Search Results (88)

Dense Long Range (LoRa) networks suffer from packet loss when many end devices contend for the same unlicensed channel. Channel activity detection (CAD) can miss weak or cross-spreading-factor (cross-SF) transmissions, while a uniform carrier sense multiple access with collision avoidance (CSMA/CA) backoff rule ignores the different time-on-air (ToA) costs of SF7–SF12 packets. To address these two coupled problems, this paper proposes an interference-limit-aware CSMA protocol (ILA-CSMA). ILA-CSMA first combines CAD with an instantaneous received signal strength indicator (RSSI) test derived from the residual interference tolerance of the selected spreading factor, and then scales the contention window according to normalized ToA. The protocol is implemented in the Framework for LoRa (FLoRa), an OMNeT++-based LoRa network simulator, and is evaluated for networks with 100–2000 nodes. Compared with Pure ALOHA, Slotted ALOHA, standard CSMA/CA, and two ablation variants, ILA-CSMA improves dense-network access by jointly reducing hidden collisions and airtime imbalance. In the 2000-node case, it increases the packet delivery ratio (PDR) by about 20 percentage points relative to standard CSMA/CA, keeps the Jain fairness index (JFI) above the 0.85 reference line, reduces the energy consumed per successful packet to 22% of the standard CSMA/CA value, and reduces conditional average packet delay from 18.5 s to 8.2 s. These results show that interference-aware sensing and ToA-aware backoff can improve large-scale LoRa access under the evaluated simulation conditions. Full article

Accurate impact localization on spacecraft structural panels subjected to contact loading by on-orbit servicing robots is critical for real-time structural health monitoring (SHM), yet remains challenging due to heterogeneous elastic wave propagation in complex aluminum structures with stiffener ribs and bonded joints. Conventional Received Signal Strength Indicator (RSSI)-based weighted centroid methods rely on fixed path-loss exponents that cannot accommodate spatially varying wave attenuation, resulting in position-dependent localization errors that worsen significantly near structural discontinuities. This paper proposes a Crow Search Algorithm (CSA)-optimized adaptive weighted centroid algorithm using distributed Fiber Bragg Grating (FBG) sensors, featuring three principal innovations: (i) a novel FBG wavelength-shift-to-RSSI amplitude mapping derived from elastic wave attenuation theory, bridging optical fiber sensing with centroid localization; (ii) per-event online weight optimization via CSA that adapts sensor contributions to each individual impact’s strain-wave signature; and (iii) a multi-objective fitness function simultaneously optimizing localization accuracy, noise robustness, and temporal consistency. The proposed method is validated across 200 impact events distributed over five representative positions on a 1 m³ Al6061 satellite-like structure with 64 FBG sensors (8 × 8 grid, 125 mm pitch), under three Gaussian noise levels (σ = 1%, 3%, 5% of signal RMS), and benchmarked against classical weighted centroid (WC), PSO-WC, GA-WC, DE-WC, and GWO-WC using paired t-tests (p < 0.01). CSA-WC achieves a mean localization error of 4.63 mm—an 83.29% improvement over classical WC and the lowest error among all five compared algorithms—with an average computation time of 0.14 s per event, satisfying real-time monitoring requirements. Full article

Triaxial induction logging is particularly outstanding in identifying reservoir parameters including anisotropic strata, inclined boreholes and horizontal wells. However, the coplanar systems follow the traditional induction method of using a shielding coil to offset the direct coupling. This method results in severe horns in the coplanar coil response, which makes it more difficult to evaluate the water (oil) saturation of the reservoir. In this study, we used an analytic method to derive the magnetic field in a finite-thickness anisotropic medium by applying tangential continuity of the electric and magnetic field strengths, introducing the magnetic vector potential and Bessel functions. The response model influenced by different parameters was established. Under the same environmental parameters, the measurement range of the vertical and horizontal conductivities was larger than that of the traditional coplanar system. The apparent conductivity of the target layer was closer to the true value of the vertical conductivity in the layered strata, with an accuracy improvement of 78.9%. Furthermore, the improved coplanar system mechanism was revealed by analyzing the spatial distributions of eddy currents and the magnitudes of the magnetic fields generated. Finally, we designed an experimental device for a coplanar sensing system. Under the same parameters, the received signals of the improved coplanar system were greater than those of the traditional coplanar system in the air, which laid a foundation for the quantitative evaluation of stratigraphic anisotropy response characterization and inversion. Full article

Environmental monitoring is essential for smart agriculture, renewable energy assessment, and climate-aware farm management. However, deploying autonomous sensing platforms in rural environments remains challenging because of energy constraints, communication reliability, and real-time processing requirements. This paper presents a modular, solar-powered environmental monitoring platform integrating LTE-M communication and TinyML-enabled edge sensing. The proposed system adopts a dual-microcontroller architecture that combines an Arduino Nano 33 BLE for real-time sensor acquisition and edge processing with an Arduino MKR NB 1500 dedicated to low-power wide-area communication. The platform integrates temperature, humidity, atmospheric pressure, rainfall, wind, and light sensors within a scalable framework. Two monitoring stations were deployed in rural regions of Romania to evaluate communication robustness, sensing stability, and energy autonomy. Field results demonstrated reliable LTE-M connectivity (4306 received signal strength indicator [RSSI] samples; mean $-75.51$ dBm) and strong agreement with a regional weather station, with mean deviations of −0.71 °C (temperature), $4.98\%$ (humidity), and a stable pressure offset of $-9.58$ hPa attributable to altitude differences. Despite a total system cost of €315, the platform achieved measurement performance comparable to that of professional meteorological stations while maintaining long-term solar-powered operation. The proposed architecture provides a scalable and cost-effective solution for distributed smart agriculture and environmental monitoring applications. Full article

Existing docking methods for mobile robots rely on a LiDAR sensor or image processing using a camera. Although both demonstrate excellent performance in terms of sensing distance and spatial resolution, they are sensitive to environmental effects, such as illumination and occlusion, and are expensive. Some environments or conditions require low-power, low-cost novel docking solutions that are less sensitive to the environment. In this study, we propose a guidance and navigation solution for a mobile robot to dock into a docking station using the values of the angle of arrival and received signal strength indicator between the mobile robot and the docking station, measured via wireless communication based on Bluetooth low energy (BLE). This proposed algorithm is a LiDAR- and camera-free docking solution. The proposed algorithm is used to run an actual mobile robot and BLE transceiver hardware, and the obtained result is significantly close to the ground truth for docking. Full article

This paper presents an experimental evaluation of the connectivity reliability of a LoRaWAN (Long Range Wide Area Network), deployed in a rural area of Mozambique, focusing on the influence of distance and relative altitude between end nodes and the gateway. The absence of telecommunications and power infrastructure in the study region provided a realistic and challenging scenario to assess LoRaWAN’s feasibility as a low-cost, low-power solution for remote sensing in disconnected environments. Field trials were conducted using an Arduino-based node (with 2 dBi antenna) powered by a 2200 mAh power bank, with no GPS or cellular support. Data were collected at four georeferenced points along a 1 km path, capturing Received Signal Strength Indicator (RSSI), Signal-to-Noise Ratio (SNR), and Packet Delivery Rate (PDR). Results confirmed that both distance and terrain elevation strongly affect performance, with significantly degraded metrics when the end nodes were located at lower altitudes relative to the gateway. Despite operational constraints, such as the need for manual firmware resets and lack of real-time monitoring, the network consistently achieved PDR above 89% and remained operational autonomously for over 24 h. The study highlights the effectiveness of installing gateways on natural elevations to improve coverage and demonstrates that even with basic hardware, LoRaWAN (Low Power Wide Area Network), is a viable and scalable option for rural connectivity. These findings offer valuable empirical evidence to promote national digital inclusion policies and future LPWAN deployments. Full article

Indoor wireless networks face increasing challenges in maintaining stable coverage and performance, particularly with the widespread use of high-frequency Wi-Fi and growing demands from smart home devices. Traditional methods to improve signal quality, such as adding access points, often fall short in dynamic environments where user movement and physical obstructions affect signal behavior. In this work, we propose a system that leverages existing Internet of Things (IoT) devices to perform real-time user localization and network adaptation using fine-grained Channel State Information (CSI) and Received Signal Strength Indicator (RSSI) measurements. We deploy multiple ESP-32 microcontroller-based receivers in fixed positions to capture wireless signal characteristics and process them through a pipeline that includes filtering, segmentation, and feature extraction. Using supervised machine learning, we accurately predict the user’s location within a defined indoor grid. Our system achieves over $82\%$ accuracy in a realistic laboratory setting and shows improved performance when excluding redundant sensors. The results demonstrate the potential of communication-based sensing to enhance both user tracking and wireless connectivity without requiring additional infrastructure. Full article

Device-free localization and tracking (DFLT) has emerged as a promising technique for location-aware Internet-of-Things (IoT) applications. However, most existing DFLT systems based on narrowband sensing networks suffer from reduced accuracy in indoor environments due to the susceptibility of received signal strength (RSS) measurements to multipath interference. In this paper, we propose a real-time DFLT system leveraging ultra-wideband (UWB) sensors. The system estimates target-induced shadowing using two UWB RSS measurements, which are shown to be more resilient to multipath effects compared to their narrowband counterparts. To enable real-time tracking, we further design an efficient measurement protocol tailored for UWB networks. Field experiments conducted in both indoor and outdoor environments demonstrate that our UWB-based system significantly outperforms its traditional narrowband DFLT solutions in terms of accuracy and robustness. Full article

Received Signal Strength Indicator (RSSI) prediction is valuable for network planning and optimization as it helps determine the optimal placements of wireless access points and enables better coverage planning. It is also crucial for efficient handover management between cells or access points, reducing dropped connections and improving service quality. Additionally, RSSI prediction supports indoor positioning systems, power management optimization, and cost-efficient network deployment. Path loss models have historically served as the foundation for RSSI prediction, providing a theoretical framework for estimating signal strength degradation. However, modern machine learning approaches have emerged as a revolutionary solution for network optimization, providing more versatile and data-driven methods to enhance wireless network performance. In this paper, an adaptive machine learning framework integrating environmental sensing parameters such as temperature, relative humidity, barometric pressure, and particulate matter for RSSI prediction is proposed. Performance analysis reveals that RSSI values are influenced by environmental factors through complex, non-linear interactions, thereby challenging the conventional linear assumptions of traditional path loss models. The proposed model demonstrates improved predictive accuracy over the baseline, with relative increases in variance explained of 6.02% and 2.04% compared to the baseline model excluding and including environmental parameters, respectively. Additionally, the root mean squared error is reduced to 1.40 dB. These results demonstrate that cognitive methods incorporating environmental data can substantially enhance RSSI prediction accuracy in wireless communications. Full article

Fingerprinting-based positioning exploiting in two dimensions the spatial side information on fingerprints from adjacent positions relative to a target position is studied. The positioning is performed at the positioning device, utilizing as fingerprints the received signal strengths of downlink radio signals, collected using a two-dimensional sensor array. The motivation is to minimize the positioning error by transferring the complexity and cost from the infrastructure to the positioning device. The goal is to learn whether spatial side information on the fingerprints can minimize the positioning error. We provide a differentiation between fingerprinting in uplink and downlink, a classification of the positioning data aggregation domains, concepts, and a related literature review. We present three pattern-matching methods for estimating the position using spatial side information, two based on regression, implemented using feedforward neural networks, and one based on classification of the fractions of the positioning area, implemented using a convolutional neural network. Fingerprinting with and without spatial side information is benchmarked using the proposed pattern-matching methods in a system simulator based on Monte Carlo methods, generating synthetic fingerprints with an indoor radio channel model and calculating the positioning error. It is observed that for the given assumptions and the system considered, fingerprinting-based positioning with spatial side information substantially reduces the positioning error. Full article

An investigation of a non-invasive method to detect defects and localize excitations in metallic structures is presented. It is shown how signals generated by very sensitive piezo sensor assemblies, secured to the metallic elements, can allow for space localization of excitations and defects in the analyzed structures. The origin of the piezo excitations are acoustic modes generated by light percussive excitations whose strength is of the order of tenths of a newton and that provide piezo signal amplitudes of a few hundred millivolts. Tests of the detection scheme of the excitations are performed on steel ropes, iron pipes, and bars with lengths in the range of 1–6 m with the sensor output signal shaped in the form of a clean pulse. It is shown that the signals generated by the piezo assemblies, when adequately shaped, can feed the input of an RF transmitter, which in turn transfers information to a remote receiver whose readout allows for remotely analyzing information collected on the metallic elements. Considering the voltage amplitude of the signals (of the order of 300 mV) generated by the piezo sensors as a result of very light percussive excitations, the low power required for transmitting data, and the low cost of the sensing and transmitting assembly, it is conceivable that our devices could detect excitations generated even tens of kilometers away and allow for setting up an array of sensors for controlling in real time the status of pipe networks. Full article

This paper presents a passive displacement sensor based on the inductive coupling between a printed UHF RFID tag and a metallic strip. The sensor operates by exploiting variations in mutual inductive coupling, which modulate the tag impedance and transmission coefficient, thereby altering the backscattered signal strength and the maximum read range of the RFID tag. The performance of the sensor is validated through simulations and experiments, which demonstrate a sensitivity characterized by an approximately 9 dB reduction in the received signal strength indicator (RSSI) and a 2.3 m decrease in the read range within the first 12 mm of displacement. Furthermore, its potential for wearable applications is showcased through respiratory monitoring, where RSSI variations of approximately 5 dB are observed between the inspiration and expiration phases when positioned on the abdominal region of a volunteer. Thus, the proposed displacement sensing approach offers a cost-effective and battery-free solution for wearable applications with remote monitoring capabilities. Full article

In this paper, the idea of a radar based on orthogonal frequency division multiplexing (OFDM) is applied to 5G NR Positioning Reference Signals (PRS). This study demonstrates how the estimation of the communication channel using the PRS can be applied for the identification of objects moving near the 5G NR receiver. In this context, this refers to a 5G NR base station capable of detecting a high-speed train (HST). The anatomy of a 5G NR frame as a sequence of OFDM symbols is presented, and different PRS configurations are described. It is shown that spectral analysis of time-varying channel impulse response weights, estimated with the help of PRS pilots, can be used for the detection of transmitted signal reflections from moving vehicles and the calculation of their time and frequency/Doppler shifts. Different PRS configurations with varying time and frequency reference signal densities are tested in simulations. The peak-to-noise-floor ratio (PNFR) of the calculated radar range–velocity maps (RVM) is used for quantitative comparison of PRS-based radar scenarios. Additionally, different echo signal strengths are simulated while also checking various observation window lengths (FFT lengths). This study proves the practicality of using PRS pilots in remote sensing; however, it shows that the most dense configurations do not provide notable improvements, while also demanding considerably more resources. Full article

For applications such as home surveillance systems and assisted living for elderly care, sensing capabilities are essential for tasks such as locating, determining the approximate position of a person, or identifying the status of a person (static or moving), since the effects caused by the presence of people can be captured in the power received by signals in an infrastructure deployed for these purposes. Human interference in Received Signal Strength Indicator (RSSI) measurements between different pairs of wireless nodes can vary depending on whether the target is moving or static. To test these ideas, an experiment was conducted using four nodes equipped with the ZigBee protocol in each corner of an empty 6.9 m × 8.1 m × 3.05 m room. These nodes were configured as routers, communicating with a coordinator outside the room that instructed the nodes to send back their pairwise RSSI measurements. The coordinator was connected to a computer in order to log the measurements, as well as the time at which the measurements were generated. The code was run for every iteration of the experiment, whether the target was static, moving, or when the number of targets was increased to five. The data were then statistically analyzed to extract patterns and other target relational parameters. There was a correlation between the change in the pairwise RSSI and the path described by the target when moving through the room. The data presented by the results can aid algorithms for device-free localization and crowd classification, with a low infrastructure cost for both, and shed light on the relevant characteristics correlated with the path and crowd size in indoor settings. Full article

This paper briefly overviews and discusses the existing techniques using antennas for passive sensing, starting from the antenna operating principle and antenna structural design to different antenna-based sensing mechanisms. The effects of different electrical properties of the material used to design an antenna, such as conductivity, loss tangent, and resistivity, are discussed to illustrate the fundamental sensing mechanisms. Furthermore, the key parameters, such as operating frequency and antenna impedance, along with the factors affecting the sensing performance, are discussed. Overall, passive sensing using an antenna is mainly achieved by altering the reflected wave characteristics in terms of center frequency, return loss, phase, and received/reflected signal strength. The advantages and drawbacks of each technique are also discussed briefly. Given the increasing relevance, millimeter-wave antenna sensors and resonator sensors are also discussed with their applications and recent advancements. This paper primarily focuses on microstrip-based radiating structures and insights for further sensing performance improvement using passive antennas, which are outlined in this study. In addition, suggestions are made for the current scientific and technical challenges, and future directions are discussed. Full article