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This paper introduces a review article on indoor localization techniques and technologies. The paper starts with current localization systems and summarizes comparisons between these systems in terms of accuracy, cost, advantages, and disadvantages. Also, the paper presents different detection techniques and compare them in terms of accuracy and cost. Finally, localization methods and algorithms, including angle of arrival (AOA), time of arrival (TOA), and recived signal strength (RSS) are introduced. The study contains concepts, requirements, and specifications for each category of methods presents pros and cons for investigated methods, and conducts comparisons between them.

Finding the reliable locations of radio devices using spatially separated sensor nodes (knows as “anchor nodes” whose positions are known in advance) is an important issue in the next-generation radio networks. The existing state-of-the-art localization techniques based on received signal strength (RSS), time of arrival (TOA) and time difference of arrival (TDOA) are suffering from inaccurate measurements due to multipath signals, interference, noise, lack of clock synchronization, high power consumption, and computational complexity. This paper presents a highly accurate and simplified versions of TOA, TDOA-based localization techniques that suit the resource constraint applications of industrial internet of things (IIoT) and towards 6G communication systems. The proposed TOA, TDOA techniques are based on orthogonal frequency division multiplexing (OFDM) channel estimation that ensures less susceptible to multipath effects, independent of synchronization errors, and reliable location estimations. Simulations were conducted using fast Fourier transform (FFT) and multiple signal classification (MUSIC) spectrum analysis methods at higher frequency bands under noisy environments and results were compared with the performance under ideal conditions. The localization accuracies are presented in terms of root mean square (RMS) errors for different noise figures, bandwidths, and their performance is analysed. The results proved that both the simplified versions of TOA and TDOA based localization provide accurate location values of sensor nodes even in noisy environments, and they are very close to the actual location values.

In this article, a human activity recognition system based on Wi-Fi signal strength variation (SSV) has been proposed. This strategy is built by exploiting the known fact that radio signal significantly reacts when it interfaces with the human body by causing fading and shadowing effects. Different irregularities in the received signal strength indicator (RSSI) propagation patterns indicate individual human activities. In the proposed method, utilizing the received RSSIs from various access points (APs) of known locations to the smartphone carried by a human, first, the position of the human is localized with the distances utilizing half the number of APs’ based on the strong RSSI values. Then, using the strongest RSSIs of the nearest AP, the activity of the human is recognized using the changing signal strengths. To accurately measure the monotonic distances by the RSSI values, the regression analysis technique (RAT) is used in the path loss model (PLM) to mitigate error significantly. Besides, to classify human activities, we calculate the deviation between any human activity and no human. Moreover, we arrange all activities in a successive order. With this infrastructure, we can develop a system where both human localization and activity recognition can be done within a single setup, which not only detects the position of a person on the floor but also produces the health condition of each person staying on the floor. In the existing methods, wirable devices are used to detect human activities, which creates irritations when they have to carry some heavy electronic device attached to their body. Moreover, these devices are expensive. On the other hand, channel state-based solutions have some advantages over wirable systems, but this technology does not support in major smartphones. So, in this work, to overcome such challenges, we have focused on an RSSI-based framework that does not need to wear electronic devices on the body as well as supports every smartphone. So, with a simple setup, the system can be operated. Our system can successfully recognize at most five activities simultaneously for the presence of the same humans in the experimental indoor premises. Such an approach enhances the interactions in intelligent healthcare systems.

Node localization is an essential aspect of wireless sensor networks (WSNs). There are mainly two types of localization algorithms used to compute the position of the node, namely range-based and range-free algorithms. Range-based localization algorithms have some hardware requirements, so they are usually expensive to implement in practice. Range-free localization algorithms are less costly for hardware, but they achieve poor localization accuracy in the real-world environment. This paper uses the simple principle of the range-free DV-hop algorithm and the less expensive range-based algorithm using received signal strength indicator (RSSI) measurement to locate unknown nodes in WSN. First, a new RSSI-based localization algorithm called disk-based multilateration (DML) is proposed to extend the well-known multilateration algorithm. Indeed, each RSSI value is associated with a distance interval used to model the imperfections of RSSI measurements. The distance interval is represented by a disk defined according to the position of the signal’s transmitter node. Then, two other algorithms that take advantage of both types of localization are proposed by combining the DV-hop and DML algorithms, namely DV + DML and DMLDV. They are evaluated in simulation on testbeds derived from a real-world RSSI measurement dataset. The obtained simulation results show that the performance of the proposed algorithms is superior to the DV-hop algorithms in the considered scenarios without requiring additional hardware and computational costs.

This paper investigates the hybrid source localization problem using the four radio measurements - time of arrival (TOA), time difference of arrival (TDOA), received signal strength (RSS), and angle of arrival (AOA). First, after invoking tractable approximations in the RSS and AOA models, the maximum likelihood estimation (MLE) problem for the hybrid TOA-TDOA-RSS-AOA data model is derived. Then a weighted least-squares problem is formulated from the MLE, which is solved using the principle of the majorization-minimization (MM), resulting in an iterative algorithm with guaranteed convergence. The key feature of the proposed method is that it provides a unified framework where localization using any possible merger out of these four measurements can be implemented as per the requirement/application. Extensive numerical simulations are conducted to study the performance of the proposed method. The obtained results indicate that the hybrid localization model improves the localization accuracy compared to the heterogeneous measurements under different network scenarios, which also includes the presence of non-line of sight (NLOS) errors.

The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.

A smart thinning operation refers to an advanced method of selecting and cutting trees to be thinned based on digitally captured forest information. In smart thinning operations, workers use the coordinates of individual trees to navigate to the target trees for thinning. However, it is difficult to accurately locate individual trees in a forest stand covered with a canopy, necessitating a precise real-time positioning system that can be used in the forest. Therefore, this study aimed to evaluate the applicability of the global navigation satellite system real-time kinematic (GNSS-RTK) device in a forest stand through analysis of its positioning accuracy within the forest environment and evaluation of the operational range of the single-baseline RTK based on analysis of the positioning precision and radio signal strength index (RSSI) change with increasing distance from the base station. The results showed that the root mean square error (RMSE) of the horizontal positioning error was highly accurate, with an average of 0.26 m in Larix kaempferi stands and 0.48 m in Pinus koraiensis stands. The RSSI decreased to a minimum of −103.3 dBm within 1 km of distance from the base station; however, this had no significant impact on the horizontal positioning precision. The conclusion is that the GNSS-RTK is suitable for use in smart thinning operations.

This study proposes a mobile positioning method that adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., two timestamps and three timestamps); from the experimental results, it can be observed that the average error of location estimation was 9.19 m by the proposed mobile positioning method with two timestamps.

Wireless Fidelity (Wi-Fi) based localization suffers from multipath propagation, signal interference, and signal loss. It affects the precise distance estimation in localization. In localization based applications, accurate distance measurement is the major challenge. This research work proposes a Grid Search approach for enhancing the accuracy of indoor localization in Wi-Fi based Internet of Things (IoT) environments. Grid Search systematically explores hyperparameter combinations, to find the optimal configuration settings for machine learning models. Hyperparameter optimization is essential as it enhances model performance by fine-tuning. In this research work, three distinct models are employed: Support Vector Regressor (SVR), k-Nearest Neighbors (KNN) and Random Forest (RF). The Grid Search approach for hyperparameter tuning is employed to train each model. It utilizes Euclidian Distance (ED) measurement and Received Signal Strength Indicator (RSSI) data. The model’s performance is evaluated using performance metrics. The Grid Search approach with the RF achieved an optimal Mean Absolute Error (MAE) of 0.8258 m. The validation of performance through comparative result analysis with existing research work underscored the effectiveness of the approach. In Scenario 1, remarkable optimal improvement of 54.92% is observed. Similarly, in Scenario 2 and Scenario 3, significant enhancements of 29.38% and 24.09% are obtained respectively. Grid Search with SVR showed superior performance, producing Mean Squared Error (MSE) 1.0826 meter 2 . This highlighted the robustness and superiority of the proposed approach in improving models performance over existing research.

Wireless communications between two devices can be protected by secret keys. However, existing key generation schemes suffer from the high bit disagreement rate and low bit generation rate. In this paper, we propose an efficient physical layer key generation scheme by exploring the Received Signal Strength (RSS) of signals. In order to reduce the high mismatch rate of the measurements and to increase the key generation rate, a pair of transmitter and receiver separately apply adaptive quantization algorithm for quantifying the measurements. Then, we implement a randomness extractor to further increase key generation rate and ensure randomness of generated of keys. Several real-world experiments are implemented to verify the effectiveness of the proposed scheme. The results show that compared with the other related schemes, our scheme performs better in bit generation rate, bit disagreement rate, and randomness.