renxian Gao, et al. "Rayleigh anomaly-enabled mode hybridization in gold nanohole arrays by scalable colloidal lithography for highly-sensitive biosensing." Nanophotonics 11, no. 3 (2022): 507–17. http://dx.doi.org/10.1515/nanoph-2021-0563.

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Abstract Plasmonic sensors exhibit tremendous potential to accomplish real-time, label-free, and high-sensitivity biosensing. Gold nanohole array (GNA) is one of the classic plasmonic nanostructures that can be readily fabricated and integrated into microfluidic platforms for a variety of applications. Even though GNA has been widely studied, new phenomena and applications are still emerging continuously expanding its capabilities. In this article, we demonstrated narrow-band high-order resonances enabled by Rayleigh anomaly in the nanohole arrays that are fabricated by scalable colloidal lith

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Bruno, Salvatore, Ionut Daniel Trifan, Lorenzo Vita, and Giuseppe Loprencipe. "Development of Low-Cost Monitoring and Assessment System for Cycle Paths Based on Raspberry Pi Technology." Infrastructures 10, no. 3 (2025): 50. https://doi.org/10.3390/infrastructures10030050.

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Promoting alternative modes of transportation such as cycling represents a valuable strategy to minimize environmental impacts, as confirmed in the main targets set out by the European Commission. In this regard, in cities throughout the world, there has been a significant increase in the construction of bicycle paths in recent years, requiring effective maintenance strategies to preserve their service levels. The continuous monitoring of road networks is required to ensure the timely scheduling of optimal maintenance activities. This involves regular inspections of the road surface, but there

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Kim, Jong-Min, and Jaiwook Baik. "Anomaly Detection in Sensor Data." Journal of Applied Reliability 18, no. 1 (2018): 20–32. http://dx.doi.org/10.33162/jar.2018.03.18.1.20.

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Rajasegarar, S., C. Leckie, and M. Palaniswami. "Anomaly detection in wireless sensor networks." IEEE Wireless Communications 15, no. 4 (2008): 34–40. http://dx.doi.org/10.1109/mwc.2008.4599219.

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Paschalidis, Ioannis Ch, and Yin Chen. "Statistical anomaly detection with sensor networks." ACM Transactions on Sensor Networks 7, no. 2 (2010): 1–23. http://dx.doi.org/10.1145/1824766.1824773.

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Haque, Shah, Mustafizur Rahman, and Syed Aziz. "Sensor Anomaly Detection in Wireless Sensor Networks for Healthcare." Sensors 15, no. 4 (2015): 8764–86. http://dx.doi.org/10.3390/s150408764.

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Samuel, Sneha. "Anomaly Detection in Ambient Air Quality." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (2022): 1560–66. http://dx.doi.org/10.22214/ijraset.2022.46844.

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Abstract: In smart cities there are systems which monitor the air pollution levels at different locations. Supervising the information given by the gas sensor devices may be a tedious task because the sensor data is simply too huge to investigate manually. Further because of electrical interference and environmental conditions, the sensors may show false values and trigger alarms. Just in case the sensor gives any anomalous values then the values should be eliminated from analysis and triggering of alarms should be stopped. The damaged sensors must get replaced as early as possible. This paper

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Bhuse, Vijay, and Ajay Gupta. "Anomaly intrusion detection in wireless sensor networks." Journal of High Speed Networks 15, no. 1 (2006): 33–51. https://doi.org/10.3233/hsn-2006-276.

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We propose lightweight methods to detect anomaly intrusions in wireless sensor networks (WSNs). The main idea is to reuse the already available system information that is generated at various layers of a network stack. To the best of our knowledge, this is the first such approach for anomaly intrusion detection in WSNs.

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Salem, Osman, Yaning Liu, and Ahmed Mehaoua. "Anomaly Detection in Medical Wireless Sensor Networks." Journal of Computing Science and Engineering 7, no. 4 (2013): 272–84. http://dx.doi.org/10.5626/jcse.2013.7.4.272.

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Cheng, Pu, and Minghua Zhu. "Lightweight Anomaly Detection for Wireless Sensor Networks." International Journal of Distributed Sensor Networks 11, no. 8 (2015): 653232. http://dx.doi.org/10.1155/2015/653232.

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Zhang, Rui, Ping Ji, Dinkar Mylaraswamy, Mani Srivastava, and Sadaf Zahedi. "Cooperative sensor anomaly detection using global information." Tsinghua Science and Technology 18, no. 3 (2013): 209–19. http://dx.doi.org/10.1109/tst.2013.6522580.

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DeMedeiros, Kyle, Abdeltawab Hendawi, and Marco Alvarez. "A Survey of AI-Based Anomaly Detection in IoT and Sensor Networks." Sensors 23, no. 3 (2023): 1352. http://dx.doi.org/10.3390/s23031352.

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Machine learning (ML) and deep learning (DL), in particular, are common tools for anomaly detection (AD). With the rapid increase in the number of Internet-connected devices, the growing desire for Internet of Things (IoT) devices in the home, on our person, and in our vehicles, and the transition to smart infrastructure and the Industrial IoT (IIoT), anomaly detection in these devices is critical. This paper is a survey of anomaly detection in sensor networks/the IoT. This paper defines what an anomaly is and surveys multiple sources based on those definitions. The goal of this survey was to

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Yang, Tao, Xuefeng Jiang, Wei Li, et al. "Cloud-edge collaborative data anomaly detection in industrial sensor networks." PLOS One 20, no. 6 (2025): e0324543. https://doi.org/10.1371/journal.pone.0324543.

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Industrial sensor networks exhibit heterogeneous, federated, large-scale, and intelligent characteristics due to the increasing number of Internet of Things (IoT) devices and different types of sensors. Efficient and accurate anomaly detection of sensor data is essential for guaranteeing the system’s operational reliability and security. However, existing research on sensor data anomaly detection for industrial sensor networks still has several inherent limitations. First, most detection models usually consider centralized detection. Thus, all sensor data have to be uploaded to the control cen

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An, Chunyan, Yingyi Liu, Qi Li, and Pengbo Si. "Weighted-Neighborhood-Information-Network-Enabled Anomaly Detection Method for Electronic Sensors and Sensor Networks." Electronics 13, no. 17 (2024): 3482. http://dx.doi.org/10.3390/electronics13173482.

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As electronic sensors and sensor networks advance, perception data are increasingly characterized by mixed attributes. Traditional anomaly detection methods predominantly focus on numerical attributes. In this paper, we introduce a weighted neighborhood information network (WNIN)-enabled anomaly detection method tailored for mixed-attribute data from electronic sensors and sensor networks. Firstly, we employ the analytic hierarchy process (AHP) to analyze the security of sensor networks, leveraging a hierarchical electronic sensor network model to construct a hierarchical perception security a

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Kim, Donghyun, Gian Antariksa, Melia Putri Handayani, Sangbong Lee, and Jihwan Lee. "Explainable Anomaly Detection Framework for Maritime Main Engine Sensor Data." Sensors 21, no. 15 (2021): 5200. http://dx.doi.org/10.3390/s21155200.

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In this study, we proposed a data-driven approach to the condition monitoring of the marine engine. Although several unsupervised methods in the maritime industry have existed, the common limitation was the interpretation of the anomaly; they do not explain why the model classifies specific data instances as an anomaly. This study combines explainable AI techniques with anomaly detection algorithm to overcome the limitation above. As an explainable AI method, this study adopts Shapley Additive exPlanations (SHAP), which is theoretically solid and compatible with any kind of machine learning al

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