Received Signal Strength Values Research Articles

A fingerprint dictionary processing approach in indoor localization system based on wi-fi

Indoor localization using Wi-Fi fingerprinting based on Received Signal Strength (RSS) has gained widespread attention due to its immunity to external factors and ability to penetrate obstacles. The localization process involves an offline phase for building a radio map and an online phase for matching location queries. Existing matching algorithms often prioritize enhancing online phase accuracy, overlooking the importance of offline data preprocessing, which can negatively impact overall performance. This study introduces a novel approach called Fingerprint Dictionary Preprocessing (FDP) that employs Convolutional Dictionary Learning (CDL) to process radio map data. CDL learns a set of kernels capturing site characteristics, representing RSS values from Access Points (APs) in a sparse manner. The proposed FDP system compresses data through feature learning, reducing storage requirements for data transmission. In the online phase, CDL is utilized for assisting matching fingerprints against the learned dictionary, accurately locating users. The contributions of the FDP system presenting a cost-effective and practical solution for indoor localization, addressing the challenges associated with large data collection and multi-dimensional data requirements, making it a promising approach for real-world applications. We conducted experiments in two real indoor environments, and the results indicated that the proposed FDP system, whether applied to the original radio map or the preprocessed fingerprint database, led to improved localization accuracy and reduced localization time.

A Multi-Objective Improved Hybrid Butterfly Artificial Gorilla Troop Optimizer for Node Localization in Wireless Sensor Groundwater Monitoring Networks

Wireless sensor networks have gained significant attention in recent years due to their wide range of applications in environmental monitoring, surveillance, and other fields. The design of a groundwater quality and quantity monitoring network is an important aspect in aquifer restoration and the prevention of groundwater pollution and overexploitation. Moreover, the development of a novel localization strategy project in wireless sensor groundwater networks aims to address the challenge of optimizing sensor location in relation to the monitoring process so as to extract the maximum quantity of information with the minimum cost. In this study, the improved hybrid butterfly artificial gorilla troop optimizer (iHBAGTO) technique is applied to optimize nodes’ position and the analysis of the path loss delay, and the RSS is calculated. The hybrid of Butterfly Artificial Intelligence and an artificial gorilla troop optimizer is used in the multi-functional derivation and the convergence rate to produce the designed data localization. The proposed iHBAGTO algorithm demonstrated the highest convergence rate of 99.6%, and it achieved the lowest average error of 4.8; it consistently had the lowest delay of 13.3 ms for all iteration counts, and it has the highest path loss values of 8.2 dB, with the lowest energy consumption value of 0.01 J, and has the highest received signal strength value of 86% for all iteration counts. Overall, the Proposed iHBAGTO algorithm outperforms other algorithms.

Optimization of received signal strength in visible light communication using the combination of heuristic algorithms

SummaryLEDs are a prominent technique for a pervasive communication system using visible light communication (VLC), since LED includes long life expectancy, fast switching, and being visible light and is less expensive, which is safe for the human body, though still many technical problems presented to be addressed, especially in an indoor environment. Due to this, the major aim of this task is to afford a hybridized optimization algorithm that is highly suitable for enabling the signal to noise ratio (SNR) and received signal strength (RSS) maximization in VLC. Here, the coordinate of each point of the receiver photo detector in the VLC system is tuned by the new hybridized algorithm. The fusion of two optimization algorithms like Deer Hunting Optimization Algorithm (DHOA) and Dragonfly Algorithm (DA) termed as Fitness‐oriented Dragonfly‐based DHOA (FD‐DHOA) is used to attain the objective concerning the RSS or received power and SNR. The proposed model tries to attain maximum RSS and SNR at the receiver instead of calculating it at every angle. Moreover, the experimental analysis proves that the developed model validates the orientation that generates an optimum RSS and SNR value at the receiver.

Indoor Geo-Indistinguishability: Adopting Differential Privacy for Indoor Location Data Protection

Due to the extensive applicability of Location-Based Services (LBSs) and the Global Navigation Satellite System (GNSS) failure in indoor environments, indoor positioning systems have been widely implemented. Location fingerprinting, in particular, collects the Received Signal Strength (RSS) from users' devices, allowing Location Service Providers (LSPs) to precisely identify their locations. Therefore, LSPs and potential attackers have implicit access to this sensitive data, violating users' privacy. This issue has been addressed in outdoor environments by introducing Geo-indistinguishability (GeoInd), an alternative representation of Differential Privacy (DP). In indoor environments, however, the user lacks their coordinates, posing a new difficulty. This paper presents a novel framework for implementing GeoInd for indoor environments. The proposed framework introduces two distance calculation and RSS generation methods based solely on RSS values. Moreover, involving other participants or trusted third parties is not necessary to protect privacy, regardless of the attackers' prior knowledge. The proposed framework is evaluated in a simulated environment and two experimental settings. The results validate the proposed framework's efficiency, effectiveness, and applicability in indoor environments under the GeoInd setting.

A CrowdSensing-based approach for proximity detection in indoor museums with Bluetooth tags

In this work, we investigate the performance of a proximity detection system for visitors in an indoor museum exploiting data collected from the crowd. More specifically, we propose a CrowdSensing-based technique for proximity detection. Users’ smartphones can collect and upload RSS (Received Signal Strength) values of nearby Bluetooth tags to a backend server, together with some context-information. In turn, the collected data are elaborated with the goal of calibrating two proximity detection algorithms: a range-based and a learning-based algorithm. We embed the algorithms with R-app, a visiting museum application tested in the Monumental Cemetery’s museum located in Piazza dei Miracoli, Pisa (IT). We detail in this work an experimental campaign to measure the performance improvements of the CrowdSensing approach with respect to state-of-the-art algorithms widely adopted in the field of proximity detection. Experimental results show a clear improvement of the performance when data from the crowd are exploited with the proposed architecture.

Machine learning techniques for received signal strength indicator prediction

The advances made in wireless communication technology have led to efforts to improve the quality of reception, prevent poor connections and avoid disconnections between wireless and cellular devices. One of the most important steps toward preventing communication failures is to correctly estimate the received signal strength indicator (RSSI) of a wireless device. RSSI prediction is important for addressing various challenges such as localization, power control, link quality estimation, terminal connectivity estimation, and handover decisions. In this study, we compare different machine learning (ML) techniques that can be used to predict the received signal strength values of a device, given the received signal strength values of other devices in the region. We consider various ML methods, such as multi-layer ANN, K nearest neighbors, decision trees, random forest, and the K-means based method, for the prediction challenge. We checked the accuracy level of the learning process using a real dataset provided by a major national cellular operator. Our results show that the weighted K nearest neighbors algorithm, for K = 3 neighbors, achieved, on average, the most accurate RSSI predictions. We conclude that in environments where the size of data is relatively small, and data of close geographical points is available, a method that predicts the coverage of a point using the coverage near geographical points can be more successful and more accurate compared with other ML methods.

PERFORMANCE ANALYSIS AND DEVELOPMENT OF PATH LOSS MODEL FOR TELEVISION SIGNALS IN IMO STATE, NIGERIA

It is impossible to overstate the importance of propagation models in wireless network planning, frequency assignment, and television parameter evaluation. The fact that no two locations are identical in terms of climatic conditions, building patterns, terrain, etc. makes using path loss predicting models for any area extremely challenging. Therefore, it is impossible to develop a single path loss model that applies to all environmental settings. The main aim of this study is to develop a path loss model for NTA channel 12 Owerri and evaluate its performance based on received signal strength values along five selected routes in Imo State, Nigeria.A suitable path loss model was developed by critically analyzing the measured path loss values of each base station, which were retrieved from the signal strength data received. The values of the developed path loss model were compared to those of other empirical path loss models developed by other researchers as well as the measured path loss values. The results show that the proposed path loss model is well suited for predicting the path loss of NTA channel 12 Owerri signals in the study environment, while the other conventional empirical models taken into consideration in this study overestimated the path loss of NTA channel 12 Owerri signals with Root Mean Square Error and Mean Error of 63.65 and above. Additionally, the findings indicate that NTA Owerri performs poorly at a distance of 18 kilometers from the base transmitting station. The overall findings are helpful for designing prospective television network channels in the study location and other similar environments.

Cramér–Rao Lower Bound Analysis of Differential Signal Strength Fingerprinting for Crowdsourced IoT Localization

Crowdsourcing is considered an efficient and promising paradigm for constructing large-scale signal fingerprint radio maps due to the proliferation of Wi-Fi-enabled devices. However, a crowdsourced Indoor Positioning System (IPS) has to handle diverse devices and the inherent heterogeneity in Received Signal Strength (RSS) measurements. To address the device heterogeneity problem, differential fingerprinting methods have been explored, which mitigate the device characteristics that cause RSS from different commercial devices to report differently. In this paper, we focus on Mean Differential Fingerprinting (MDF) that produces the differential fingerprints by subtracting the mean RSS value of all APs from the original RSS fingerprints. We study the localization performance of the MDF method by means of the Cramér-Rao Lower Bound (CRLB) and show analytically that it outperforms another method that addresses device diversity. Furthermore, we evaluate the localization accuracy of existing solutions using real-life Wi-Fi RSS datasets collected by multiple consumer devices. The experimental results confirm our analytical findings and demonstrate the effectiveness of the MDF method to mitigate device diversity, as well as other factors that affect the RSS readings including the device carrying mode and power control schemes of the Wi-Fi infrastructure, thus contributing to the wider adoption of crowdsourced IPS.

Investigative Assessment of the Impact of Perturbations on the Digital Terrestrial Television Broadcasting Signal

This study examines the effects of perturbations on the digital terrestrial television broadcasting (DTTB) signal in Jos, Plateau State, Nigeria, using measurement-based performance analysis. In order to examine the effects of environmental perturbations on DTTB signal, measurement campaign used the Nigerian Television Authority, Integrated Television Services (ITS) digital video broadcasting -second generation (DVB-T2) signal. The aim of the research was to identify the environmental and atmospheric perturbations with highest impact on DTTB signal. Field measurement was accurately obtained throughout the measurement campaign using the E8000 series spectrum analyzer, a DVB-T2 receiver, and other facilities in order to actualize the two important measurement characteristics, such as field strength and received signal strength (RSS). The outcome showed a significant amount of dips in route A, depriving towns surrounding rocky hills of a DVB-T2 signal, as demonstrated in the Du community with RSS values of -87.1 dBm, 85.2 dB , and -27.8 dB V/m, -23.9 dB V/m for field strength. Additionally, the results show that signal performance was better in the dry season compared to the rainy season (more primary coverage in the dry season compared to the rainy season), with the RSS value being -41.6 dBm versus -36.4 dBm and the field strength value being 34 dB/m versus 38.6 dB/m for route A, or the same on route B. Solving signal propagation issues requires understanding the fundamental metrics driving an increase in perturbations in any broadcast environment. Creating a model to address these issues will help with system evaluation, network planning, and broadcast service operation, ensuring quality of service (QoS).

Landmark-Based Indoor Positioning: a Non-Intrusive and Cost-Effective Approach

This paper proposes a method for identifying indoor landmarks that can serve as fingerprints in indoor environments. The reason for this proposal is that every building has several distinctive locations. These signatures will be used as positional markers if they can be found. Landmarks such as Wi-Fi, entrance, windows, and elevators are recognizable. The proposed method detects these landmarks as follows: Wi-Fi Received Signal Strength (RSS) is used to detect the Wi-Fi landmark. The difference between GPS signals and Wi-Fi RSS values is used to locate the entrance landmark. GPS signals, though not available indoors, can be detected near windows and used to locate windows. Elevators are identified using two criteria: user movement behavior and loss of Wi-Fi RSS. The accuracy of detecting Wi-Fi, entrance, and window landmarks is 2 m, while the accuracy of detecting the elevator landmark is 1 m.

Minimization of Handover Decisions with Quality of Service Using Fuzzy Logic Prediction Model

Artificial Intelligence (AI) based network technologies considered best method to enhance the Quality of Service (QoS) of handoff algorithms due to its ability to handle huge data in fast processing. It helps to take effective handoff decision based on Received Signal Strength (RSS), traffic intensity, speed and diversity. In this paper the fuzzy logic prediction model has been developed for handoff decisions. On retrieving the network, the RSS was developed to form a time series data over a period of time. The data is then proceeded with the newly proposed fuzzy logic prediction model for estimation and prediction coefficients, while the predicted values of RSS are organized as fuzzy sets and in conjunction with other measured parameters of network. Moreover, the Received Signal Strength Indicator (RSSI), traffic load in the network, channel capacity, network load (NL), Bit Error Rate (BER), received signal power level has been estimated throughput the Signal to Noise Ratio (SNR), In addition, to user preferences such as the security and cost of the network. The overall performance of proposed fuzzy logic prediction model is capable to predict the handover decision ahead then the available RSS method and other handover necessity estimation techniques. This model also reduces the ping-pong effect associated with other techniques of handover.

A WLAN indoor location method based on quadratic wavelet analysis

The drift phenomenon of received signal strength may occur due to the influence of environmental change, which will affect the results of wireless local area network indoor positioning. This paper proposes an indoor location method based on quadratic wavelet analysis to solve this problem. The first wavelet analysis can obtain wavelet coefficients. The threshold interval of wavelet coefficients can be calculated by Tukey's test method, in which the mutation signal value can be filtered according to the interval. The drift signal value cannot be detected due to the limitation of primary wavelet analysis. The filtered received signal strength values can be analysed with quadratic wavelet analysis. The drift signal value can be found and filtered out by the mutation signal to obtain the new dataset of the received signal strength. The experimental results show that the influence of signal drift can be greatly reduced by the method of quadratic wavelet analysis proposed in this paper; the positioning accuracy error is lower than the traditional filtering algorithm; and the positioning accuracy can be improved.

Supplementary open dataset for WiFi indoor localization based on received signal strength

Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction.

A real-time fingerprint-based indoor positioning using deep learning and preceding states

In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase.In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN.

A Novel Valued Tolerance Rough Set and Decision Rules Method for Indoor Positioning Using WiFi Fingerprinting.

In recent years, due to the ubiquitous presence of WiFi access points in buildings, the WiFi fingerprinting method has become one of the most promising approaches for indoor positioning applications. However, the performance of this method is vulnerable to changes in indoor environments. To tackle this challenge, in this paper, we propose a novel WiFi fingerprinting method that uses the valued tolerance rough set theory–based classification method. In the offline phase, the conventional received signal strength (RSS) fingerprinting database is converted into a decision table. Then a new fingerprinting database with decision rules is constructed based on the decision table, which includes the credibility degrees and the support object set values for all decision rules. In the online phase, various classification levels are applied to find out the best match between the RSS values in the decision rules database and the measured RSS values at the unknown position. The experimental results compared the performance of the proposed method with those of the nearest-neighbor-based and the random statistical methods in two different test cases. The results show that the proposed method greatly outperforms the others in both cases, where it achieves high accuracy with 98.05% of right position classification, which is approximately 50.49% more accurate than the others. The mean positioning errors at wrong estimated positions for the two test cases are 1.71 m and 1.99 m, using the proposed method.

A fingerprint-based localization algorithm based on LSTM and data expansion method for sparse samples

The accuracy of WiFi fingerprint-based localization is related to the number of reference points, generally, to obtain better positioning accuracy, enough samples must be collected, which will inevitably lead to a huge sampling workload. Thus, it will be of great significance to design an algorithm using sparse samples to achieve positioning accuracy like that of dense samples. This paper proposes a WiFi fingerprint-based localization algorithm using Long Short-Term Memory Network (LSTM) with explainable feature and a sparse sample expansion algorithm (PGSE) based on Principal component analysis and Gaussian process regression for sparse samples. Specifically, in the case of limited number of collected reference points, principal component analysis is used to select the access point, and Gaussian process regression is used to model the reference point coordinates and the corresponding received signal strength values in the training sample set, to expand the signal data and construct a new fingerprint database. The effectiveness of the PGSE algorithm is verified by using the public dataset ’UJIIndoorLoc’. At the same time, the applicability of PGSE expansion algorithm to data with temporal information is verified in the fingerprint-based localization method. In addition, this paper also proposes a WiFi-RSSI indoor localization method based on Long Short-Term Memory Network. Lots of experiments are conducted in the actual scenes and the results are compared with several existing methods. The results indicate that the proposed method improves the precision of indoor localization on an average level compared to state-of-the-art methods.

RSS based multistage statistical method for attack detection and localization in IoT networks

Accurate and reliable positioning of nodes is a must for location-based services (LBS) in the Internet of things (IoT) networks. The LBS are ubiquitous and an easy target for non-cryptographic attacks that traditional security methods cannot address. In this work, we detect the Received Signal Strength (RSS) based attacks that affect the localization of smart devices in the IoT networks and report the attack tolerance of popular IoT protocols. A two-tier ratio metric method and Residue Under Curve (RUC) metric method is utilized to detect the malicious node in the IoT protocols. We propose to use a novel Geometric, and Arithmetic Mean (GM–AM) ratio as a feature to detect the RSS attacks where GM follows strictly Schur-Concavity property and AM follows non-strict concavity property. We evaluate the performance of the proposed method on real-world IoT testbeds with Wireless Fidelity (Wi-Fi), Zigbee, Bluetooth Low Energy (BLE), and Long-Range Wide-Area Network (LoRaWAN) protocols using the RSS values of these opportunistic signals. Also, the effect of RSS attacks on the localization for different protocols is investigated, and we report the method that provides the least localization error under these attacks.

Improving Weighted Multiple Linear Regression Algorithm for Radiolocation Estimation in LoRaWAN

In location based services, Weighted Multiple Linear Regression (WMLR) algorithm is used for radio device position estimation. Nevertheless, WMLR provides coarse location estimate, because weights apportioned to the received signal strength (RSS) for each hearable base station during matrix weight formation are not properly distributed. In an attempt to address the problem articulated above, an improved WMLR that enhanced the accuracy of radio device position estimate is proposed in this work. Min-Max scaling was used to determine the weight for each RSS values logged at different BS, as such forming a refined matrix weight. Public on-site outdoor Long Range Wide Area Network (LoRaWAN) RSS data set was used to assess the improved WMLR estimation algorithm on the basis of accuracy. The location accuracy of the proposed method is validated with the existing WMLR algorithm and Federal Communication Commission (FCC) maximum location error benchmark. Results show that the location accuracy of the improved approach outperformed that of the existing WMLR localization method.

Received Signal Strength Estimation in Indoor Environment Using High Frequency Rytov Approximation

We present a new and improved formulation of Rytov approximation (RA) using the theory of ray tracing in low loss media for solving the direct scattering problem. The resultant model provides an accurate prediction of wave scattering in the presence of electrically large scatterers with high permittivity and small loss tangent. The high validity range and the straightforward linear formulation of the proposed model make it suitable for accurately predicting the received signal strength (RSS) in indoor environments. We provide experimental results from a real indoor environment to show that the proposed method closely matches the experimental RSS values and achieves up to ten times higher validity range than any other existing linear approximate models. Also, since the proposed method provides an accurate solution to the direct scattering problem, it can be an ideal candidate for inverse scattering applications.

An improved genetic algorithm approach to spectrum sensing for long range based cognitive radio networks

AbstractBy designing cognitive radio networks (CRNs) for low power wide area networks (LPWANs), adaptive spectrum sensing plays an important role in using frequency bands effectively. With spectrum sensing, existing spectrum holes are filled by enabling secondary users to use the licensed band that primary users do not use. Many researchers have proposed various optimization or artificial intelligence techniques in CRNs for spectrum sensing. However, there is hardly any adaptive spectrum sensing method integrated into the LoRa communication standard, which is a very popular LPWAN technology. For this purpose, an adaptive optimization based spectrum sensing methodology with an improved genetic algorithm is proposed in this study. Depending on this methodology, two fitness functions adapted to the LoRa‐based network are defined. Thanks to these functions, the movement of individuals in the network population is provided. In parallel, received signal strength values of optimum LoRa nodes were determined by genetic coding, and bands to be used by SUs instead of PUs were discovered considering minimum bit errors. To verify the success of the proposed method, various simulation experiments were carried out. The performance results strikingly show that the proposed method is quite successful for LoRa‐CRNs. For example, the proposed method significantly improved the bit error rate performance, and reduced the number of faulty transmissions caused by packet collisions. It also reduced the packet error rate by more than 8% per minute in a 200‐node network.