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LoRa is a long-range, low-power wireless communication technology widely used in Internet of Things (IoT) applications. However, its conventional implementation through Long Range Wide Area Network (LoRaWAN) presents operational constraints due to its centralized topology and reliance on gateways. To overcome these limitations, this work designs and validates a gateway-free mesh communication system that operates directly on commercially available commodity microcontrollers, implementing lightweight self-healing mechanisms suitable for resource-constrained devices. The system, based on ESP32 microcontrollers and LoRa modulation, adopts a mesh topology with custom mechanisms including neighbor-based routing, hop-by-hop acknowledgments (ACKs), and controlled retransmissions. Reliability is achieved through hop-by-hop acknowledgments, listen-before-talk (LBT) channel access, and duplicate suppression using alternate link triggering (ALT). A modular prototype was developed and tested under three scenarios such as ideal conditions, intermediate node failure, and extended urban deployment. Results showed robust performance, achieving a Packet Delivery Ratio (PDR), the percentage of successfully delivered DATA packets over those sent, of up to 95% in controlled environments and 75% under urban conditions. In the failure scenario, an average Packet Recovery Ratio (PRR), the proportion of lost packets successfully recovered through retransmissions, of 88.33% was achieved, validating the system’s self-healing capabilities. Each scenario was executed in five independent runs, with values calculated for both traffic directions and averaged. These findings confirm that a compact and fault-tolerant LoRa mesh network, operating without gateways, can be effectively implemented on commodity ESP32-S3 + SX1262 hardware.

This work presents the development and implementation of a remote UV radiation monitoring station using LoRaWAN technology at the Universidad de las Américas. The main objective was to establish a system capable of measuring UV radiation levels through solarimeters, ensuring the remote transmission of data to protect the health and safety of students and staff exposed to solar radiation. To achieve this, several activities were conducted, including analyzing the architecture and communication components of LoRaWAN technology, designing a prototype based on this architecture, implementing the prototype based on the proposed design, and conducting functional tests to validate the system’s operability. The system included the installation of a solarimeter and a receiver or gateway, configured to operate from 8 a.m. to 6 p.m. The data collected by the prototype were validated through comparisons with measurements from the environmental monitoring system of the Secretariat of Environment of the Metropolitan District of Quito, which allowed for the verification of the prototype’s reliability. With this system, it was possible to identify patterns of high UV radiation and calculate error percentages in comparison with reference systems.

This paper proposes two solutions based on angle diversity receivers (ADRs) to mitigate inter-cell interference (ICI) in underground mining visible light communication (VLC) systems, one of them is a novel approach. A realistic VLC system based on two underground mining scenarios, termed as mining roadway and mine working face, is developed and modeled. A channel model based on the direct component in line-of-sight (LoS) and reflections of non-line-of-sight (NLoS) links is considered, as well as thermal and shot noises. The design and mathematical models of a pyramid distribution and a new hemi-dodecahedral distribution are addressed in detail. The performances of these approaches, accompanied by signal combining schemes, are evaluated with the baseline of a single photo-diode in reception. Results show that the minimum lighting standards established in both scenarios are met. As expected, the root-mean-square delay spread decreases as the distance between the transmitters and receivers increases. Furthermore, the hemi-dodecahedron ADR in conjunction with the maximum ratio combining (MRC) scheme, presents the best performance in the evaluated VLC system, with a maximum user data rate of 250 Mbps in mining roadway and 120 Mbps in mine working face, received energy per bit/noise power of 32 dB and 23 dB, respectively, when the bit error rate corresponds to 10 − 4 , and finally, values of 120 dB in mining roadway and 118 dB in mine working face for signal-to-interference-plus-noise ratio are observed in a cumulative distribution function.

Underground Mining (UM) is a hostile industry that generally requires a wireless communication system as a cross-cutting axis for its optimal operation. Therefore, in the last five years, it has been shown that, in addition to radio-frequency-based communication links, wireless optical communications, such as Visible Light Communication (VLC), can be applied to UM environments. The application of VLC systems in underground mines, known as UM-VLC, must take into account the unique physical features of underground mines. Among the physical phenomena found in underground mines, the most important ones are the positioning of optical transmitters and receivers, irregular walls, shadowing, and a typical phenomenon found in tunnels known as scattering, which is caused by the atmosphere and dust particles. Consequently, it is necessary to use proper dust particle distribution models consistent with these scenarios to describe the scattering phenomenon in a coherent way in order to design realistic UM-VLC systems with better performance. Therefore, in this article, we present an in-depth study of the interaction of optical links with dust particles suspended in the UM environment and the atmosphere. In addition, we analytically derived a hemispherical 3D dust particle distribution model, along with its main statistical parameters. This analysis allows to develop a more realistic scattering channel component and presents an enhanced UM-VLC channel model. The performance of the proposed UM-VLC system is evaluated using computational numerical simulations following the IEEE 802.1.5.7 standard in terms of Channel Impulse Response (CIR), received power, Signal-to-Noise-Ratio (SNR), Root Mean Square (RMS) delay spread, and Bit Error Rate (BER). The results demonstrate that the hemispherical dust particle distribution model is more accurate and realistic in terms of the metrics evaluated compared to other models found in the literature. Furthermore, the performance of the UM-VLC system is negatively affected when the number of dust particles suspended in the environment increases.

Visiblelightcommunication (VLC) is an emerging technology that offers an alternative to traditional wireless communications systems. However, the technology presents limitations related to the impact of the receiver’s orientation, which can significantly impact its performance. To address this issue, VLC systems use diversity schemes, such as transmitter and receiver diversity. In this paper, we derive an analytical expression for the probability of maintaining a line-of-sight (LoS) link in an underground mining visible light communication (UM-VLC) system with a receiver embedded in an object, such as a helmet, by considering user mobility. We show that the angle of incidence depends on the distance from the source and derive the probability accordingly for single-input single-output (SISO), multiple-input single-output (MISO), and single-input multiple-output cases (SIMO). Our results show that the analytical results fit with the simulated results. Furthermore, the resulting probabilities show that the angular position of the receiver significantly affects the channel’s quality, with the optimal position dependent on the field-of-view characteristics. These findings can provide an appropriate framework for receiver and transmitter diversity design through analytical expression.

This article proposes a novel method for detecting coronavirus disease 2019 (COVID-19) in an underground channel using visible light communication (VLC) and machine learning (ML). We present mathematical models of COVID-19 Deoxyribose Nucleic Acid (DNA) gene transfer in regular square constellations using a CSK/QAM-based VLC system. ML algorithms are used to classify the bands present in each electrophoresis sample according to whether the band corresponds to a positive, negative, or ladder sample during the search for the optimal model. Complexity studies reveal that the square constellation N=22i×22i,(i=3) yields a greater profit. Performance studies indicate that, for BER = 10−3, there are gains of −10 [dB], −3 [dB], 3 [dB], and 5 [dB] for N=22i×22i,(i=0,1,2,3), respectively. Based on a total of 630 COVID-19 samples, the best model is shown to be XGBoots, which demonstrated an accuracy of 96.03%, greater than that of the other models, and a recall of 99% for positive values.

Multiple simultaneous sound source localization (SSL) is one of the most important applications in the speech signal processing. The one-step algorithms with the advantage of low computational complexity (and low accuracy), and the two-step methods with high accuracy (and high computational complexity) are proposed for multiple SSL. In this article, a combination of one-step-based method based on the generalized eigenvalue decomposition (GEVD), and a two-step-based method based on the adaptive generalized cross-correlation (GCC) by using the phase transform/maximum likelihood (PHAT/ML) filters along with a novel T-shaped circular distributed microphone array (TCDMA) is proposed for 3D multiple simultaneous SSL. In addition, the low computational complexity advantage of the GCC algorithm is considered in combination with the high accuracy of the GEVD method by using the distributed microphone array to eliminate spatial aliasing and thus obtain more appropriate information. The proposed T-shaped circular distributed microphone array-based adaptive GEVD and GCC-PHAT/ML algorithms (TCDMA-AGGPM) is compared with hierarchical grid refinement (HiGRID), temporal extension of multiple response model of sparse Bayesian learning with spherical harmonic (SH) extension (SH-TMSBL), sound field morphological component analysis (SF-MCA), and time-frequency mixture weight Bayesian nonparametric acoustical holography beamforming (TF-MW-BNP-AHB) methods based on the mean absolute estimation error (MAEE) criteria in noisy and reverberant environments on simulated and real data. The superiority of the proposed method is presented by showing the high accuracy and low computational complexity for 3D multiple simultaneous SSL.

Epilepsy affects millions worldwide, making timely seizure detection crucial for effective treatment and enhanced well-being. Electroencephalogram (EEG) analysis offers a non-intrusive solution, but its visual interpretation is prone to errors and requires a lot of time. Many existing works focus solely on achieving competitive levels of accuracy without considering processing speed or the computational complexity of their models. This study aimed to develop an automated technique for identifying epileptic seizures in EEG data through analysis methods. The efforts have been primarily focused on achieving high accuracy results by operating exclusively within a narrow frequency band of the signal, while also aiming to minimize computational complexity. In this article, a new automated approach is presented for seizure detection by combining signal processing and machine learning techniques. The proposed method comprises four stages: (1) Preprocessing: Savitzky–Golay filter to remove the background noise. (2) Decomposition: discrete wavelet transform (DWT) to extract spontaneous alpha and beta frequency bands. (3) Feature extraction: six features (mean, standard deviation, skewness, kurtosis, energy, and entropy) are computed for each frequency band. (4) Classification: a support vector machine (SVM) method classifies signals as normal or containing a seizure. The method was assessed using two publicly available EEG datasets. For the alpha band, the highest achieved accuracy was 92.82%, and for the beta band it was 90.55%, which demonstrates adequate capability in both bands for accurate seizure detection. Furthermore, the obtained low computational cost suggests a potentially valuable application in real-time assessment scenarios. The obtained results indicate its capacity as a valuable instrument for diagnosing epilepsy and monitoring patients. Further research is necessary for clinical validation and potential real-time deployment.

This paper presents the design, implementation, and experimental evaluation of a visible light communication (VLC) system using a small solar panel with a custom signal-conditioning circuit as an optical receiver in a greenhouse environment. In this context, VLC can enables simultaneous lighting and data transmission, while solar panels can serve dual roles as energy harvesters and receivers. The work focuses on using mini solar panels, coupled with tailored circuitry, to address practical challenges in greenhouse communication. Theoretical modeling includes Lambertian emission and Beer–Lambert attenuation to characterize the optical channel, while accounting for the solar panel’s inherent low-pass filtering, which limits frequency response. Key performance metrics, including received optical power, bit error ratio (BER), and communication range, were evaluated under varying humidity conditions and at different transmitter-receiver distances. Experimental results using a modulated LED source showed that under high-humidity greenhouse conditions, the system achieves BER only at short ranges ( 0.3-0.4 m), increasing to - for 0.55-0.70 m. Beyond this range, link performance degrades due to receiver sensitivity limits. For reference, comparisons with reported photodetector results under similar greenhouse conditions highlighted that solar panels, while less sensitive at long distances, remain a viable option for robust, low-data-rate VLC applications. These findings offer insights into the design of photovoltaic-based VLC systems, highlighting opportunities to enhance receiver sensitivity and optimize dual-purpose operation.

This paper proposes a new system based on the Industrial Internet of Things (IIoT) for the monitoring of Mobile Health (m-Health) of workers in the underground mining industry. The proposed architecture uses a hybrid model in data transmission. Visible Light Communication (VLC) is used for downlink because of its narrow coverage, which aids in worker positioning. Radio frequency (RF) communication technology is used to send data for primary vital signs in the uplink, which is more efficient in transmission and is a viable solution according to the problem raised. The results obtained in terms of coverage and transmission for the downlink and uplink links show the feasibility of implementing the proposed system.