Received Signal Strength Indicator Values Research Articles

A Light Solution for Device Diversity Problem in a Wireless Local Area Network Fingerprint Indoor Positioning System

The development of location-based services requires an increasingly accurate positioning system technology. Research on outdoor positioning systems has achieved satisfactory accuracy and has been commonly used in various location-based services. The research trend is now shifting toward the Indoor Positioning System (IPS). One technique that is widely used in Wi-Fi-based IPS is fingerprinting. The fingerprinting technique on Wi-Fi uses the Received Signal Strength Indicator (RSSI) value. The problem that occurs is that the results of RSSI measurements on smartphones of different brands will produce different RSSI values, also known as device diversity. Device diversity will cause a decrease in system accuracy. This study aims to offer a solution to the problem of device diversity in Wi-Fi IPS based on RSSI Fingerprinting, i.e., to get a minor distance error. The proposed solution is to modify the original database radio map into two new databases: the difference database and the ratio database. The Difference Database Radiomap was able to reduce the average value of distance errors by 24.3% in Meizu and 28% in OPPO. Then, using the Radiomap database ratio, the average value of distance errors could be reduced by 13% in Meizu and 24% in OPPO. From the calculation, Radiomap database ratio can provide solutions to the problem of device diversity for an Indoor Positioning System better than the difference database radiomap if we looked at reduced distance error.

Extremely Randomized Trees Regressor Scheme for Mobile Network Coverage Prediction and REM Construction

In mobile communications network planning (and designing any radio system), coverage prediction helps network operators optimize cellular networks to improve customer experience. Accordingly, several path-loss models have been proposed that depend on many conditions, such as suitable selection of the terrain for each model, the height of the receiver and transmitter above ground and the distance between them, and the presence of obstacles. This may increase the prediction error between actual and estimated values, which change according to the propagation model selected. To overcome these problems, we propose a novel approach to mobile coverage prediction based on an extremely randomized trees regressor (ERTR) algorithm. In addition, we construct a radio environment map (REM) over a Google Earth digital map to improve visualization of the results and to easily detect coverage holes and traffic hotspots. For this purpose, we utilize a dataset with real measurements collected from Victoria Island and Ikoyi in Lagos, Nigeria. For performance evaluation, we use k-fold cross-validation based on four error metrics: relative error, root mean squared error, mean absolute error, and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> score. The proposed ERTR scheme achieves the best performance in terms of accuracy and computational load in predicting the reference signal received power and the received signal strength indicator value. We prove this with extensive simulation analysis and by comparing the error metrics of the proposed ERTR approach with an existing method widely used to perform coverage prediction, called ordinary kriging. We also compared seven machine learning regression algorithms, namely, random forest, a bagging regressor, support vector regression, k-nearest neighbors, a deep neural network, Gaussian process regression, and the decision tree.

Real-Time Step Length Estimation in Indoor and Outdoor Scenarios.

In this paper, human step length is estimated based on the wireless channel properties and the received signal strength indicator (RSSI) method. The path loss between two ankles, called the on-ankle path loss, is converted from the RSSI, which is measured by our developed wearable hardware in indoor and outdoor ambulation scenarios. The human walking step length is estimated by a reliable range of RSSI values. The upper threshold and the lower threshold of this range are determined experimentally. This paper advances our previous step length measurement technique by proposing a novel exponential weighted moving average (EWMA) algorithm to update the upper and lower thresholds, and thus the step length estimation, recursively. The EWMA algorithm allows our measurement technique to process each shorter subset of the dataset, called a time window, and estimate the step length, rather than having to process the whole dataset at a time. The step length is periodically updated on the fly when the time window is "sliding" forwards. Thus, the EWMA algorithm facilitates the step length estimation in real-time. The impact of the EWMA parameter is analysed, and the optimal parameter is discovered for different experimental scenarios. Our experiments show that the EWMA algorithm could achieve comparable accuracy as our previously proposed technique with errors as small as 3.02% and 0.30% for the indoor and outdoor scenarios, respectively, while the processing time required to output an estimation of the step length could be significantly shortened by 53.96% and 60% for the indoor walking and outdoor walking, respectively.

RSSI study of wireless Internet of Things technologies

The Internet of Things (IoT) idea now entails a dispersed collection of different sensor networks with several functions that collect data, which is then analysed and used in applications such as smart cities. These networks are capable of transmitting massive volumes of data in a relatively efficient, yet unsecure wireless environment. These applications will only succeed if is developed a dependable a low-cost real-time method for pinpointing accurate location. Power consumption is another consideration for indoor localisation. Recent wireless technologies like ZigBee, Bluetooth Low Energy (BLE) and Long Range (LoRa), use less resources, making them ideal for interior positioning. When IoT devices are utilised, these technologies are compared in terms of precision of localisation and power consumption. Tracking the Received Signal Strength Indicator (RSSI) values can be used to locate mobile sensor nodes for low-power IoT networks. The RSSI values of sensor nodes in the BLE, ZigBee and LoRa networks for IoT were explore in this study.

Inter-User Distance Estimation Based on a New Type of Fingerprint in Massive MIMO System for COVID-19 Contact Detection.

In this paper, we address the challenging task of estimating the distance between different users in a Millimeter Wave (mmWave) massive Multiple-Input Multiple-Output (mMIMO) system. The conventional Time of Arrival (ToA) and Angle of Arrival (AoA) based methods need users under the Line-of-Sight (LoS) scenario. Under the Non-LoS (NLoS) scenario, the fingerprint-based method can extract the fingerprint that includes the location information of users from the channel state information (CSI). However, high accuracy CSI estimation involves a huge overhead and high computational complexity. Thus, we design a new type of fingerprint generated by beam sweeping. In other words, we do not have to know the CSI to generate fingerprint. In general, each user can record the Received Signal Strength Indicator (RSSI) of the received beams by performing beam sweeping. Such measured RSSI values, formatted in a matrix, could be seen as beam energy image containing the angle and location information. However, we do not use the beam energy image as the fingerprint directly. Instead, we use the difference between two beam energy images as the fingerprint to train a Deep Neural Network (DNN) that learns the relationship between the fingerprints and the distance between these two users. Because the proposed fingerprint is rich in terms of the users’ location information, the DNN can easily learn the relationship between the difference between two beam energy images and the distance between those two users. We term it as the DNN-based inter-user distance (IUD) estimation method. Nonetheless, we investigate the possibility of using a super-resolution network to reduce the involved beam sweeping overhead. Using super-resolution to increase the resolution of low-resolution beam energy images obtained by the wide beam sweeping for IUD estimation can facilitate considerate improvement in accuracy performance. We evaluate the proposed DNN-based IUD estimation method by using original images of resolution 4 × 4, 8 × 8, and 16 × 16. Simulation results show that our method can achieve an average distance estimation error equal to 0.13 m for a coverage area of 60 × 30 m2. Moreover, our method outperforms the state-of-the-art IUD estimation methods that rely on users’ location information.

A practical approach based on Bluetooth Low Energy and Neural Networks for indoor localization and targeted devices’ identification by smartphones

With the constant development of innovative technologies and the resulting growth of new services available on the market, applications that aim to control devices employing smartphones include more extensive selection menus increasingly. This condition can lead to non-intuitive use of the system. As a result, the user may reap an adverse experience while practicing these applications. An indoor tracking system must create a control method with a dynamic user interface and estimate the device pointed to by a smartphone. The use of Bluetooth Low Energy (BLE) and the fingerprinting technique, based on Received Signal Strength Indication (RSSI) values, can be a feasible solution for indoor localization. Furthermore, to obtain ever more precise indoor localization systems, there is continuous research on artificial neural networks’ employment as they adapt more quickly to changes in the RSSI values. This paper proposes a new approach to control devices through smartphones based on the joint application of BLE, fingerprinting, and neural networks. The system offers a dynamic user interface that changes according to the target device through sensors commonly located in modern smartphones.

LoRaWAN Based Indoor Localization Using Random Neural Networks

Global Positioning Systems (GPS) are frequently used as a potential solution for localization applications. However, GPS does not work indoors due to a lack of direct Line-of-Sight (LOS) satellite signals received from the End Device (ED) due to thick solid materials blocking the ultra-high frequency signals. Furthermore, fingerprint localization using Received Signal Strength Indicator (RSSI) values is typical for localization in indoor environments. Therefore, this paper develops a low-power intelligent localization system for indoor environments using Long-Range Wide-Area Networks (LoRaWAN) RSSI values with Random Neural Networks (RNN). The proposed localization system demonstrates 98.5% improvement in average localization error compared to related studies with a minimum average localization error of 0.12 m in the Line-of-Sight (LOS). The obtained results confirm LoRaWAN-RNN-based localization systems suitable for indoor environments in LOS applied in big sports halls, hospital wards, shopping malls, airports, and many more with the highest accuracy of 99.52%. Furthermore, a minimum average localization error of 13.94 m was obtained in the Non-Line-of-Sight (NLOS) scenario, and this result is appropriate for the management and control of vehicles in indoor car parks, industries, or any other fleet in a pre-defined area in the NLOS with the highest accuracy of 44.24%.

Computational intelligence-enabled prediction and communication mechanism for IoT-based autonomous systems

Autonomous systems and the Internet of Things (IoT) have become more sophisticated research areas and entered successfully into various daily living activities such as smart homes & buildings, autonomous cars, drones, robots, etc. A crucial and essential aspect of these systems is the precision and accuracy of the decision-making process, i.e., the decision support system. Likewise, developing a completely autonomous system is an open research problem. This paper proposes a computational intelligence-based prediction and communication mechanism for the independent system where IoT is used as a data collection tool. Initially, energy gauge (EG) devices collect helpful information about neighboring devices in the IoT networks. Then, information about the potential relaying devices is broadcasted by the concerned EG device, which uses every member device to adjust routing path(s) in the autonomous system. Furthermore, every EG device has an embedded computational intelligent decision support system that is used to precisely predict the criticality of a neighboring device (preferably relay) in the autonomous systems. Therefore, every device must ensure data transmission via the most reliable path(s), i.e., avoiding critical devices if possible. A device is assumed critical if either its residual energy or received signal strength indicator value is less than the defined threshold values for the autonomous systems. Additionally, the proposed mechanism has ensured a uniform traffic distribution of the transmitted packets in the autonomous system. The operational applicability of the proposed computational intelligence-enabled prediction mechanism in the autonomous system is verified by comparing it with the existing approaches. Simulation results show that the proposed scheme has enhanced the accuracy of the concerned autonomous systems more than other schemes.

Combining Multichannel RSSI and Vision with Artificial Neural Networks to Improve BLE Trilateration

The demands for accurate positioning and navigation applications in complex indoor environments such as emergency call positioning, fire-fighting services, and rescue operations are increasing continuously. Indoor positioning approaches apply different types of sensors to increase the accuracy of the user’s position. Among these technologies, Bluetooth Low Energy (BLE) appeared as a popular alternative due to its low cost and energy efficiency. However, BLE faces challenges related to Received Signal Strength Indicator (RSSI) fluctuations caused by human body shadowing. This work presents a method to compensate RSSI values by applying Artificial Neural Network (ANN) algorithms to RSSI measurements from three BLE advertising channels and a wearable camera as an additional source of information for the presence or absence of human obstacles. The resulting improved RSSI values are then converted into ranges using path loss models, and trilateration is applied to obtain indoor localization. The proposed artificial system provides significantly better localization solutions than fingerprinting or trilateration using uncorrected RSSI values.

Hybrid localization scheme using K‐fold optimization with machine learning in WSN

SummaryNode localization technology can identify and track nodes, making observing data more relevant; for example, information received at the sink node would be useless to the client if node localization data from the sensor region were not included. Localization is described as determining the location of unknown sensor nodes named destination nodes applying the recognized location of anchor nodes based on measurements such as time difference of occurrence, time of occurrence, angle of occurrence, triangulation, and maximum probability. The purpose of node localization is to assign coordinates points to all sensor nodes arbitrarily put in the monitoring region and have an unknown location. Localization of nodes is essential to account for the cause of events that help group sensor querying, routing, and network coverage. In this paper, data transmission among the nodes is done by comparing the received signal strength indicator (RSSI) value with the supervised learning value. If the RSSI value is less than the supervised learning value, the data transmission takes place; else, no transmission. This paper proposes a hybrid localization scheme that effectively uses K‐fold optimization with supervised learning and gives good results for distance error and RSSI/energy efficiency. The proposed scheme can effectively detect the optimal path for data transmission, node localization for the destination, and overall performance enhancement using threshold decision making.

Optimizing the LoRa network performance for industrial scenario using a machine learning approach

In this article, the performance of the LoRa network for an industrial scenario has been optimized using a machine learning approach. The network performance is analyzed in terms of received power, outage probability, spectral efficiency and bit error rate (BER). A link-level performance of the LoRa network for an indoor industrial area considering both the non-obstructive and obstructive scenarios has been experimentally evaluated in terms of received signal strength indicator (RSSI) and signal-to-noise ratio (SNR). Using the measured values of RSSI and SNR at the LoRa gateway, the received power is mathematically modelled which is further considered as an optimization problem. First, an artificial neural network (ANN) model was built and trained to predict the received power. Particle swarm optimization (PSO) algorithm was further used to find the optimal values of LoRa parameters corresponding to maximum received power. Simulation results reveal that the proposed optimization approach significantly improves the outage probability, spectral efficiency and BER of the LoRa network.

Internet of Things (IoT) Device Management in Rural Areas to Support Precision Agriculture

Internet of Things (IoT) devices have an important role in supporting precision agriculture systems, namely acting as the first layer consisting of several nodes in a limited network group as a producer of land, environmental and plant parameter data that will be processed at the next level. This node is an embedded system consisting of sensors, microcontrollers, power supplies and wireless communication media. The challenge that arises is when the number of new nodes that will be connected to the concentrator in a limited network group is quite large and is still done manually by the user, so that it has the potential to cause complexity and errors in configuration. The purpose of this research is to design a self-registration scheme from each new node to the concentrator in a restricted network group automatically. The method used by new nodes for self-registration is to read the RSSI (Received Signal Strength Indicator) value from the concentrator, then exchange metadata according to the OCF (Open Connectivity Foundation) standard. The concentrator will refer to the maximum number of nodes allowed to join the group and check the metadata of the new node. If the registration process is successful, the new node will gain access rights to communicate with the concentrator and its closest neighbours in the network group. This mechanism will help farmers in rural areas to be able to configure Internet of Things (IoT) devices easily and provide benefits in supporting precision agriculture.

Machine learning-based indoor localization and occupancy estimation using 5G ultra-dense networks

Nowadays, mobile applications need the location of the running devices to operate properly. This has increased the interest in indoor localization. Furthermore, the ability to sense mobile devices in indoor environments opens the door for building occupancy-count estimation. Studies have shown that occupant's detection and building occupancy-count estimation can be utilized to improve the efficiency of building operation and management. This research introduces new models to study the performance of such indoor localization and building occupancy-count estimation using the available technological advances in 5G Ultra-Dense Networks (UDNs). We propose an algorithm to collect the Received Signal Strength Indicator (RSSI) from User Equipments (UEs) and use it to build a fingerprinting database. We then use Machine Learning (ML) to estimate the location of the UEs in buildings from their RSSI values. Detecting users in the building is treated as a binary-classification problem. We then use various ML algorithms to build models for indoor occupancy-count estimation. Finally, the localization of users is used to estimate occupancy in specific sections of the building. The simulation results show that UDNs can provide accurate indoor localization, occupancy-count estimation in a building and in parts within the building.

정밀 위치 확인을 위한 레이더 기반 실내 위치추적 시스템 설계에 관한 연구

As the use of smart devices increases, the importance of the IoT(Internet of Things) and AR(Augmented Reality) is growing. This leads increasing demand for accurate location-based services, but tracking indoor location using existed way is difficult and expensive. This paper devises a new method to address these shortcomings. The radar based method process conjoined four-directed beacons’ BLE(Bluetooth Low Energy) RSSI(Received Signal Strength Indicator) values to find out the direction and distance of the target. During the process Kalman filter is applied to reduce noise of RSSI caused by unpredictable radio propagation characteristics in various indoor environments, also using LSTM(Long Short Term Memory) to track the exact location.

IMPLEMENTATION OF RANGE-BASED AND RANGE-FREE 3D INDOOR LOCALIZATION IN MULTI-STORY BUILDING BASED ON RSSI

The real-life indoor localization implementation in a multi-story building is reasonably necessary. Multi-floor shopping centers, airports, residential areas, especially in the big cities, apply positioning schemes to ease visitors or inhabitants. However, most indoor localization researches still emphasize 2D-indoor localization, and the multi-story indoor localization implementations are still limited. One of the challenges of 3D-indoor localization implementation is the shadowing effect caused by signal propagation obstructed by objects in the room, the walls, and floors between rooms. Some researchers conducted the 3D-indoor localization to consider the elevation property of the position estimation scenario. However, there are still very few experimental results in an actual multi-story building as the authors' concerns. This paper proposes the measurement campaign of a 3D-indoor localization system in the actual multi-story building by applying the range-based and range-free method based on the Wireless-Fidelity (Wi-Fi). This research is essential since Wi-Fi is available in almost all smart devices and is installed almost in every corner globally. Compared to other approaches, we propose a relatively simple Wi-Fi-based indoor 3D localization utilizing the specific parameter, received signal strength indicator (RSSI), in a static indoor lobbies environment. Despite some of its advantages, the RSSI parameter has a disadvantage in signal fluctuation over time. In our approach, we tried to solve this issue by applying the min-max algorithm to improve the known trilateration method as the range-based method. We implemented the min-max to observe how far the range-based can still give acceptable positioning results in an actual multi-story building. On the other hand, we used the RSSI values for the range-free method to construct the fingerprint database and employed the machine-learning (ML)-based pattern matching algorithm, the random forest algorithm. We expect to solve the shadowing problem with this radio fingerprint method and to achieve minimal errors. We conducted the measurement campaign using the low-cost Wi-Fi module, the ESP-8266, to generate the RSSI. We placed three ESP-8266 nodes on each floor of a two-floor building as the access points (APs) and an ESP-8266 as a target node or a station (STA). We emphasized two performance metrics to evaluate our proposed system performance: the location estimation accuracy observed as the mean square error (MSE) and the precision shown as the standard deviation (Std Dev). The results show that the fingerprint technique yielded the MSE of 0.9m and Std Dev of 0.69 m, while the min-max method resulted in the performance of MSE of 1.79 m and Std Dev of 0.89 m. These results show that the fingerprint technique still gave better accuracy and precision in the same measurement campaign than the min-max. However, the min-max results are also acceptable since the whole multi-floor building has more than 4 m in elevation. The indoor localization system for multi-story buildings can be applied using both the fingerprint and the min-max in a relatively static environment by observing our system performance metric

Fault-Tolerant indoor localization based on speed conscious recurrent neural network using Kullback-Leibler divergence.

IoT services are the basic building blocks of smart cities, and some of such crucial services are provided by smart buildings. Most of the services like smart meters, indoor navigation, lighting control, etc., which contribute to smart buildings, need the locations of people or objects within the building. This gave rise to Indoor Localization, where only the infrastructure of the building has to be used for localization as accessing the Global Positioning System is difficult in indoor environments. Many approaches have been proposed to predict locations based on the infrastructure available indoors, and some of such techniques use Wi-Fi access points. Still, unfortunately, very few studies have concentrated on tolerating faults while being cost-effective. This work discusses hardware implementation of indoor localization. It then proposes a learning algorithm SRNN (Speed Conscious Recurrent Neural Network) that uses the RSSI (Received Signal Strength Indicator) values of available Wi-Fi access points in the building and predicts the location. Also, fault-tolerant approaches termed nearest RSSI and the most recent RSSI using Kullback–Leibler Divergence have been proposed to improve the location accuracy when access points go down and are prone to faults. Both the proposed approaches nearest RSSI and most recent RSSI along with SRNN improve the location accuracy by 4% and 2.1%, respectively, over existing techniques and contribute to optimizing predicted location's accuracy in Indoor Localization an IoT service for smart buildings.Supplementary informationThe online version contains supplementary material available at 10.1007/s12083-022-01301-y.

Study of RSSI Accuracy for Outdoor Sensor Localization

The movement characteristics that nodes exhibit will bring serious problems in a Wireless Sensor Network (WSN) because most of the protocols do not adapt to node movement. This requires a mobility-aware evaluation technique to locate nodes, forecast communication quality, trace the movement trajectory and formulate thresholds to initiate future handover. One off-the-shelf technology using the Received Signal Strength Indicator (RSSI) of wireless radio is extensively adopted because of the small expense. A node can compute the relative distance between it and its partner by using RSSI readings. However, the measurement results of RSSI fluctuate significantly due to the effects of shadowing and fading. This paper is oriented to investigate the localization accuracy of RSSI in an outdoor ambient condition. To do this, some dynamic and stationary tests are carried out, a calibration line displaying the one-to-one match between RSSI and distance is mapped, five filtering approaches are developed to alleviate the fluctuation of movement curves, and the alleviation effect is measured by calculating the Root Mean Square Error (RMSE) values and by converting the time-domain filtering results into the Fast Fourier Transform (FFT) spectrum. Although the optimized RMSE is reduced to 0.86 and the noise FFT amplitude is less than 1[Formula: see text]dBm, it may still have a single RSSI value corresponding to more than one distance. Moreover, these distances can differ as much as 2.8[Formula: see text]m. Since the evaluation error is not acceptable for most cases, it is inaccurate for a mobile node to measure the distance from its partner rely on RSSI readings under outdoor scenarios.

Towards a better indoor positioning system: A location estimation process using artificial neural networks based on a semi-interpolated database

The Wi-Fi-fingerprinting positioning method is used widely in indoor positioning environments due to its simplicity and wide coverage. However, in the offline phase of the method, the collection process is a fundamental and critical step that requires time and effort. Moreover, the location estimation process, which is executed in the second Wi-Fi-fingerprinting phase (online phase), needs to be accurate enough to guarantee efficient indoor positioning. Hence, in this work, a novel indoor location-estimation process based on a semi-interpolated radio map and artificial neural network (ANN) is presented. A mobile application is built to gather the received signal strength indicator (RSSI) fingerprinting to construct a radio map, which is then expanded with the biharmonic spline interpolation (BSI) method through the estimation of more RSSI values. A feedforward back propagation (FFBP) neural network and generalised regression neural network (GRNN) were built in the online phase for the location-estimation process. They were trained using the expanded dataset by taking the reference point (X, Y) coordinates as their desired output and using two different forms of the data as their inputs. The first inputs are the RSSI values from the 17 access points (APs) – three of the APs have dual-band i.e, support both 2.4 and 5 GHz – and the second input is based on a selected set of APs, which produce a high level of acceptable RSSI and their coordinates. A comparison between these two models was done. The results show that FFBP outperforms GRNNs in terms of structure simplicity, while GRNNs achieved more accurate prediction results with an average distance error of up to 0.48 m. Hence, our proposed methodology leverages building a simple neural network topology that has good location estimation results for indoor positioning in a low-cost localisation process.

Sistem Monitoring Tekanan Pada Pipa Air Menggunakan Arduino Uno Pada Jaringan Lora 920-923 Mhz

In everyday life water is one of the components that is very important and becomes a basic need for human life and other living things. Of course, when flowing water to all corners there are obstacles such as leaks in water pipes that can be caused by air pressure. These leaks can cause the flowing water to get dirty or even not flow all the way to the destination. Therefore, this study will be created a pressure monitoring system on water pipes that will use pressure transmitter sensors that serve to read the pressure on water pipes using LoRaWAN data communication, this communication is used because the device can communicate up to a distance of 5 to 15 kilometers. To connect or send data from the sensor to LoRa requires an Arduino uno that will process the data from the sensor to be used. To store data using the Antares platform. This research is useful for monitoring pressure on water pipes and minimizing leaks in pipes. By using the method of calculating linear regression equations obtained a very accurate value for water pressure readings on pressure sensors that are 98.9%, while the results of testing the values received signal strength indicator (RSSI), signal noise radio (SNR) and packet loss are interconnected, in the value of RSSI and SNR the smaller the packet loss the more data is lost. The use of Spreading Factor values can also be taken into account if you want a longer range with a small packet loss result.

Investigation of UHF Signal Strength Propagation at Warehouse Management Applications Based on Drones and RFID Technology Utilization

As a part of the supply chain, inventory management includes, among other things, maintaining the storage of stock, controlling the amount of product for sale and order fulfilment. In business terms, inventory management means the right stock, at the right levels, in the right place, at the right time. In the case of large outdoor warehouses, common identification methods are lengthy and inappropriate. One way to determine inventory easily and quickly is to deploy UAV’s (unmanned aerial vehicle) for product identification purposes. In this case, however, there is a problem in determining where the goods are located. A drone moves at higher altitudes, which can lead to a situation where we will not be able to determine the exact location of the goods. This article deals with a method of determining the correct flight level suitable to distinguish the identified items located at least 2 m apart. The evaluation is performed based on an RSSI (received signal strength indicator) value. The experiment proved that even at maximum reading distance of selected passive UHF RFID tags the two objects can be distinguished.