Received Signal Strength Research Articles

Wireless sensor localization based on distance optimization and assistance by mobile anchor nodes: a novel algorithm.

Wireless sensor networks (WSNs) have wide applications in healthcare, environmental monitoring, and target tracking, relying on sensor nodes that are joined cooperatively. The research investigates localization algorithms for both target and node in WSNs to enhance accuracy. An innovative localization algorithm characterized as an asynchronous time-of-arrival (TOA) target is proposed by implementing a differential evolution algorithm. Unlike available approaches, the proposed algorithm employs the least squares criterion to represent signal-sending time as a function of the target position. The target node's coordinates are estimated by utilizing a differential evolution algorithm with reverse learning and adaptive redirection. A hybrid received signal strength (RSS)-TOA target localization algorithm is introduced, addressing the challenge of unknown transmission parameters. This algorithm simultaneously estimates transmitted power, path loss index, and target position by employing the RSS and TOA measurements. These proposed algorithms improve the accuracy and efficiency of wireless sensor localization, boosting performance in various WSN applications.

Research on dynamic positioning of navigation system using K nearest neighbor algorithm and bayesian fusion based on visible light communication

This study presents a novel Bayesian-K Nearest Neighbor (B-KNN) fusion algorithm aimed at mitigating dynamic positioning errors in navigation systems that utilize visible light communication. The proposed fusion methodology leverages a fingerprint database constructed from the received signal strength data acquired by a mobile receiver. Through the selection of the nearest navigation path points for clustering and the utilization of a Bayesian algorithm with Gaussian fitting, the posterior probability of each cluster is computed to facilitate the prediction of the location of navigation path points via fingerprint matching. The results indicate that 89.8% of simulations exhibit a dynamic positioning error below 1 m within a 20 m×5 m×4 m model, achieving a success rate of 89.8%. Comparative analysis reveals that the B-KNN fusion algorithm enhances dynamic positioning accuracy by 68.3% while reducing computational overhead by approximately 48.3% in contrast to conventional KNN algorithms. Experimental assessments demonstrate that in scenarios involving ambient light interference within a 2 m×1 m×1.5 m model, the majority (95%) of dynamic positioning errors exceed 9 cm, with a significant portion surpassing 10 cm. Conversely, under conditions devoid of ambient light interference, only 4% of errors exceed 9 cm, with 14% falling below 5 cm, indicative of a marked 39.81% improvement in accuracy.

Indoor positioning systems based on visible light communication

Indoor positioning systems (IPS) are crucial in the ever-expanding landscape of wireless communication systems. These systems can generally be classified into four categories based on the underlying positioning technologies. One of them is based on visible light technology using Light Emitting Diodes (LEDs). In this paper, we first present a simple transmission model from the sender to the receiver in a visible light system. Subsequently, we construct a higher-precision, cost-effective model based on the technology proposed by Epsilon. Next, we introduce the most commonly used fingerprint recognition method and triangulation method in visible light indoor positioning. Traditional fingerprint recognition methods have issues such as significant errors. Recent studies have proposed various improvements based on different problems. To address the problem of high position errors, new research introduces an accurate Visible Light Positioning (VLP) method based on location fingerprinting and meta-heuristic. The method presented in this paper addresses the issue of position errors being affected in special cases. A typical IPS is an indoor positioning system utilizing dual-tone multi-frequency (DTMF) technology. The system design involves arranging LED lamps in a rectangular grid shape and using a basic positioning unit. The research discussed in this paper also explores the calculation of channel gains and simulation results of positioning errors. In the proposed positioning system, the coarse position is obtained through the ID acquisition process. When the mobile unit receives one or more LED signals, the ID acquisition process is used to obtain the nearest LEDs ID based on the highest Received Signal Strength Indication (RSSI). Simulation results indicate an average positioning error of 18 millimeters at a Signal-to-Noise Ratio (SNR) of 10 dB. Through the results of our research, we conclude that the wide applicability of VLC technology makes it a cornerstone in many fields. In our daily lives, optical communication positioning technology continues to improve, promising broader applications in the future.

Proximity Sensor for Measuring Social Interaction in a School Environment.

Social interactions are characterized by being very diverse and changing over time. Understanding this diversity and dynamics, as well as their emerging patterns, is of great interest from social, health, and educational perspectives. The development of new devices has been made possible in recent years by advances in applied technology. This paper presents the design and development of a novel device composed of several sensors. Specifically, we propose a proximity sensor integrated by three devices: a Bluetooth sensor, a global positioning system (GPS) unit and an accelerometer. By means of this sensor it is possible to detect the presence of neighboring sensors in various configurations and operating conditions. Profiles based on the Received Signal Strength Indicator (RSSI) exhibit behavior consistent with that reported by empirical relationships. The present sensor is functional in detecting the proximity of other sensors and is thus useful for the identification of interactions between people in relevant contexts such as schools.

Enhanced Indoor Positioning Using RSSI and Time-Distributed Auto Encoder-Gated Recurrent Unit Model.

This study presents a novel approach to indoor positioning leveraging radio frequency identification (RFID) technology based on received signal strength indication (RSSI). The proposed methodology integrates Gaussian Kalman filtering for effective signal preprocessing and a time-distributed auto encoder-gated recurrent unit (TAE-GRU) model for precise location prediction. Addressing the prevalent challenges of low accuracy and extended localization times in current systems, the proposed method significantly enhances the preprocessing of RSSI data and effectively captures the temporal relationships inherent in the data. Experimental validation demonstrates that the proposed approach achieves a 75.9% improvement in localization accuracy over simple neural network methods and markedly enhances the speed of localization, thereby proving its practical applicability in real-world indoor localization scenarios.

Tag-free indoor fall detection using transformer network encoder and data fusion

This work presents a radio frequency identification (RFID)-based technique to detect falls in the elderly. The proposed RFID-based approach offers a practical and efficient alternative to wearables, which can be uncomfortable to wear and may negatively impact user experience. The system utilises strategically positioned passive ultra-high frequency (UHF) tag array, enabling unobtrusive monitoring of elderly individuals. This contactless solution queries battery-less tag and processes the received signal strength indicator (RSSI) and phase data. Leveraging the powerful data-fitting capabilities of a transformer model to take raw RSSI and phase data as input with minimal preprocessing, combined with data fusion, it significantly improves activity recognition and fall detection accuracy, achieving an average rate exceeding 96.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$96.5\\%$$\\end{document}. This performance surpasses existing methods such as convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM), demonstrating its reliability and potential for practical implementation. Additionally, the system maintains good accuracy beyond a 3-m range using minimal battery-less UHF tags and a single antenna, enhancing its practicality and cost-effectiveness.

A Proof of Concept Using BLE to Optimize Patients Turnaround Time (PTAT) in Health Care

In 2022, Malaysia's Ministry of Health (MOH) acknowledged that government hospitals continue to face challenges in delivering high-quality patient care. A significant issue is the absence of a Real-Time Patient Location Monitoring and Tracking Solution, which prevents doctors from efficiently tracking patients within medical facilities. This lack of real-time tracking contributes to a decline in service quality, creating an urgent need for optimization in patient monitoring and tracking solutions. Implementing an effective tracking system can significantly enhance patient care quality, improve resource utilization management, and boost staff productivity, ultimately saving costs, time, and resources for hospitals. One critical area for improvement is the Patient Turnaround Time (PTAT), which refers to the duration a patient spends being processed at a hospital before being discharged. PTAT is currently prolonged in many hospitals due to the complex processes involved and patients becoming untraceable while waiting for their turn. To address these challenges, we have developed an Internet of Things (IoT) solution utilizing Bluetooth Low Energy (BLE) technology to optimize PTAT. This solution enables hospital staff to track patient movements and localize them in real-time. By using wearable devices, the system measures the distance from patients to various Access Points (AP) spread throughout the hospital based on the dBm value. This paper presents comprehensive experimentation for the Proof of Concept (POC) and pilot testing of this BLE-based solution. The results demonstrate the accuracy of using the Received Signal Strength Indicator (RSSI) to optimize patient turnaround times in hospitals. By implementing this BLE solution, hospitals can reduce the time patients spend waiting for treatment, thereby improving overall patient satisfaction and hospital efficiency. The ability to monitor patient locations in real-time will ensure that patients receive timely care, reducing the likelihood of them wandering off and becoming untraceable. This technology represents a significant step forward in modernizing patient care and streamlining hospital operations in Malaysia.

A WKNN Indoor Fingerprint Localization Technique Based on Improved Discrimination Capability of RSS Similarity.

There are various indoor fingerprint localization techniques utilizing the similarity of received signal strength (RSS) to discriminate the similarity of positions. However, due to the varied states of different wireless access points (APs), each AP's contribution to RSS similarity varies, which affects the accuracy of localization. In our study, we analyzed several critical causes that affect APs' contribution, including APs' health states and APs' positions. Inspired by these insights, for a large-scale indoor space with ubiquitous APs, a threshold was set for all sample RSS to eliminate the abnormal APs dynamically, a correction quantity for each RSS was provided by the distance between the AP and the sample position to emphasize closer APs, and a priority weight was designed by RSS differences (RSSD) to further optimize the capability of fingerprint distances (FDs, the Euclidean distance of RSS) to discriminate physical distance (PDs, the Euclidean distance of positions). Integrating the above policies for the classical WKNN algorithm, a new indoor fingerprint localization technique is redefined, referred to as FDs' discrimination capability improvement WKNN (FDDC-WKNN). Our simulation results showed that the correlation and consistency between FDs and PDs are well improved, with the strong correlation increasing from 0 to 76% and the high consistency increasing from 26% to 99%, which confirms that the proposed policies can greatly enhance the discrimination capabilities of RSS similarity. We also found that abnormal APs can cause significant impact on FDs discrimination capability. Further, by implementing the FDDC-WKNN algorithm in experiments, we obtained the optimal K value in both the simulation scene and real library scene, under which the mean errors have been reduced from 2.2732 m to 1.2290 m and from 4.0489 m to 2.4320 m, respectively. In addition, compared to not using the FDDC-WKNN, the cumulative distribution function (CDF) of the localization errors curve converged faster and the error fluctuation was smaller, which demonstrates the FDDC-WKNN having stronger robustness and more stable localization performance.

Indoor unknown radio transmitter localization using improved RSSD and grey correlation degree

Abstract Accurate location of unknown radio transmitter (URT) is the key to secure wireless communication. Since the fingerprint positioning methods based on received signal strength difference (RSSD) can adapt to the diversity of transmitting power and frequency, RSSD has become a popular scheme for locating the unknown radio transmitter. However, the RSSD is obtained by subtracting the RSS from two different access points (APs), so the interference of noise on the RSS is inherited and amplified by the RSSD. Besides, the need for more APs to ensure positioning accuracy leads to an increase in hardware costs. In this paper, a RSSD-based fuzzy weight grey correlation degree positioning algorithm, called FUZZY-GREY, is proposed to reduce the interference of noise, save AP hardware cost and improve the positioning accuracy. Firstly, online RSSD vector is improved by using fuzzy weight to reduce the noise interference. Secondly, the RSSD-based grey correlation coefficient is designed to calculate the correlation degree of the corresponding RSSD and ensure data integrity. Finally, a RSSD-based grey correlation degree scheme combining with fuzzy weight is proposed to select optimal reference points (RPs). Simulation and experimental results show that the proposed algorithm has better positioning performance than weighted k-nearest neighbor (WKNN), maximum correlation coefficient estimation (MCORE), Naive Bayes and support vector machine (SVM) in the case of different selected K numbers, grid distances, noise levels and AP numbers.

Localizing Mobile Target using Kalman Filtering and Least Square Boosting Ensemble Learning based Approach

Trilateration-based target localization using received signal strengths (RSS) within wireless sensor networks (WSN) often results in inaccurate location estimates due to the considerable fluctuations in RSS measurements encountered in indoor environments. Enhancing the precision of RSS-based localization systems has been a primary area of interest in extensive research efforts. This study introduces two range-free algorithms, Ensemble Learning (EL) and EL+KF, which leverage RSS measurements for localization. In contrast to trilateration, the EL- based localization approach enables the direct estimation of target locations based on field measurements, eliminating the need for distance calculations. Notably, unlike other cutting-edge localization and tracking (L&T) scheme like the support vector regression (SVR), the LSBoost (Least Squares Boosting) EL based localization architecture can be trained very quickly using RSS measurements to determine the mobile target's position. Furthermore, the proposed EL-based localization scheme incorporates the Kalman filter (KF) to achieve additional refinement in target location estimates. To assess the localization effectiveness of these proposed algorithms in the presence of noisy RF channels and dynamic target motion models, rigorous simulations were conducted. Thanks to the robust generalization capabilities of EL, the simulation results reveal that the presented EL-based localization algorithms exhibit superior performance compared to trilateration and the SVR-based localization scheme, particularly in terms of indoor localization accuracy.

Efficient high‐fidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environments

AbstractWith the rapid development of wireless communication systems, particularly in the era of 5G and the Internet of Things, deploying wireless communication networks in indoor environments has become crucial. Indoor infrastructure deployment necessitates innovative approaches for efficiently and accurately obtaining received signal strength (RSS) maps. However, traditional methods for acquiring RSS maps, such as empirical and deterministic models, are limited by significant inaccuracies and high computational demands. Empirical models often fail to capture the complex dynamics of indoor environments, resulting in deviations from actual signal behaviours. On the other hand, deterministic models, while more accurate, are computationally intensive due to their reliance on detailed physical modelling of wave propagation. This study introduces a machine learning approach based on deep convolutional generative adversarial networks (DCGAN) aimed at reconstructing indoor RSS maps with minimal RSS measurements. By leveraging DCGAN's generative and adversarial training capabilities, the method not only surpasses traditional interpolation methods in efficiency and precision but also offers new possibilities for the rapid deployment and optimization of wireless communication systems in indoor environments.

A Hybrid Localization Algorithm for Enhanced Accuracy and Robustness in Healthcare Systems

This paper presents a novel hybrid localization algorithm designed for healthcare systems, integrating Received Signal Strength Indicator (RSSI) and Time of Arrival (ToA) measurements with machine learning techniques. The algorithm aims to enhance the accuracy, robustness, and computational efficiency of sensor localization in dynamic healthcare environments. Experimental results demonstrate that the hybrid algorithm achieves a significantly lower localization error, averaging 0.5 meters, compared to traditional RSSI-only and ToA-only methods. The algorithm's rapid convergence and low computational time make it suitable for real-time applications. Additionally, its robustness to measurement noise, a common challenge in healthcare settings, underscores its reliability. This research underscores the potential of advanced localization technologies to improve patient monitoring, safety, and overall healthcare delivery, with future work poised to further enhance performance and adaptability.

An intelligent security mechanism in mobile Ad-Hoc networks using precision probability genetic algorithms (PPGA) and deep learning technique (Stacked LSTM)

The Mobile Ad-hoc Networks (MANETs) have gained a significant attention in the recent years due to their proliferation and huge application purposes. To defend from many types of modern cyber dangers like Distributed Denial of Service (DDoS) attacks, Advanced Persistent Threats (APTs), Insider Threats, Ransomware, Zero-Day Exploits, Social Engineering tactics, and etc is not easy when it comes to keeping MANETs security. These complex assaults focus on network infrastructure, take advantage of weaknesses in communication protocols and control user actions. Although there have been improvements in intrusion detection systems (IDS), it is still difficult to fully safeguard MANETs. The purpose of this research is to create advanced methods that can accurately find and decrease attacks inside MANETs. Applying Sensor-based Feature Extraction (SFE) to extract useful network features such as Received Signal Strength Indication (RSSI) and Time of Travel (TOT) from datasets NSL-KDD and CICIDS-2017. Utilizing the fresh method of Precise Probability Genetic Algorithm (PPGA) optimization for removing unrelated details, which enhances precision in detecting attacks. Predicting normal and attacking labels by applying Stacked Recurrent Long Short Term Memory (SRLSTM) method, fine-tuning classifier's parameters in every layer to improve outcomes. In order to authenticate and compare the suggested methods with current attack detection tactics, this study will make use of various evaluation measurements. The NSL-KDD, which is a benchmark dataset in network intrusion detection research, has a wide variety of network traffic data with instances that are labeled as normal and different attacks. CICIDS-2017 is similar because it contains an extensive dataset too - this includes real-world traces from network traffic where there's both regular activity and harmful actions. The purpose is to enhance the existing status of MANET security so as it can withstand more strongly against cyber dangers. According to the outcomes, it is analyzed that the attack detection accuracy has improved greatly 99 % when compared to other methods, as shown by the detailed assessment measurements. Better handling of big datasets with top detection accuracy reduces the time needed 8.9 s for training and testing models. Decrease in misclassification results and better ability to differentiate normal network actions from harmful intrusions. Improved resistance of MANETs to different cyber dangers, guaranteeing the safety and dependability of network communication in changing and non-centralized settings.

Pathloss Assessment of a Terrestrial Digital UHF Channel over Kano City, Nigeria

Path loss assessment is an important factor in wireless communications to ensure quality of service. This study involves the measurement of Received Signal Strength (RSS) of Digital Terrestrial Television (DTTV) Star Times Station in Kano at 1 km intervals along three routes. Data were gathered during dry and wet season months in the year 2022. The path loss along the routes were computed based on the Okumura-Hata model. It was observed that path losses were higher during wet than dry season months. The result also indicates a higher negative correlation coefficient (R) of -0.745 between Line of Sight (LOS) and RSS for wet and -0.690 for dry season months respectively for route, A. Similarly, R, of -0.739 between LOS and RSS was also observed during wet and -0.589 for dry season months for route, B. In addition, R, of -0.420 was observed between LOS and RSS during wet and -0.328 for dry season months along route C. The average path loss for wet season is 133.90 dB and for dry season is 127.90 dB. Furthermore, this study introduced a Modified Hata Path Loss Model (MHPL) that takes into account the impact of specific tropospheric parameters such as LOS and elevation. Findings further indicated that the path loss predicted by the conventional Hata model exhibited a steady exponential increase with LOS. Similarly, MHPL followed a comparable trajectory, with some exceptions manifesting as peaks and valleys. The overall findings will be useful for power budgeting that will enhance quality of service of DTTV signal over Kano city.

Pathloss Assessment of a Terrestrial Digital UHF Channel over Kano City, Nigeria

Path loss assessment is an important factor in wireless communications to ensure quality of service. This study involves the measurement of Received Signal Strength (RSS) of Digital Terrestrial Television (DTTV) Star Times Station in Kano at 1 km intervals along three routes. Data were gathered during dry and wet season months in the year 2022. The path loss along the routes were computed based on the Okumura-Hata model. It was observed that path losses were higher during wet than dry season months. The result also indicates a higher negative correlation coefficient (R) of -0.745 between Line of Sight (LOS) and RSS for wet and -0.690 for dry season months respectively for route, A. Similarly, R, of -0.739 between LOS and RSS was also observed during wet and -0.589 for dry season months for route, B. In addition, R, of -0.420 was observed between LOS and RSS during wet and -0.328 for dry season months along route C. The average path loss for wet season is 133.90 dB and for dry season is 127.90 dB. Furthermore, this study introduced a Modified Hata Path Loss Model (MHPL) that takes into account the impact of specific tropospheric parameters such as LOS and elevation. Findings further indicated that the path loss predicted by the conventional Hata model exhibited a steady exponential increase with LOS. Similarly, MHPL followed a comparable trajectory, with some exceptions manifesting as peaks and valleys. The overall findings will be useful for power budgeting that will enhance quality of service of DTTV signal over Kano city.

Design of a Frequency Controller System for UHF Radio Wave Receiver Device

Frequency is fundamental in data communication for transmitter and receiver stations. The most common problem found in the data communication is the signal quality measurement. A LoRa module, as one of the wireless devices with UHF (Ultra High Radio) frequencies, has extended range frequencies and signal strength parameters (RSSI, Received Signal Strength Indicator). RSSI parameter on signal bandwidth and the distance between stations. However, there is no supporting research in terms of the RSSI quality and short-distance variations. In line with this, this study aims to design a frequency control system of a LoRa module and to investigate the effect of short-distance and frequency differences on RSSI. The system consists of a transmitter, a LoRa SX1278 module, and push buttons as frequency control buttons. The system's performance was tested by receiving data over various distances from 0 to 1.25 m. The results show that the system works efficiently in controlling the frequency (ranging from 433 – 525 MHz) with good flexibility and accuracy. The system maintained an RSSI level > -120 dBm using a constant power supply and varied distances. The highest RSSI level is found at the shortest distance (0.2 m) with a higher frequency (525 MHz). There is a significant correlation between distance, frequency variations, and RSSI levels (R2 > 0.75). It can be concluded that the LoRa SX1278 module can be used as a short-distance-based data communication with moderate quality (average RSSI levels are about 80 - 120 dBm). Higher frequencies provide better RSSI levels due to increased transmission energy.

Evaluating Signal Quality and System Performance in NB-IoT Communications

The expansion of the Internet of Things (IoT) has created a need for reliable and fault-tolerant communication networks. However, ensuring consistent signal quality and power efficiency has proven to be challenging. This study evaluated the performance of Narrowband IoT (NB-IoT) communications using the SIM7020 module connected to a Raspberry Pi 4 Model B, focusing on signal quality across indoor, outdoor, urban and rural areas. Supervised machine learning for indoor localisation based on Received Signal Strength Indicator (RSSI) has been introduced, for example, to enhance NB-IoT performance. However, this and other approaches have encountered difficulties in mobile and obstructed environments, including signal attenuation, connectivity variability and increased power consumption. The objective of this study was to analyse NB-IoT signal strength and power consumption, providing guidance for deploying real-time communication IoT applications. Empirical data was analysed to understand the RSSI and Cellular Signal Quality (CSQ) in different locations. Signal quality in urban and outdoor environments was prone to fluctuations due to mobility and interference, whereas rural areas had weaker but more consistent signals. Indoor environments suffered from significant signal attenuation. The results emphasise the importance of improved handover mechanisms and adaptive deployment strategies to ensure reliable connectivity across various IoT applications.

Radio Tomography Imaging using Adjacent Criterion Method to determine the Localization Error

Observed that most setups have limitations in the number of RF nodes due to a limited number of measurements. However, it is well known that the main difficulty in radio tomographic imaging attributes to the uncertainties in the receive signal strength (RSS) measurements of transceivers due to multipath effects, especially, when the environment of interest is much cluttered, and requirements on the larger number of nodes for the performance improvements. However, no study has been conducted to solve the inverse problem and improve the quality of the reconstructed image using a reduced sensor model for Radio tomography system localization. This work focuses on the design and development of a Radio tomography system for human localization that will employ a transceiver sensor arrangement to increase the number of measurements, without making any changes to the hardware design as well as the number of pixels in the sensing domain. An image reconstruction technique namely, Adjacent Criterion Method (ACM) was proposed to enhance the image spatial resolution. A number of experiments were used to evaluate the performance of the system. The results showed that the proposed technique improves the spatial resolution and exhibits more accurate tomograms

H2LWRF-PDR: An efficient indoor positioning algorithm using a single Wi-Fi access point and Pedestrian Dead Reckoning

Indoor localization is challenging in densely populated urban areas due to the multipath effect, attenuation, noise, and difficulty finding reliable Wi-Fi access points. In urban flats, multiple access points are not easily available or require infrastructure setup, which increases cost. To address the above issues, the article proposes a new algorithm called ‘H2LWRF-PDR’ based on Wi-Fi fingerprinting using a single access point with extended features. This algorithm is designed for static and moving objects. The proposed algorithm first converts the received signal strengths from a single access point into five statistical features used as fingerprints. Then, hierarchical clustering is applied to remove outliers from the fingerprints. Then, a random forest classifier is used to predict the cluster values. Finally, the lowess filters are used to smoothen the output values from the classifier. Furthermore, these outputs are combined with the proposed Pedestrian Dead Reckoning (PDR) algorithm. In the end, a Savitzky–Golay filter is utilized to remove the fluctuations in the trajectories. The proposed approach has been validated in urban flat and laboratory environments and compared with state-of-the-art localization algorithms. The proposed algorithm ‘H2LWRF-PDR’ showed 50% improvements in an urban flat dataset and at least 30% in the laboratory dataset.

Using proximity loggers in studies of sheep behavior: an estimation of the daily frequency of water consumption

BackgroundProximity loggers are a biotelemetry technology used to quantify animal–animal interactions non-invasively. They are small devices can be affixed to a target animal and detects and records if another device is within a certain distance, which can provide unprecedented insights into individual behavior and interactions with other animals or other aspects of the environment. Proximity loggers also can record the duration and distance between the two or more individuals detected, and have been used to study a diverse array of interactions. Studying drinking behavior in sheep is important for several critical reasons, such as improving animal welfare, enhancing productivity, managing resources efficiently, and ensuring sustainable agricultural practices. The objective of this study was to test the capacity for Bluetooth Low Energy proximity loggers to quantify drinking habits in sheep (n = 7 adult females). At all times, they had access to water from two drinking troughs. Animals wore neck collars that contained a mobile logger, and two stationary loggers were placed under the drinking troughs. To store the records downloaded from the stationary loggers, a gateway was placed close to the troughs. The mobile loggers were programmed as beacons to transmit their ID to the stationary loggers, which were programmed as receivers to scan the proximity of the mobile loggers, record the time, the IDs of the mobile loggers, and the received signal strength of the contact with the mobile loggers.ResultsEwes visited the drinking troughs 2.4 ± 0.5 times per day (rank 1.4–2.9), most frequently (42%) between 0800 and 1000 h, and 84% occurred in the daytime. Mean length of each water intake was 1.72 ± 0.30 min. The cosinor analysis of the distribution of drinking events revealed a significant (P < 0.0001) 24-h circadian rhythm in drinking events, and the acrophase (time of the highest proportion of events) occurred at 1054 h.ConclusionsConsidering that studying water intake in sheep is crucial for optimizing their health, welfare, and management, the proximity loggers were demonstrated to be appropriate for documenting the drinking habits of sheep, and probably, would be suitable for studies of other behaviors that involve approaching other animals or objects.