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This study investigates the performance of Internet of Things (IoT)-based monitoring systems using a mobile hotspot and IoT sensors for temperature and humidity data transmission. The research is based on the IoT concept, which enables electronic devices to communicate and exchange data through internet networks without direct human intervention. System performance was evaluated using standard Quality of Service (QoS) parameters, including throughput, packet loss, delay, and jitter. The experimental setup utilized a NodeMCU ESP32 microcontroller and a DHT22 sensor, with measurements conducted at various transmission distances through wireless communication media. The objective was to determine the reliability of hotspot connectivity and sensor communication in supporting IoT applications. The results indicate that the optimal performance was achieved at a distance of 20 meters using a 40-lambda variation. Furthermore, the communication signal between the ESP32 device and the mobile hotspot remained detectable up to a maximum distance of 32 meters. These findings demonstrate the effectiveness of the proposed IoT system for environmental monitoring applications within specific transmission ranges.

The rapid growth of the Internet of Things (IoT) has driven numerous innovations in wireless communications that not only demand technical efficiency but also raise philosophical questions about the nature of scientific knowledge. One such innovation is Physical Layer Network Coding (PLNC), a communication technique that utilizes signal interference as a source of information to enhance system performance. This paper examines the philosophical dimensions of science within PLNC, focusing on three fundamental aspects: ontology, epistemology, and axiology. Ontologically, PLNC represents a new paradigm in wireless communication that reinterprets interference not merely as noise but as an opportunity. Epistemologically, knowledge of PLNC is derived through scientific methods such as mathematical modeling, experimentation, and simulation—yielding intersubjective and verifiable truths. Axiologically, PLNC holds practical value in terms of energy efficiency, data reliability, and contributions to the sustainability of IoT ecosystems, while also raising ethical considerations regarding privacy and information security. Thus, this study demonstrates that the development of PLNC cannot be separated from philosophical reflection, emphasizing the profound interconnection between technological advancement, scientific methodology, and human values.

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.

The old bridge structure, are subjects that must be monitored in bridge maintenance. Bridge detection supervision is very necessary to determine structural deformation caused by normal operations or environmental impacts such as temperature, humidity and heavy vehicle loads. Monitoring the structure as a whole also needs to be carried out after extreme conditions occur, such as an earthquake. Research on structural dynamics analysis of steel frame bridge conditions to determine the behavior of the structure. Vibration data is read using a wireless sensor network or accelerometer. The vibration mode signal obtained is processed using Fast Fourier Transform (FFT) analysis via MATLAB software, which will produce a graph of the relationship between the frequency domain and the time domain. Through this graph, the dynamic characteristics of the bridge structure can be analyzed, with several variations in the condition of the bridge structure.

The Message Queue Telementary Transport (MQTT) protocol is able to adjust the sending and receiving of messages to monitor in accordance with the user's preferences because the sending and receiving of messages is topic based on a specified topic, making it necessary to routinely monitor the condition of patients who have been diagnosed with heart problems from a distance. With the aim to perform a Quality of Service (QoS) analysis with throughput, delay, and packet loss parameters using Unshielded Twisted Pair internet transmission media (UTP) and Wireless, the goal of this research is to design and implement (MQTT) a heart rate monitoring device with an EKG module as a sensor and ESP32 as a microcontroller. On the Ubidots website, EKG signals are transmitted over the internet and shown in real time. QoS analysis is performed using the Wireshark application. Data was collected on two scenarios at intervals of 30 minutes, 1 hour, 2 hours, 5 hours, 8 hours, 12 hours, 18 hours, and 23 hours. The throughput, latency, and packet loss metrics used in this study's results cause different value variations; these are influenced by the weather, internet bandwidth, computer, and router specifications. According to testing, the tool is portable and has a 3000mAh battery, but it has the restriction that it can only be used with reliable internet and bandwidth.

Indoor positioning technology based on smartphones plays an important role in the current technological development context. Especially in applications such as warehouses, supermarkets, hospitals, or buildings. While the global positioning system (GNSS) is popular and effective outdoors, it has several limitations when operating in enclosed spaces, such as indoors, due to the complexity of these environments. Smartphones have many built-in sensors (such as light sensors, sound sensors, gyroscopes, accelerometers, and magnetic sensors) and support the connection of various types of wireless communication technologies such as Wi-Fi and Bluetooth. However, such sensors were not initially developed for positioning applications. This study addresses the positioning problem using the MUSIC technique in conjunction with the Time of Arrival (ToA) method. The effectiveness of the positioning solution is evaluated through the signal-to-noise ratio (SNR) index. The absolute error and squared error indices are evaluated through the cumulative distribution function (CDF) to indicate the effectiveness of the proposed solution. Additionally, we propose a Pedestrian Dead Reckoning method to determine a person's position in indoor environments continuously. Based on the segmentation of the moving process by turns, the direction measurements in each segment are processed using a Kalman filter, which is designed to enhance the results achieved by the system. We also discuss the challenges and some future research directions in the field of smartphone-based indoor positioning.

Wireless communication, in its infrastructure nature, faces many challenges such as fading, data coverage, and interference issues. Therefore, High-Fidelity or (Li-Fi) is utilized due to its ability to naturally provide high-density wireless data coverage in closure’s particularly helpful for application(s) in some areas while the radio interference conditions are concern. This article illustrates an advanced Li-Fi approach performing high-speed data transmission between two Personal Computers (PCs) utilizing the Arduino Nano-based technique. In the experimental phase, data is mainly used to be transmitted over red laser diode (630 nm) through (30 cm) in distance, a distance of 30 cm, achieving a high peak speed reach to about (512Bps). The proposed approach performance is computed by evaluating the most important and related metrics like Signal-to-Noise Ratio (SNR), Bit-Error-Rate (BER), and influence of throughput on input data over various light circumstance. The proposed approach mainly utilizes a keypad as a user input and two related detection models for both a solar cell and a photodetector in order to make a powerful comparison in terms of performance. the results showed that when the photodetector applies a higher-detection efficiency (via BER enhancement which reaches to 20% over solar-cell), the solar-cell clarify outstanding power and cost-activity. The mentioned findings are propped by elaborated statistical-analyses and MATLAB simulation to design, simulate and visualize the validate functionalities of the robustness and scalability properties of the proposed Li-Fi approach.

The electromagnetic field on high-frequency radio waves and its impact on signal transmission quality. High-frequency radio waves, used in wireless communication, can be affected by electromagnetic fields from various external sources, such as electronic equipment and building structures. This study analyzes the interaction between electromagnetic fields and radio waves, as well as their impact on transmission speed and signal range. The analysis results show that electromagnetic fields can cause distortion or interference in radio waves, affecting the quality of the received signal. This study provides deeper insights into the importance of managing electromagnetic fields to ensure stability and efficiency in high-frequency radio communication systems. The findings are expected to serve as a reference for the development of communication technology and the mitigation of electromagnetic interference on radio waves.

Objective: analyze the modulation scheme that can intelligently select the appropriate modulation model for service conditions to obtain a high Signal to Noise Ratio, as well as throughput efficiency on wireless networks through the DNN approach. Method: this study uses simulations with the Python language, through AI-Driven on BPSK, QPSK, 16-QAM, and 64-QAM modulation, to determine the SNR and Quality of Service (QoS) produced, both through conventional approaches and Deep Neuro Network (DNN). Researh Finding: AI-Driven modulation used for Cognitive Cellular Networks (CCN), through Deep Neuro Network designed to intelligently classify and select the appropriate modulation model to be applied, shows significant improvement in throughput efficiency, QoS and has the ability to adapt to the environment in dynamic networks. Conclussion: AI-Driven using Deep Neuro Network is able to dynamically adapt to determine the selected modulation model, according to the user's environmental conditions, increase spectrum efficiency and throughput, and increase SNR which can automatically increase the efficiency of network usage.

Data transmission efficiency is crucial in wireless sensor networks (WSNs), where limited battery life and signal reliability are significant concerns. This research explores various machine learning algorithms aimed at optimizing data transmission in WSNs, focusing on reducing energy consumption and enhancing network stability. Simulation results indicate marked improvements in efficiency, making WSNs more viable for long-term deployment across diverse environments.

Abstrak – Penelitian ini bertujuan untuk mengevaluasi signal wireless di SMP Negeri 10 Salatiga, dengan menggunakan Received Signal Strength Indicator (RSSI). Tujuan penelitian adalah untuk memahami distribusi kekuatan signal di sekitar lingkungan sekolah dan mengidentifikasi potensi perbaikan dalam infastruktur wireless. Metodologi melibatkan pengukuran RSSI pada lokasi strategis pengukuran nilai RSSI dilakukan dengan perhitungan otomatis menggunakan aplikasi Wi-Fi Analyzer, inSSIDer Home, dan Wi-Fi SNR. Perhitungan nilai RSSI juga dihitung dengan cara manual menggunakan rumus RSSI. Selain menghitung nilai RSSI juga ada Tahapan Interference Co-Channel dan pengukuran SNR kemudian data dianalisis untuk mengungkap pola dan variasi. Hasil penelitian menyoroti area dengan kekuatan signal rendah dan memberikan wawasan tentang efektivitas jaringan wireless. Temuan ini dapat mendukung perbaikan penempatan perangkat dan optimalisasi konektivitas. Kesimpulannya, penelitian ini memberikan kontribusi pada peningkatan pengalaman belajar berbasis teknologi informasi di SMP Negeri 10 Salatiga.

A network is a system in which computing devices are connected to each other and can share data and resources. To establish a connection, these network devices follow certain rules called communication protocols, both through physical and wireless technology. The performance level of a Wi-Fi network, for example inside a building, can be measured by how well users receive the Wi-Fi signal. The goal of this network is to establish timely and precise communication between users and data senders. A successful Internet connection often depends on receiving a strong and consistent signal, while a fluctuating or weak signal is undesirable. A commonly used approach to measure the average Wi-Fi signal level in a building without needing to understand every detail of the building layout is to use Single Slope Modeling (1SM). This model bases its calculations on the distance between the sending and receiving devices, so it can be used to analyze the performance of Wi-Fi networks, as is done in the case of educational institution buildings. Apart from that, in an effort to optimize network performance, a genetic algorithm was also created which was implemented using the PHP language. This algorithm is based on the principles of genetic algorithms to overcome optimization problems that may arise in the context of such networks.