Displaying publications 1 - 20 of 706 in total

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  1. Elgaud MM, Zan MSD, Abushagur AAG, Hamzah AE, Mokhtar MHH, Arsad N, et al.
    Sensors (Basel), 2021 Jun 23;21(13).
    PMID: 34201845 DOI: 10.3390/s21134299
    For almost a half-decade, the unique autocorrelation properties of Golay complementary pairs (GCP) have added a significant value to the key performance of conventional time-domain multiplexed fiber Bragg grating sensors (TDM-FBGs). However, the employment of the unipolar form of Golay coded TDM-FBG has suffered from several performance flaws, such as limited improvement of the signal-to-noise ratio (SNIR), noisy backgrounds, and distorted signals. Therefore, we propose and experimentally implement several digital filtering techniques to mitigate such limitations. Moving averages (MA), Savitzky-Golay (SG), and moving median (MM) filters were deployed to process the signals from two low reflectance FBG sensors located after around 16 km of fiber. The first part of the experiment discussed the sole deployment of Golay codes from 4 bits to 256 bits in the TDM-FBG sensor. As a result, the total SNIR of around 8.8 dB was experimentally confirmed for the longest 256-bit code. Furthermore, the individual deployment of MA, MM, and SG filters within the mentioned decoded sequences secured a further significant increase in SNIR of around 4, 3.5, and 3 dB, respectively. Thus, the deployment of the filtering technique alone resulted in at least four times faster measurement time (equivalent to 3 dB SNIR). Overall, the experimental analysis confirmed that MM outperformed the other two techniques in better signal shape, fastest signal transition time, comparable SNIR, and capability to maintain high spatial resolution.
  2. Tahir TF, Salhin A, Ab Ghani S
    Sensors (Basel), 2012 Nov 06;12(11):14968-82.
    PMID: 23202196 DOI: 10.3390/s121114968
    A flow injection analysis (FIA) incorporating a thiosemicarbazone-based coated wire electrode (CWE) was developed method for the determination of mercury(II). A 0.1 M KNO(3) carrier stream with pH between 1 and 5 and flow rate of 1 mL·min(-1) were used as optimum parameters. A linear plot within the concentration range of 5 × 10(-6)–0.1 M Hg(II), slope of 27.8 ± 1 mV per decade and correlation coefficient (R2) of 0.984 were obtained. The system was successfully applied for the determination of mercury(II) in dental amalgam solutions and spiked environmental water samples. Highly reproducible measurements with relative standard deviation (RSD < 1% (n = 3)) were obtained, giving a typical throughput of 30 samples·h(-1).
  3. Pirouzi G, Abu Osman NA, Oshkour AA, Ali S, Gholizadeh H, Abas WA
    Sensors (Basel), 2014;14(9):16754-65.
    PMID: 25207872 DOI: 10.3390/s140916754
    The suspension system and socket fitting of artificial limbs have major roles and vital effects on the comfort, mobility, and satisfaction of amputees. This paper introduces a new pneumatic suspension system that overcomes the drawbacks of current suspension systems in donning and doffing, change in volume during daily activities, and pressure distribution in the socket-stump interface. An air pneumatic suspension system (APSS) for total-contact sockets was designed and developed. Pistoning and pressure distribution in the socket-stump interface were tested for the new APSS. More than 95% of the area between each prosthetic socket and liner was measured using a Tekscan F-Scan pressure measurement which has developed matrix-based pressure sensing systems. The variance in pressure around the stump was 8.76 kPa. APSS exhibits less pressure concentration around the stump, improved pressure distribution, easy donning and doffing, adjustability to remain fitted to the socket during daily activities, and more adaptability to the changes in stump volume. The volume changes were adjusted by utility of air pressure sensor. The vertical displacement point and reliability of suspension were assessed using a photographic method. The optimum pressure in every level of loading weight was 55 kPa, and the maximum displacement was 6 mm when 90 N of weight was loaded.
  4. Mahdin H, Abawajy J
    Sensors (Basel), 2011;11(10):9863-77.
    PMID: 22163730 DOI: 10.3390/s111009863
    Radio frequency identification (RFID) systems are emerging as the primary object identification mechanism, especially in supply chain management. However, RFID naturally generates a large amount of duplicate readings. Removing these duplicates from the RFID data stream is paramount as it does not contribute new information to the system and wastes system resources. Existing approaches to deal with this problem cannot fulfill the real time demands to process the massive RFID data stream. We propose a data filtering approach that efficiently detects and removes duplicate readings from RFID data streams. Experimental results show that the proposed approach offers a significant improvement as compared to the existing approaches.
  5. You KY, Mun HK, You LL, Salleh J, Abbas Z
    Sensors (Basel), 2013;13(3):3652-63.
    PMID: 23493127 DOI: 10.3390/s130303652
    A moisture detection of single rice grains using a slim and small open-ended coaxial probe is presented. The coaxial probe is suitable for the nondestructive measurement of moisture values in the rice grains ranging from from 9.5% to 26%. Empirical polynomial models are developed to predict the gravimetric moisture content of rice based on measured reflection coefficients using a vector network analyzer. The relationship between the reflection coefficient and relative permittivity were also created using a regression method and expressed in a polynomial model, whose model coefficients were obtained by fitting the data from Finite Element-based simulation. Besides, the designed single rice grain sample holder and experimental set-up were shown. The measurement of single rice grains in this study is more precise compared to the measurement in conventional bulk rice grains, as the random air gap present in the bulk rice grains is excluded.
  6. Al-Mishmish H, Akhayyat A, Rahim HA, Hammood DA, Ahmad RB, Abbasi QH
    Sensors (Basel), 2018 Oct 28;18(11).
    PMID: 30373314 DOI: 10.3390/s18113661
    Wireless Body Area Networks (WBANs) are single-hop network systems, where sensors gather the body's vital signs and send them directly to master nodes (MNs). The sensors are distributed in or on the body. Therefore, body posture, clothing, muscle movement, body temperature, and climatic conditions generally influence the quality of the wireless link between sensors and the destination. Hence, in some cases, single hop transmission ('direct transmission') is not sufficient to deliver the signals to the destination. Therefore, we propose an emergency-based cooperative communication protocol for WBAN, named Critical Data-based Incremental Cooperative Communication (CD-ICC), based on the IEEE 802.15.6 CSMA standard but assuming a lognormal shadowing channel model. In this paper, a complete study of a system model is inspected in the terms of the channel path loss, the successful transmission probability, and the outage probability. Then a mathematical model is derived for the proposed protocol, end-to-end delay, duty cycle, and average power consumption. A new back-off time is proposed within CD-ICC, which ensures the best relays cooperate in a distributed manner. The design objective of the CD-ICC is to reduce the end-to-end delay, the duty cycle, and the average power transmission. The simulation and numerical results presented here show that, under general conditions, CD-ICC can enhance network performance compared to direct transmission mode (DTM) IEEE 802.15.6 CSMA and benchmarking. To this end, we have shown that the power saving when using CD-ICC is 37.5% with respect to DTM IEEE 802.15.6 CSMA and 10% with respect to MI-ICC.
  7. Kadirgama K, Noor MM, Abd Alla AN
    Sensors (Basel), 2010;10(3):2054-63.
    PMID: 22294914 DOI: 10.3390/s100302054
    Metal cutting processes are important due to increased consumer demands for quality metal cutting related products (more precise tolerances and better product surface roughness) that has driven the metal cutting industry to continuously improve quality control of metal cutting processes. This paper presents optimum surface roughness by using milling mould aluminium alloys (AA6061-T6) with Response Ant Colony Optimization (RACO). The approach is based on Response Surface Method (RSM) and Ant Colony Optimization (ACO). The main objectives to find the optimized parameters and the most dominant variables (cutting speed, feedrate, axial depth and radial depth). The first order model indicates that the feedrate is the most significant factor affecting surface roughness.
  8. Yulkifli Y, Yandes WP, Isa IM, Hashim N, Ulianas A, Sharif SNM, et al.
    Sensors (Basel), 2023 Oct 10;23(20).
    PMID: 37896460 DOI: 10.3390/s23208366
    The fabrication of a zinc hydroxide nitrate-sodium dodecylsulfate bispyribac modified with multi-walled carbon nanotube (ZHN-SDS-BP/MWCNT) paste electrode for uric acid and bisphenol A detection was presented in this study. Electrochemical impedance spectroscopy, chronocoulometry, square-wave voltammetry, and cyclic voltammetry were all used to examine the electrocatalytic activities of modified paste electrodes. The modified electrode's sensitivity and selectivity have been considered in terms of the composition of the modifier in percentages, the types of supporting electrolytes used, the pH of the electrolyte, and square-wave voltammetry parameters like frequency, pulse size, and step increment. Square-wave voltammetry is performed by applying a small amplitude square-wave voltage to a scanning potential from -0.3 V to +1.0 V, demonstrating a quick response time and high sensitivity. The ZHN-SDS-BP/MWCNT sensor demonstrated a linear range for uric acid and bisphenol A from 5.0 µM to 0.7 mM, with a limit of detection of 0.4 µM and 0.8 µM, respectively, with good reproducibility, repeatability, and stability as well. The modified paste electrode was successfully used in the determination of uric acid and bisphenol A in samples of human urine and lake water.
  9. Mahdi RI, Gan WC, Abd Majid WH
    Sensors (Basel), 2014;14(10):19115-27.
    PMID: 25317763 DOI: 10.3390/s141019115
    Ferroelectric poly(vinylidene fluoride-trifluoroethylene) (P(VDF-TrFE)) copolymer 70/30 thin films are prepared by spin coating. The crystalline structure of these films is investigated by varying the annealing temperature from the ferroelectric phase to the paraelectric phase. A hot plate was used to produce a direct and an efficient annealing effect on the thin film. The dielectric, ferroelectric and pyroelectric properties of the P(VDF-TrFE) thin films are measured as a function of different annealing temperatures (80 to 140 °C). It was found that an annealing temperature of 100 °C (slightly above the Curie temperature, Tc) has induced a highly crystalline β phase with a rod-like crystal structure, as examined by X-ray. Such a crystal structure yields a high remanent polarization, Pr = 94 mC/m2, and pyroelectric constant, p = 24 μC/m2K. A higher annealing temperature exhibits an elongated needle-like crystal domain, resulting in a decrease in the crystalline structure and the functional electrical properties. This study revealed that highly crystalline P(VDF-TrFE) thin films could be induced at 100 °C by annealing the thin film with a simple and cheap method.
  10. Ramanjot, Mittal U, Wadhawan A, Singla J, Jhanjhi NZ, Ghoniem RM, et al.
    Sensors (Basel), 2023 May 15;23(10).
    PMID: 37430683 DOI: 10.3390/s23104769
    A significant majority of the population in India makes their living through agriculture. Different illnesses that develop due to changing weather patterns and are caused by pathogenic organisms impact the yields of diverse plant species. The present article analyzed some of the existing techniques in terms of data sources, pre-processing techniques, feature extraction techniques, data augmentation techniques, models utilized for detecting and classifying diseases that affect the plant, how the quality of images was enhanced, how overfitting of the model was reduced, and accuracy. The research papers for this study were selected using various keywords from peer-reviewed publications from various databases published between 2010 and 2022. A total of 182 papers were identified and reviewed for their direct relevance to plant disease detection and classification, of which 75 papers were selected for this review after exclusion based on the title, abstract, conclusion, and full text. Researchers will find this work to be a useful resource in recognizing the potential of various existing techniques through data-driven approaches while identifying plant diseases by enhancing system performance and accuracy.
  11. Lye MH, AlDahoul N, Abdul Karim H
    Sensors (Basel), 2023 Jul 30;23(15).
    PMID: 37571588 DOI: 10.3390/s23156804
    Vidos from a first-person or egocentric perspective offer a promising tool for recognizing various activities related to daily living. In the egocentric perspective, the video is obtained from a wearable camera, and this enables the capture of the person's activities in a consistent viewpoint. Recognition of activity using a wearable sensor is challenging due to various reasons, such as motion blur and large variations. The existing methods are based on extracting handcrafted features from video frames to represent the contents. These features are domain-dependent, where features that are suitable for a specific dataset may not be suitable for others. In this paper, we propose a novel solution to recognize daily living activities from a pre-segmented video clip. The pre-trained convolutional neural network (CNN) model VGG16 is used to extract visual features from sampled video frames and then aggregated by the proposed pooling scheme. The proposed solution combines appearance and motion features extracted from video frames and optical flow images, respectively. The methods of mean and max spatial pooling (MMSP) and max mean temporal pyramid (TPMM) pooling are proposed to compose the final video descriptor. The feature is applied to a linear support vector machine (SVM) to recognize the type of activities observed in the video clip. The evaluation of the proposed solution was performed on three public benchmark datasets. We performed studies to show the advantage of aggregating appearance and motion features for daily activity recognition. The results show that the proposed solution is promising for recognizing activities of daily living. Compared to several methods on three public datasets, the proposed MMSP-TPMM method produces higher classification performance in terms of accuracy (90.38% with LENA dataset, 75.37% with ADL dataset, 96.08% with FPPA dataset) and average per-class precision (AP) (58.42% with ADL dataset and 96.11% with FPPA dataset).
  12. Suhaimi SMI, Muhamad NA, Bashir N, Mohd Jamil MK, Abdul Rahman MN
    Sensors (Basel), 2022 Jan 18;22(3).
    PMID: 35161466 DOI: 10.3390/s22030722
    Flashover on transmission line insulators is one of the major causes of line outages due to contamination from the environment or ageing. Power utility companies practicing predictive maintenance are currently exploring novel non-contact methods to monitor insulator surface discharge activities to prevent flashover. This paper presents an investigation on the UV pulse signals detected using UV pulse sensor due to the discharges on the insulator surfaces under varying contamination levels and insulator ages. Unaged and naturally aged insulators (0 to >20 years) were artificially contaminated (none, light to heavy contamination). The electrical stresses on the insulator surfaces were varied to generate varying discharge intensity levels on the surfaces of the insulator. The DC and harmonic components of UV pulse signals detected during surface discharges were recorded and analysed. Results show a positive correlation between the discharge intensity level of contaminated and aged transmission insulators with the DC and harmonic components of the UV pulse signals. Furthermore, the study revealed that under dry insulator surface conditions, insulator ageing has a more profound effect during discharges than contamination level. The findings from this study suggest that the use of UV pulse sensors to monitor UV pulse signals emitted during insulator surface discharges can be another novel non-contact method of monitoring transmission line insulator surface conditions.
  13. Mohamad FS, Mat Zaid MH, Abdullah J, Zawawi RM, Lim HN, Sulaiman Y, et al.
    Sensors (Basel), 2017 Dec 02;17(12).
    PMID: 29207463 DOI: 10.3390/s17122789
    This article describes chemically modified polyaniline and graphene (PANI/GP) composite nanofibers prepared by self-assembly process using oxidative polymerization of aniline monomer and graphene in the presence of a solution containing poly(methyl vinyl ether-alt-maleic acid) (PMVEA). Characterization of the composite nanofibers was carried out by Fourier transform infrared (FTIR) and Raman spectroscopy, transmission electron microscopy (TEM) and scanning electron microscopy (SEM). SEM images revealed the size of the PANI nanofibers ranged from 90 to 360 nm in diameter and was greatly influenced by the proportion of PMVEA and graphene. The composite nanofibers with an immobilized DNA probe were used for the detection of Mycobacterium tuberculosis by using an electrochemical technique. A photochemical indicator, methylene blue (MB) was used to monitor the hybridization of target DNA by using differential pulse voltammetry (DPV) method. The detection range of DNA biosensor was obtained from of 10-6-10-9 M with the detection limit of 7.853 × 10-7 M under optimum conditions. The results show that the composite nanofibers have a great potential in a range of applications for DNA sensors.
  14. Chizari H, Hosseini M, Poston T, Razak SA, Abdullah AH
    Sensors (Basel), 2011;11(3):3163-76.
    PMID: 22163792 DOI: 10.3390/s110303163
    Sensing and communication coverage are among the most important trade-offs in Wireless Sensor Network (WSN) design. A minimum bound of sensing coverage is vital in scheduling, target tracking and redeployment phases, as well as providing communication coverage. Some methods measure the coverage as a percentage value, but detailed information has been missing. Two scenarios with equal coverage percentage may not have the same Quality of Coverage (QoC). In this paper, we propose a new coverage measurement method using Delaunay Triangulation (DT). This can provide the value for all coverage measurement tools. Moreover, it categorizes sensors as 'fat', 'healthy' or 'thin' to show the dense, optimal and scattered areas. It can also yield the largest empty area of sensors in the field. Simulation results show that the proposed DT method can achieve accurate coverage information, and provides many tools to compare QoC between different scenarios.
  15. Khan AS, Balan K, Javed Y, Tarmizi S, Abdullah J
    Sensors (Basel), 2019 Nov 14;19(22).
    PMID: 31739437 DOI: 10.3390/s19224954
    Vehicular ad hoc networks (VANET) are also known as intelligent transportation systems. VANET ensures timely and accurate communications between vehicle to vehicle (V2V) and vehicle to infrastructure (V2I) to improve road safety and enhance the efficiency of traffic flow. Due to its open wireless boundary and high mobility, VANET is vulnerable to malicious nodes that could gain access into the network and carry out serious medium access control (MAC) layer threats, such as denial of service (DoS) attacks, data modification attacks, impersonation attacks, Sybil attacks, and replay attacks. This could affect the network security and privacy, causing harm to the information exchange within the network by genuine nodes and increase fatal impacts on the road. Therefore, a novel secure trust-based architecture that utilizes blockchain technology has been proposed to increase security and privacy to mitigate the aforementioned MAC layer attacks. A series of experiment has been conducted using the Veins simulation tool to assess the performance of the proposed solution in the terms of packet delivery ratio (PDR), end-to-end delay, packet loss, transmission overhead, and computational cost.
  16. Muhammad A, Yusof NA, Hajian R, Abdullah J
    Sensors (Basel), 2016;16(1).
    PMID: 26805829 DOI: 10.3390/s16010056
    In this work, a novel electrochemical sensor was fabricated for determination of amoxicillin in bovine milk samples by decoration of carboxylated multi-walled carbon nanotubes (MWCNTs) with gold nanoparticles (AuNPs) using ethylenediamine (en) as a cross linker (AuNPs/en-MWCNTs). The constructed nanocomposite was homogenized in dimethylformamide and drop casted on screen printed electrode. Field emission scanning electron microscopy (FESEM), energy dispersive X-Ray (EDX), X-Ray diffraction (XRD) and cyclic voltammetry were used to characterize the synthesized nanocomposites. The results show that the synthesized nanocomposites induced a remarkable synergetic effect for the oxidation of amoxicillin. Effect of some parameters, including pH, buffer, scan rate, accumulation potential, accumulation time and amount of casted nanocomposites, on the sensitivity of fabricated sensor were optimized. Under the optimum conditions, there was two linear calibration ranges from 0.2-10 µM and 10-30 µM with equations of Ipa (µA) = 2.88C (µM) + 1.2017; r = 0.9939 and Ipa (µA) = 0.88C (µM) + 22.97; r = 0.9973, respectively. The limit of detection (LOD) and limit of quantitation (LOQ) were calculated as 0.015 µM and 0.149 µM, respectively. The fabricated electrochemical sensor was successfully applied for determination of Amoxicillin in bovine milk samples and all results compared with high performance liquid chromatography (HPLC) standard method.
  17. Lai CQ, Ibrahim H, Abd Hamid AI, Abdullah JM
    Sensors (Basel), 2020 Sep 14;20(18).
    PMID: 32937801 DOI: 10.3390/s20185234
    Traumatic brain injury (TBI) is one of the common injuries when the human head receives an impact due to an accident or fall and is one of the most frequently submitted insurance claims. However, it is often always misused when individuals attempt an insurance fraud claim by providing false medical conditions. Therefore, there is a need for an instant brain condition classification system. This study presents a novel classification architecture that can classify non-severe TBI patients and healthy subjects employing resting-state electroencephalogram (EEG) as the input, solving the immobility issue of the computed tomography (CT) scan and magnetic resonance imaging (MRI). The proposed architecture makes use of long short term memory (LSTM) and error-correcting output coding support vector machine (ECOC-SVM) to perform multiclass classification. The pre-processed EEG time series are supplied to the network by each time step, where important information from the previous time step will be remembered by the LSTM cell. Activations from the LSTM cell is used to train an ECOC-SVM. The temporal advantages of the EEG were amplified and able to achieve a classification accuracy of 100%. The proposed method was compared to existing works in the literature, and it is shown that the proposed method is superior in terms of classification accuracy, sensitivity, specificity, and precision.
  18. Behjati M, Mohd Noh AB, Alobaidy HAH, Zulkifley MA, Nordin R, Abdullah NF
    Sensors (Basel), 2021 Jul 26;21(15).
    PMID: 34372281 DOI: 10.3390/s21155044
    Currently, smart farming is considered an effective solution to enhance the productivity of farms; thereby, it has recently received broad interest from service providers to offer a wide range of applications, from pest identification to asset monitoring. Although the emergence of digital technologies, such as the Internet of Things (IoT) and low-power wide-area networks (LPWANs), has led to significant advances in the smart farming industry, farming operations still need more efficient solutions. On the other hand, the utilization of unmanned aerial vehicles (UAVs), also known as drones, is growing rapidly across many civil application domains. This paper aims to develop a farm monitoring system that incorporates UAV, LPWAN, and IoT technologies to transform the current farm management approach and aid farmers in obtaining actionable data from their farm operations. In this regard, an IoT-based water quality monitoring system was developed because water is an essential aspect in livestock development. Then, based on the Long-Range Wide-Area Network (LoRaWAN®) technology, a multi-channel LoRaWAN® gateway was developed and integrated into a vertical takeoff and landing drone to convey collected data from the sensors to the cloud for further analysis. In addition, to develop LoRaWAN®-based aerial communication, a series of measurements and simulations were performed under different configurations and scenarios. Finally, to enhance the efficiency of aerial-based data collection, the UAV path planning was optimized. Measurement results showed that the maximum achievable LoRa coverage when operating on-air via the drone is about 10 km, and the Longley-Rice irregular terrain model provides the most suitable path loss model for the scenario of large-scale farms, and a multi-channel gateway with a spreading factor of 12 provides the most reliable communication link at a high drone speed (up to 95 km/h). Simulation results showed that the developed system can overcome the coverage limitation of LoRaWAN® and it can establish a reliable communication link over large-scale wireless sensor networks. In addition, it was shown that by optimizing flight paths, aerial data collection could be performed in a much shorter time than industrial mission planning (up to four times in our case).
  19. Behjati M, Nordin R, Zulkifley MA, Abdullah NF
    Sensors (Basel), 2022 Nov 19;22(22).
    PMID: 36433554 DOI: 10.3390/s22228957
    This paper proposes an effective global path planning technique for cellular-connected UAVs to enhance the reliability of unmanned aerial vehicles' (UAVs) flights operating beyond the visual line of sight (BVLOS). Cellular networks are considered one of the leading enabler technologies to provide a ubiquitous and reliable communication link for UAVs. First, this paper investigates a reliable aerial zone based on an extensive aerial drive test in a 4G network within a suburban environment. Then, the path planning problem for the cellular-connected UAVs is formulated under communication link reliability and power consumption constraints. To provide a realistic optimization solution, all constraints of the optimization problem are defined based on real-world scenarios; in addition, the presence of static obstacles and no-fly zones is considered in the path planning problem. Two powerful intelligent optimization algorithms, the genetic algorithm (GA) and the particle swarm optimization (PSO) algorithm, are used to solve the defined optimization problem. Moreover, a combination of both algorithms, referred to as PSO-GA, is used to overcome the inherent shortcomings of the algorithms. The performances of the algorithms are compared under different scenarios in simulation environments. According to the statistical analysis of the aerial drive test, existing 4G base stations are able to provide reliable aerial coverage up to a radius of 500 m and a height of 85 m. The statistical analysis of the optimization results shows that PSO-GA is a more stable and effective algorithm to rapidly converge to a feasible solution for UAV path planning problems, with a far faster execution time compared with PSO and GA, about two times. To validate the performance of the proposed solution, the simulation results are compared with the real-world aerial drive test results. The results comparison proves the effectiveness of the proposed path planning method in suburban environments with 4G coverage. The proposed method can be extended by identifying the aerial link reliability of 5G networks to solve the UAV global path planning problem in the current 5G deployment.
  20. Ahmad MI, Ab Rahim MH, Nordin R, Mohamed F, Abu-Samah A, Abdullah NF
    Sensors (Basel), 2021 Nov 17;21(22).
    PMID: 34833705 DOI: 10.3390/s21227629
    As nuclear technology evolves, and continues to be used in various fields since its discovery less than a century ago, radiation safety has become a major concern to humans and the environment. Radiation monitoring plays a significant role in preventive radiological nuclear detection in nuclear facilities, hospitals, or in any activities associated with radioactive materials by acting as a tool to measure the risk of being exposed to radiation while reaping its benefit. Apart from in occupational settings, radiation monitoring is required in emergency responses to radiation incidents as well as outdoor radiation zones. Several radiation sensors have been developed, ranging from as simple as a Geiger-Muller counter to bulkier radiation systems such as the High Purity Germanium detector, with different functionality for use in different settings, but the inability to provide real-time data makes radiation monitoring activities less effective. The deployment of manned vehicles equipped with these radiation sensors reduces the scope of radiation monitoring operations significantly, but the safety of radiation monitoring operators is still compromised. Recently, the Internet of Things (IoT) technology has been introduced to the world and offered solutions to these limitations. This review elucidates a systematic understanding of the fundamental usage of the Internet of Drones for radiation monitoring purposes. The extension of essential functional blocks in IoT can be expanded across radiation monitoring industries, presenting several emerging research opportunities and challenges. This article offers a comprehensive review of the evolutionary application of IoT technology in nuclear and radiation monitoring. Finally, the security of the nuclear industry is discussed.
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