Received Signal Strength Indicator Research Articles

An adaptive indoor localization model based on Cauchy-PSO optimized BPNN

Received Signal Strength Indication (RSSI) fluctuates with the change of indoor noise, resulting in a large positioning error of the trained Back Propagation Neural Network (BPNN). An adaptive indoor positioning model based on Cauchy particle swarm optimization (Cauchy-PSO) BPNN is proposed to solve the problem. In the off-line training phase, the signal with less noise intensity acquired in a good environment is selected as the original training set in the localization phase. The variance of the received set of signals is used as a measure of the noise intensity of the current environment. In the localization phase, the variance of each set of signals received is calculated at equal intervals. If the variance of adjacent intervals differs significantly, the system adjusts the original training set data according to the current noise intensity and re-trains the BP model online. Meanwhile, the particle swarm optimization algorithm using Cauchy variance to optimize the BP network tends to fall into the disadvantage of local optimum. Considering that the collected fingerprint database may generate “high-dimensional disasters”, Principal Component Analysis (PCA) is used to select and downscale the features of the wireless Access Point (AP). The proposed adaptive localization model can be trained online. The improved Cauchy-PSO algorithm and data dimensionality reduction can further improve the localization accuracy and training speed of the BP model. The experimental results show that the adaptive indoor localization model has strong adaptive capability in a noise-varying environment.

Autonomous multicopter landing on a moving vehicle based on RSSI

AbstractCurrently, most of the studies on unmanned aerial vehicle (UAV) automatic landing systems mainly depend on image information to determine the landing location. However, the system requires a camera, a gimbal system and a separate image-processing device, which increases the weight and power consumption of the UAV, resulting in a shorter flight time. In addition, a large amount of computation and slow reaction speed can cause the camera to miss a proper landing moment. To solve these problems, in this study, the moving direction and relative distance between an object and the automatic landing system were measured using a receive signal strength indicator of the radio-frequency (RF) signal. To improve the movement direction and relative distance estimation accuracy, the noise in the RF signal was minimised using a low pass filter and moving average filter. Based on the filtered RF signal, the acceleration of the multicopter to reach the object was estimated by adopting the proportional navigation algorithm. The performance of the proposed algorithm for precise landing on a moving vehicle was demonstrated through experiments.

Artificial Intelligence Techniques for Mobile Station Location Estimation

Modern wireless communication systems require positioning functions, which provide are automatic location estimation of stations within a network. However, when new networks are implemented, much higher accuracy is required when determining geographical coordinates of a mobile station to develop of services related to the station location. To solve the problem of mobile station positioning, its geographical coordinates are calculated, coordinates of the closest base stations being known. The paper proposes to use a genetic neuro-fuzzy controller for improving the effectiveness of positioning a mobile station. Positioning methods providing usage of artificial intelligence methods are based on measurements of levels for signals from the closets access points or base stations, their coordinates are known. The proposed localization method is based on values of received signal strength indicator – RSSI. At the same time, the RSSI method has a disadvantage – low accuracy, which is proposed to be increased by applying methods of artificial intelligence – fuzzy logic, neural networks, genetic algorithms. Therefore, the objective of this paper is to elaborate an optimized method for determining location of a mobile station. In compliance with the suggested method, RSSI values and ToA values enter the genetic neuro-fuzzy controller, after corresponding processing, the distance from the mobile station to the base station appears at its output.

Traffic based power consumption and node deployment in green LTE-A cellular networks

The energy utilization of green cellular networking or cellular network infrastructure has become a popular research domain due to the economic concern of network operators and global climate change. Also, due to the growth of wireless networks, the authors are attracted to finding novel relay station (RS) deployment techniques to support high user density and high data rate services by reducing energy consumption and operating costs of different network elements. The location of RSs greatly influences how far a cell can be covered. Therefore, the deployment RS plays a major role in widening the cell's coverage radius. To solve these challenges and to realize greener network designs by maximizing power consumption with QoS assurance, a novel traffic-based power consumption minimization and node deployment for the LTE green cellular networks is proposed. Also, key performance indicators (KPIs) are required to monitor and improve network performance. KPIs control the accomplished resource utilization and the quality of services. The research work comprises RS deployment, traffic estimation, and power allocation. Power losses, communication delays, increased implementation costs, and decreased throughput are the effects of an inappropriate RS deployment. Therefore, this research introduces the transmission area-based relay stations deployment scheme (TARSD) with multiobjective functions maximization of coverage area, minimization of the overlapping area and the minimization of power consumption cost. Traffic estimation helps to identify future capacity requirements and enables improved planning and decision-making. The deployment scheme of a cellular network relies on network traffic estimation. Nash traffic entropy learning algorithm (NTEL) is used to estimate traffic, and based on the estimated eNB switching ON/OFF is performed to minimize power consumption. Inter cell interference (ICI) can impact the performance of the LTE network. Several power allocation schemes can be used to improve system performance to achieve the optimal trade-off between interference and signal-to-noise ratio (SINR). Because of its good performance and efficacy in improving the mutual information between a channel's input and output, the enhanced low complexity water-filling (ELCWF) has been widely employed in power allocation with its increment and decrement properties. This research studies the statistical behaviour of a Received Signal Strength Indicator (RSSI), Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ) and Signal to Interference noise Ratio (SINR). Also, statistical analysis of cost versus coverage performance is examined. Under the 7th simulation run, the proposed method yields the best coverage of 88.81 and 98.74%, respectively. In the experimental scenario, for the proposed model, the deployment cost for BS and RS are 27 and 13 units, the average throughput per user is 9.74 Mbps, and the coverage ratio is 97% under the BS candidate location of 20, respectively. In the power allocation stage, if the number of users is 50, the proposed model yields a power consumption of 0.2 W, throughput of 23.4 Mbps, delay of 15 ms and SINR of 20.12 dB, respectively.

RSSI based Device-free Human Identification

Researchers have explored many methods and techniques for human detection and identification in diverse contexts. One such approach is studying variations of Radio Frequency (RF) signals (e.g., Received Signal Strength Indicator (RSSI)). RSSI based techniques have been widely used in human detection, but not for unique human identification due to its noisy nature. This research focused on investigating the possibilities and limitations of device-free human identification using Wi-Fi RSSI data with machine learning-based classification techniques. To inspect the characteristics of Wi-Fi RSSI data, the authors have conducted statistical analysis techniques. A Kalman filter was applied to minimize the noise in Wi-Fi RSSI data, followed by a feature extraction process. Furthermore, the authors have conducted multiple research experiments in different configurations of receivers and participant numbers. The experimental results show that the human identification accuracy level increases with the number of receivers used for the data collection. Moreover, the authors have identified that human identification accuracy can be further improved by leveraging proper noise reduction methods and feature extraction processes. For a Kalman filter applied and feature-extracted Wi-Fi RSSI dataset of 20 people, the Support Vector Machine (SVM) - Radial Kernel model recorded the highest average accuracy of 99.58%.

A Study and Analysis of a New Hybrid Approach for Localization in Wireless Sensor Networks

Accurate localization of nodes in a wireless sensor network (WSN) is imperative for several important applications. The use of global positioning systems (GPS) for localization is the natural approach in most domains. In WSNs, however, the use of GPS is challenging because of the constrained nature of deployed nodes as well as the often inaccessible sites of WSN nodes deployment. Several approaches for localization without the use of GPS and harnessing the capabilities of the received signal strength indicator (RSSI) exist in literature, but each of these makes the simplifying assumption that all the WSN nodes are within the communication range of every other node. In this paper, we go beyond this assumption and propose a hybrid technique for node localization in large WSN deployments. The hybrid technique comprises a loose combination of a machine learning (ML) based approach for localization involving random forest and a multilateration approach. This hybrid approach takes advantage of the accuracy of ML localization and the iterative capabilities of multilateration. We demonstrate the efficacy of the proposed approach through experiments on a simulated set-up and follow it up with a feasibility demonstration through a prototypical implementation in the real world.

Fusion of artificial intelligence and game theory for resource allocation in non‐orthogonal multiple access‐assisted device‐to‐device cooperative communication

SummaryDevice‐to‐device (D2D) communication offers a low‐cost paradigm where two devices in close proximity can communicate without needing a base station (BS). It significantly improves radio resource allocation, channel gain, communication latency, and energy efficiency and offers cooperative communication to enhance the weak user's network coverage. The cellular mobile users (CMUs) share the spectral resources (e.g., power, channel, and spectrum) with D2D mobile users (DMUs), improving spectral efficiency. However, the reuse of radio resources causes various interferences, such as intercell and intracell interference, that degrade the performance of overall D2D communication. To overcome the aforementioned issues, this paper presents a fusion of AI and coalition game for secure resource allocation in non‐orthogonal multiple access (NOMA)‐based cooperative D2D communication. Here, NOMA uses the successive interference cancellation (SIC) technique to reduce the severe impact of interference from the D2D systems. Further, we utilized a coalition game theoretic model that efficiently and securely allocates the resources between CMUs and DMUs. However, in the coalition game, all DMUs participate in obtaining resources from CMUs, which increases the computational overhead of the overall system. For that, we employ artificial intelligence (AI) classifiers that bifurcate the DMUs based on their channel quality parameters, such as reference signal received power (RSRP), received signal strength indicator (RSSI), signal‐to‐noise ratio (SNR), and channel quality indicator (CQI). It only forwards the DMUs that have better channel quality parameters into the coalition game, thus reducing the computational overhead of the overall D2D communication. The performance of the proposed scheme is evaluated using various statistical metrics, for example, precision score, accuracy, recall, F1 score, overall sum rate, and secrecy capacity, where an accuracy of 99.38% is achieved while selecting DMUs for D2D communication.

Security Analysis of Eemccp Protocol for Underwater Acoustic Sensor Networks

Underwater Acoustic Communication (UWAC) is a recent addition to the ad hoc networking arena, and it has a variety of applications using autonomous underwater vehicles (AUVs). However, UWAC faces several challenges such as the Doppler Effect, channel availability problems, and limited bandwidth. Efficient management of the network is crucial to overcome these challenges, and clustering is an effective approach to achieve this. In this paper, we propose a novel protocol called EEMCCP (Efficient Energy Management and Cluster-based Communication Protocol) that uses clusters to route packets efficiently with the lowest cost. The Chaotic Algae Algorithm (CAA) is used to efficiently achieve clustering without requiring node position information. One node from each cluster acts as the Cluster Head (CH) to ensure the proper combination of network components. The proposed protocol does not consider nodes based on their location or position. Instead, the Received Signal Strength Indicator (RSSI) value of the Hello packets is used to select some CHs as gateway nodes, which are responsible for supplying AUVs with data. When gateway CH nodes exhaust their energy, back-up nodes take over, making the protocol flexible. Communication links transmit the data from AUV nodes to terrestrial destinations. We compare the performance of EEMCCP with existing underwater communication protocols, and our analysis shows that EEMCCP achieves a higher throughput and a higher Packet Delivery Ratio (PDR) than the existing protocols. EEMCCP is a cost-effective solution for transmitting data underwater, and it opens up several avenues for research in the field of UWAC. EEMCCP achieves a higher throughput and a higher Packet Delivery Ratio (PDR) than existing underwater communication protocols such as AEERP and EEDBR, making it a cost-effective solution to transmit data underwater.

Machine learning‐based LoRa localisation using multiple received signal features

AbstractLow‐power localisation systems are crucial for machine‐to‐machine communication technologies. This article investigates LoRa technology for localisation using multiple features of the received signal, such as Received Signal Strength Indicator (RSSI), Spreading Factors (SF), and Signal to Noise Ratio (SNR). A novel range‐based technique to estimate the distance of a target node from a LoRa gateway using machine‐learning models that incorporates SF, SNR, and RSSI to train the models is proposed. A modified trilateration approach is then used to localise the target node from three gateways. Our experiment used three LoRaWAN gateways and two sensor nodes, on a sports oval with an approximate area coverage of 30,000 square metres. The authors also used a public LoRaWAN dataset to build a model test the proposed method and compare both range‐based distance mapping with trilateration and fingerprint‐based direct location estimation techniques. Our method achieved an average distance error of 43.97 m on our experimental dataset. The results show that the combination of RSSI, SNR, and SF‐based distance mapping provides ∼10% improvement on ranging accuracy and 26.58% higher accuracy for trilateration‐based localisation when compared with just using RSSI. Our method also achieved 50% superior localisation accuracy with fingerprint‐based direct location estimation approaches.

Wi-CL: Low-Cost WiFi-Based Detection System for Nonmotorized Traffic Travel Mode Classification

Traffic travel mode identification and classification are crucial for the development of intelligent transportation systems (ITSs). At present, scholars have investigated the classification of motorized and nonmotorized traffic travel in various road environments; however, the classification of walking and bicycle modes in nonmotorized travel has been largely ignored. Therefore, in this paper, we investigate nonmotorized traffic travel and propose a new low-cost nonmotorized traffic travel mode classification system, known as the Wi-Fi classification (Wi-CL) system that uses Wi-Fi signal detectors and the refined characteristics of nonmotorized travel modes. The Wi-CL system includes four modules: data acquisition module, data processing module, feature extraction module, and mode classification module. In the data acquisition module, the proposed system detects the Wi-Fi signals of traffic participants in road environments. In addition, we propose a received signal strength indicator (RSSI) filtering algorithm for hybrid traffic networks that effectively addresses surrounding obstacles and environmental noise. In the feature extraction module, we extract relevant traffic features to construct a mode classification model. Finally, a recurrent neural network (RNN) framework based on the long short-term memory (LSTM) algorithm is successfully implemented in the mode classification module for traffic travel mode identification. To validate the effectiveness of the Wi-CL system, extensive experiments were conducted using field data collected by Wi-Fi detectors installed at the South China University of Technology (SCUT). The experimental results show that the proposed RSSI filtering algorithm achieves excellent signal filtering results in real road traffic environments. In addition, the constructed travel speed estimation algorithm outperforms other baseline models in four different scenarios (flat-peak walking, midday peak walking, flat-peak cycling, and midday peak cycling), achieving an overall classification accuracy of 97.92%. In summary, our Wi-CL system is a feasible approach for nonmotorized traffic travel mode classification.

Adaptive Scheme of Denoising Autoencoder for Estimating Indoor Localization Based on RSSI Analytics in BLE Environment

In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.

A novel scheme based on coarse‐grained localization and fine‐grained isolation for defending against Sybil attack in low power and lossy networks

AbstractDue to the lack of security mechanisms and sensor resources, the IPv6 Routing Protocol for Low power and Lossy networks (RPL) is vulnerable to various attacks, among which Sybil attack has received a lot of attention. This paper proposes a novel scheme based on coarse‐grained localization and fine‐grained isolation (CLFI) for defending Sybil attack. In CLFI, normal neighbor nodes are identified based on received signal strength indicator (RSSI). To further identify Sybil nodes, a physical address identifier called is defined, which is calculated as the difference of RSSI at two different receivers when they are receiving the same packet. In order to facilitate the calculation of , a set of anchor nodes are deployed to broadcast the received RPL control packets. Then Sybil behaviors can be detected by comparing the physical address identifier with the logical address embedded in the received packets. The proposed CLFI scheme has been completely implemented in the Contiki operating system. The extensive evaluation in comparison with the state‐of‐the‐art scheme indicates that CLFI can quickly localize and isolate Sybil nodes.

An environment safety monitoring system for agricultural production based on artificial intelligence, cloud computing and big data networks

Monitoring the agricultural production environment is crucial for optimal crop growth and resource efficiency. Cloud Computing, Artificial Intelligence (AI), and Big Data have revolutionized traditional agriculture, promising improved output and product quality. The popularity of these technologies drives their application in safety monitoring. This system facilitates data collection and transmission among equipment, overcoming challenges of traditional systems like investment, costs, and maintenance. In this paper, cloud computing-based AI optimization technology and big data network were proposed to monitor the safety of the agricultural production environment, and the shortcomings of traditional distance vector hop (DV hop) positioning algorithms were analyzed in depth. RSSI (Received Signal Strength Indication) technology improved the traditional DV Hop location method. The paper analyses direct and indirect transmission for data transmission between WSN and cloud nodes and favors indirect transmission because it consumes less invalid energy. Finally, the article compares several evaluations of alternative algorithms for monitoring system performance, including data transmission reliability, data reception rate, and data delay. The experimental results in this paper showed that in the data reception rate test, the data reception rate of System 2 was 97% at the lowest and 99% at the highest, both exceeding 95%.

NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio.

Robot localization is a crucial task in robotic systems and is a pre-requisite for navigation. In outdoor environments, Global Navigation Satellite Systems (GNSS) have aided towards this direction, alongside laser and visual sensing. Despite their application in the field, GNSS suffers from limited availability in dense urban and rural environments. Light Detection and Ranging (LiDAR), inertial and visual methods are also prone to drift and can be susceptible to outliers due to environmental changes and illumination conditions. In this work, we propose a cellular Simultaneous Localization and Mapping (SLAM) framework based on 5G New Radio (NR) signals and inertial measurements for mobile robot localization with several gNodeB stations. The method outputs the pose of the robot along with a radio signal map based on the Received Signal Strength Indicator (RSSI) measurements for correction purposes. We then perform benchmarking against LiDAR-Inertial Odometry Smoothing and Mapping (LIO-SAM), a state-of-the-art LiDAR SLAM method, comparing performance via a simulator ground truth reference. Two experimental setups are presented and discussed using the sub-6 GHz and mmWave frequency bands for communication, while the transmission is based on down-link (DL) signals. Our results show that 5G positioning can be utilized for radio SLAM, providing increased robustness in outdoor environments and demonstrating its potential to assist in robot localization, as an additional absolute source of information when LiDAR methods fail and GNSS data is unreliable.

Indoor WiFi-Beacon Dataset Construction Using Autonomous Low-Cost Robot for 3D Location Estimation

Datasets used for artificial-neural-network and machine-learning applications play a vital role in the research and application of such techniques in solving real-life problems. The construction and availability of large datasets to be used in the off-line phase of ANN training is usually a crucial and time-consuming step towards system construction. In this work, a framework for autonomous construction of a diverse, extensive, and open dataset* with built-in redundancy is demonstrated. As part of the framework, a low-cost robot using off-the-shelf components is built that constructs the dataset autonomously. The robot includes a controller network with multiple WiFi-transceiver nodes for collecting received-signal-strength indicators (RSSIs) at various elevation points throughout the building. All nodes are configured with direct internet access to streamline the data collection towards an online database that is constructed as part of this framework. Preliminary validation and analysis of the dataset are discussed, and an exploration of the application domain of the dataset is carried out. Moreover, this paper investigates the effect of the height of the hand-held mobile WiFi antenna attached to the robot on the received power strength of the WiFi signal.

Overtaking Assistance System based on Signal Strength to Prevent Accidents in V2V Communication Environment

The directional antennas with received signal strength indicator (RSSI) output from 0.52 V to 1.34 V are designed as a receiving component. These antennas have the ability to rotate up to 180° to find the maximum signal strength angle. RSSI signal strength is converted as customized measurement unit as Analog Reading Units (ARU) which varies from 0ARU to 400ARU and signal receiver has −90 dBm receiving sensitivity. The experimental results demonstrate that, the variance of RSSI with speed of a vehicle shows a clear relationship at the position of signal receiving point. Two regions are defined as effective region and non-effective region to show the capability of the proposed model. Effective region is of 60 m range from one vehicle to the other vehicle which plays a role in the overtaking process. When a vehicle is detected within the range, the RSSI value is greater than 150ARU. When the distance is less than 160 m, the RSSI value will be greater than 200ARU. Results prove that the overtaking status of a vehicle is clearly identified as zones and sectors ranging from 1 m to 160 m.

An improved filtering algorithm for indoor localization based on DE-PSO-BPNN

Among the indoor localization algorithms, the algorithm based on traditional Back Propagation Neural Network (BPNN) has the problems of slow convergence and easy to fall into local optimum. It is difficult to apply the algorithm in noisy environments. Therefore, in this paper, we propose a novel indoor localization algorithm where the whole localization process is divided into two parts: data preprocessing and localization output. Data preprocessing means using filtering algorithm to process the Received Signal Strength Indication (RSSI) sequence. It is considered that the initial value of the received sequence has a significant impact on the performance of Kalman Filter (KF). An improved Kalman Filtering algorithm (DBSCAN-KF) is proposed based on the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. First, the RSSI values that are seriously disturbed by noise in the sequence are removed using the DBSCAN algorithm, and then the RSSI sequences are processed using KF so that the RSSI values can be closer to the theoretical values. The localization output part is to reduce the localization error caused by the BPNN. In this paper, the Differential Evolution (DE) algorithm and Particle Swarm Optimization (PSO) algorithm are combined, and the Differential Evolution Particle Swarm Optimization (DE-PSO) algorithm is proposed. The BPNN weights and thresholds are optimized in parallel, which improves the speed and ability of global optimization search and further avoids the shortcomings of traditional BPNNs that are prone to fall into local optimization in the training process. Experimental results show that the BPNN localization algorithm based on DBSCAN-KF improves the average localization accuracy by 0.26m compared with the BPNN localization algorithm without filtering. After filtering, the localization algorithm based on DE-PSO improved BPNN (DE-PSO-BP) improves the average localization accuracy by about 24% compared with the localization algorithm based on DE-PSO-BP. The localization algorithm based on DE-PSO-BP improves the average localization accuracy by about 61% compared with the traditional BPNN.

TFUZZY-OF: a new method for routing protocol for low-power and lossy networks load balancing using multi-criteria decision-making

The internet of things (IoT) based on a network layer perspective includes low-power and lossy networks (LLN) that are limited in terms of power consumption, memory, and energy usage. The routing protocol used in these networks is called routing over low-power and lossy networks (RPL). Therefore, the IoT networks include smart objects that need multiple routing for their interconnections which makes traffic load balancing techniques indispensable to RPL routing protocol. In this paper, we propose a method based on fuzzy logic and the technique for the order of prioritization by similarity to the ideal solution (TOPSIS) as a well-known multi-criteria decision-making method to solve the load balancing problem by routing metrics composition. For this purpose, a combination of both link and node routing metrics namely hop count, expected transmission count, and received signal strength indicator is used. The results of simulations show that this method can increase the quality of services in terms of packet delivery ratio and average end-to-end delay.

Federated Kalman Filter for Secure IoT-Based Device Monitoring Services

Device monitoring services have increased in popularity with the evolution of recent technology and the continuously increased number of Internet of Things (IoT) devices. Among the popular services are the ones that use device location information. However, these services run into privacy issues due to the nature of data collection and transmission. In this work, we introduce a platform incorporating Federated Kalman Filter (FKF) with a federated learning approach and private blockchain technology for privacy preservation. We analyze the accuracy of the proposed design against a standard Kalman Filter (KF) implementation of localization based on the Received Signal Strength Indicator (RSSI). The experimental results reveal significant potential for improved data estimation for RSSI-based localization in device monitoring.

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era.

The Internet-of-Things (IoT) has been used with greater frequency to track peoples' daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.