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Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology. [ABSTRACT FROM AUTHOR]

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

Summary: In wireless sensor networks (WSNs), target tracking has been prominently raised in recent days. Because of the frequent utilization of WSN, the attention on target tracking is greatly increased. The estimated optimal value derived from the earlier moment is rarely taken into consideration in traditional target‐tracking algorithms. One of the most crucial uses of WSNs is mobile target tracking, and it is especially used for spying. Precision surveillance is heavily dependent on localization or distance estimation, and extensive study has been done in this area. This research aims to develop a new target‐tracking network‐assisted target movement prediction model in WSN with reduced energy consumption. The major phases involved in the proposed target movement prediction in WSN are (a) mobility target tracking and (b) target movement prediction. Initially, the mobility target tracking is done with the help of adaptive distributed extended Kalman filtering (ADEKF). The mobility target tracking performance is improved by optimally tuning the parameters from ADEKF with the support of the improved squid game optimizer (ISGO). Then, the target movement prediction phase is executed with the help of input parameters like "Angle of Arrival (AoA) and Received Signal Strength (RSS)," and the optimal progress of the mobile node is predicted. The implementation outcome of the proposed tracking network‐assisted target movement prediction model is validated concerning various performance metrics. Overall performance analysis shows that the developed model offers 2.5% in terms of RMSE measures. The developed model shows better analysis while validating with existing approaches. [ABSTRACT FROM AUTHOR]

Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios. [ABSTRACT FROM AUTHOR]

In recent years, wireless sensor devices have become able to perform multiple functions such as detecting human sleep conditions, blood pressure, heartbeat, and running paths. We use the wireless channel model of a wearable Zigbee wireless sensing node to conduct research on human posture recognition. The received signal strength indicator (RSSI) obtained through the transmission and reception of wireless signals is used to obtain the model of the wireless channel. The wireless sensor nodes receive different RSSI patterns of human gesture based on which they recognize a gesture through their respective wireless channels by performing distance processing on the collected signal data. However, in this paper, we propose a weighted random reference pattern (WRRP) to achieve a higher recognition accuracy. Experimental results show that WRRP can achieve a recognition accuracy of 98%. [ABSTRACT FROM AUTHOR]

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions. [ABSTRACT FROM AUTHOR]

Summary: LEDs 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. [ABSTRACT FROM AUTHOR]

Indoor wireless positioning has long been a dynamic field of research due to its broad application range. While many commercial products have been developed, they often are not open source or require substantial and costly infrastructure. Academically, research has extensively explored Bluetooth Low Energy (BLE) for positioning, yet there are a noticeable lack of studies that comprehensively compare traditional algorithms under these conditions. This research aims to fill this gap by evaluating classical positioning algorithms such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Naïve Bayes (NB), and a Received Signal Strength-based Neural Network (RSS-NN) using BLE technology. We also introduce a novel method using Convolutional Neural Networks (CNN), specifically tailored to process RSS data structured in an image-like format. This approach helps overcome the limitations of traditional RSS fingerprinting by effectively managing the environmental dynamics within indoor settings. In our tests, all algorithms performed well, consistently achieving an average accuracy of less than two meters. Remarkably, the CNN method outperformed others, achieving an accuracy of 1.22 m. These results establish a solid basis for future research, particularly towards enhancing the precision of indoor positioning systems using deep learning for cost-effective, easy to set up applications. [ABSTRACT FROM AUTHOR]

Recently, the Underwater Wireless Sensor Network (UWSN) with movable nodes has been launched because it has a lot of potential uses in aquatic science and is getting a lot of attention from researchers. Because there are so many more possible underwater uses, it is important to make sure that signals can get from one underwater point to another. Because of the long lags in signal transmission and the changing speed of sound underwater, it is hard and not possible to use the usual localization method in UWSN. Node localization (NL) in UWSN tries to figure out where new nodes are by using known nodes. The correctness of location can have a big effect on how well a UWSN works. UWSN has a lot of trouble with precise NL. When it comes to WSN, NL problems mean figuring out where unknown sensor nodes (SNs) are. This shows how important it is to have a perfect NL system. The Enhanced Dwarf Mongoose Optimization based Node Localization Scheme (EDMO-NLS) for UWSN is being worked on in this study. The scout group, the alpha group, and the babysitters are the three social groups of dwarf mongooses that were used in the suggested method. The family hunts as a unit, and the main female starts hunting. This sets the sleeping mounds, the road for hunting, and the distance that is covered. The EDMO-NLS method shown here finds nodes whose locations are unknown in UWSN. The modeling results showed that the proposed model worked better than existing methods. Based on the findings and the discussion, it is clear that the suggested method has shown the best localization success in UWSN.

Visible light positioning (VLP) has drawn great attention in the field of indoor positioning as light communication has been popularized in low-cost Internet-of-Things (IOT) devices. In this paper, we investigate the VLP problem using the received signal strength (RSS) and by only considering the line-of-slight (LOS) propagation. The RSS-based VLP problem is highly nonlinear, and its solutions may be trapped in local optima without a good initial guess. To circumvent this difficulty, we propose closed-form solutions of the VLP problem considering a known or unknown user orientation. By applying the weighted least squares (WLS) method, the closed-form solutions are divided into two stages. In the stage-one WLS solution, the nonlinear VLP problem is transformed into a pseudo-linear form by introducing some auxiliary variables, which are considered to be independent of each other. The estimates of the stage-one WLS solution are further refined in the stage-two WLS solution by exploiting the constrained relationships among these defined variables. The simulation results show that the stage-two WLS solution provides good estimates for the user position and orientation. The proposed stage-two WLS solution outperforms the existing methods especially at a high signal-to-noise ratio (SNR). [ABSTRACT FROM AUTHOR]

The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources.

Indoor positioning is the key enabling technology for many location-aware applications. As GPS does not work indoors, various solutions are proposed for navigating devices. Among these solutions, Bluetooth low energy (BLE) technology has gained significant attention due to its affordability, low power consumption, and rapid data transmission capabilities, making it highly suitable for indoor positioning. Received signal strength (RSS)-based positioning has been studied intensively for a long time. However, the accuracy of RSS-based positioning can fluctuate due to signal attenuation and environmental factors like crowd density. Angle of arrival (AoA)-based positioning uses angle measurement technology for location devices and can achieve higher precision, but the accuracy may also be affected by radio reflections, diffractions, etc. In this study, a dual-branch convolutional neural network (CNN)-based BLE indoor positioning algorithm integrating RSS and AoA is proposed, which exploits both RSS and AoA to estimate the position of a target. Given the absence of publicly available datasets, we generated our own dataset for this study. Data were collected from each receiver in three different directions, resulting in a total of 2675 records, which included both RSS and AoA measurements. Of these, 1295 records were designated for training purposes. Subsequently, we evaluated our algorithm using the remaining 1380 unseen test records. Our RSS and AoA fusion algorithm yielded a sub-meter accuracy of 0.79 m, which was significantly better than the 1.06 m and 1.67 m obtained when using only the RSS or the AoA method. Compared with the RSS-only and AoA-only solutions, the accuracy was improved by 25.47% and 52.69%, respectively. These results are even close to the latest commercial proprietary system, which represents the state-of-the-art indoor positioning technology.

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

In recent years, Wi-Fi sensing technology has become an emerging research direction of human–computer interaction due to its advantages of low cost, contactless, illumination insensitivity, and privacy preservation. At present, Wi-Fi sensing research has been expanded from target location to action recognition and identity recognition, among others. This paper summarizes and analyzes the research of Wi-Fi sensing technology in human identity recognition. Firstly, we overview the history of Wi-Fi sensing technology, compare it with traditional identity-recognition technologies and other wireless sensing technologies, and highlight its advantages for identity recognition. Secondly, we introduce the steps of the Wi-Fi sensing process in detail, including data acquisition, data pre-processing, feature extraction, and identity classification. After that, we review state-of-the-art approaches using Wi-Fi sensing for single- and multi-target identity recognition. In particular, three kinds of approaches (pattern-based, model-based, and deep learning-based) for single-target identity recognition and two kinds of approaches (direct recognition and separated recognition) for multi-target identity recognition are introduced and analyzed. Finally, future research directions are discussed, which include transfer learning, improved multi-target recognition, and unified dataset construction. [ABSTRACT FROM AUTHOR]

Non-cooperative target tracking technology has significant applications in the field of ocean, serving both military and civilian purposes. The emerging underwater acoustic sensor networks (UASNs) have great potential for target tracking, but face challenges that include energy constraints, stratification effect, variable environment, and measurement outliers. To address these issues, a non-cooperative target tracking method based on UASNs is proposed in this paper. First, a mutual information (MI) function is established with the predicted target state and the received signal strength. Then, an MI-based active tracking node selection method is designed to reduce energy consumption and ensure tracking accuracy. After identifying active tracking nodes, a ray tracking method is used to calculate the distances under stratification effect. Subsequently, a robust and adaptive tracking algorithm is designed based on interactive multiple models assisted unscented Kalman filter to achieve accurate tracking, in which the equivalent noise covariance is utilized to change measurement noise adaptively, and a detector is implemented to detect and correct measurement outliers. The motion model transition probability is also adaptively adjusted based on the corrected data. Finally, the simulation and experimental results verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Target localization in a wireless sensor network (WSN) has received more and more attention in recent years, and has promoted many new applications due to the low cost, low bandwidth, low energy consumption, and collision avoidance mechanism. How to provide accurate location information has always been a hot research topic in 5G/B5G application scenarios. In this paper, the path loss information or received signal strength (RSS) of the received signal is considered in a WSN for the extended Kalman filter (EKF) to realize trajectory tracking of multiple targets, and the tracked targets are then localized through multiple sensors. Moreover, since there may be several objects or clutter interference in the communication environment, in order to reduce the impact of interference, we consider the probabilistic data association filter (PDAF) or probability hypothesis density filter (PHDF) to improve the tracking performance. Each sensor sends the received distance estimation information to the fusion center (FC), which calculates the optimal position for each target. Through simulation results, the proposed weighted least squares (WLS) trilateration method in this paper can effectively improve the average root mean squared error (RMSE) performance as sensors are evenly distributed around the tracking trajectories. [ABSTRACT FROM AUTHOR]

Aiming at the problem that traditional wireless sensor networks produce errors in the positioning and tracking of motorised targets due to noise interference, this paper proposes a motorised target tracking method with a convolutional bi-directional long and short-term memory neural network and extended Kalman filtering, which is trained by using the simulated RSSI value and the actual target value of motorised targets collected from the convolutional bi-directional neural network to the sensor anchor node, so as to obtain a more accurate initial value of the manoeuvre target, and at the same time, the extended Kalman filtering method is used to accurately locate and track the real-time target, so as to obtain the accurate positioning and tracking information of the real-time target. Through experimental simulation, it can be seen that the algorithm proposed in this paper has good tracking effect in both linear manoeuvre target tracking scenarios and non-linear manoeuvre target tracking scenarios.

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

Indoor wireless positioning has long been a dynamic field of research due to its broad application range. While many commercial products have been developed, they often are not open source or require substantial and costly infrastructure. Academically, research has extensively explored Bluetooth Low Energy (BLE) for positioning, yet there are a noticeable lack of studies that comprehensively compare traditional algorithms under these conditions. This research aims to fill this gap by evaluating classical positioning algorithms such as K-Nearest Neighbor (KNN), Weighted K-Nearest Neighbor (WKNN), Naïve Bayes (NB), and a Received Signal Strength-based Neural Network (RSS-NN) using BLE technology. We also introduce a novel method using Convolutional Neural Networks (CNN), specifically tailored to process RSS data structured in an image-like format. This approach helps overcome the limitations of traditional RSS fingerprinting by effectively managing the environmental dynamics within indoor settings. In our tests, all algorithms performed well, consistently achieving an average accuracy of less than two meters. Remarkably, the CNN method outperformed others, achieving an accuracy of 1.22 m. These results establish a solid basis for future research, particularly towards enhancing the precision of indoor positioning systems using deep learning for cost-effective, easy to set up applications.

Data security is one of the prime concerns in wireless networks. PLKG has been emerging as an attractive alternative to traditional cryptographic techniques. PLKG is more computationally efficient than cryptography. Moreover, PLKG using Principal component analysis (PCA) as pre-processing may further save computations. This paper proposes three mechanisms to select components of PCA which are based on Information content, Mean and Histfit. Bit Disagreement Rate (BDR) is compared for each mechanism. Histfit based method is found to be best. Since only two components are supposed to be processed for key generation, it is computationally efficient/ power efficient too.

Several indoor positioning systems that utilize visible light communication (VLC) have recently been developed. Due to the simple implementation and high precision, most of these systems are dependent on received signal strength (RSS). The position of the receiver can be estimated according to the positioning principle of the RSS. To improve positioning precision, an indoor three-dimensional (3D) visible light positioning (VLP) system with the Jaya algorithm is proposed. In contrast to other positioning algorithms, the Jaya algorithm has a simple structure with only one phase and achieves high accuracy without controlling the parameter settings. The simulation results show that an average error of 1.06 cm is achieved using the Jaya algorithm in 3D indoor positioning. The average errors of 3D positioning using the Harris Hawks optimization algorithm (HHO), ant colony algorithm with an area-based optimization model (ACO-ABOM), and modified artificial fish swam algorithm (MAFSA) are 2.21 cm, 1.86 cm and 1.56 cm, respectively. Furthermore, simulation experiments are performed in motion scenes, where a high-precision positioning error of 0.84 cm is achieved. The proposed algorithm is an efficient method for indoor localization and outperforms other indoor positioning algorithms.

The article presents the dynamic estimation method of the path loss exponent parameter in the function of the distance based on the conducted measurements. A specific feature of this solution is its suitability for distance estimation on devices which are characterised by a small amount of resources. The presented method allows to provide an acceptable precision of distance estimation while using a relatively small measurement set. For this purpose, real RSSI (Received Signal Strength Indicator) measurements were used and estimation of the path-loss exponent was performed with the use of a Bayesian particle filter. The article, apart from a detailed demonstration of the algorithms, presents the results of the sensitivity analysis of this method to change the number of inserted particles and of the repetitions of calculations needed to estimate the path loss exponent. Additionally, the results of the model stability study on the size change of the experimental dataset RSSI are presented. The properties and accuracy of the proposed method are verified based on a set of actual measurement data. All the obtained results indicate the utility of the Bayesian filtering method for effective estimation of the path loss exponent and confirm the possibility of using the described method in systems with a limited amount of computing resources. [ABSTRACT FROM AUTHOR]

Device-free localization (DFL) is a technology designed to determine the positions of targets without the need for them to carry electronic devices. It achieves this by analyzing the shadowing effects of radio links within wireless sensor networks (WSNs). However, obtaining high precision in DFL often results in increased energy consumption, severe electromagnetic interference, and other challenges that impact positioning accuracy. Most DFL schemes for accurate tracking require substantial memory and computing resources, which make them unsuitable for resource-constrained applications. To address these challenges, we propose an intelligent mesh cluster (IMC) algorithm that achieves accurate tracking by adaptively activating a subset of wireless links. This approach not only reduces electromagnetic interference but also saves energy. The IMC algorithm leverages geometric objects, such as meshes and mesh clusters formed by wireless links, to achieve low computational complexity. By scanning a subset of mesh cluster-related wireless links near the DFL target, the algorithm significantly reduces the computational requirements. The target's location estimate is determined based on the connection information among the mesh clusters. We conducted numerous simulations to evaluate the performance of the IMC algorithm. The results demonstrate that the IMC algorithm outperforms grid-based and particle filter-based DFL methods, confirming its effectiveness in achieving accurate and efficient localization. [ABSTRACT FROM AUTHOR]

Advanced Indoor Positioning Systems (IPS) based on Received Signal Strength (RSS) fingerprints have been paramount in 6G network research and commercial exploitation due to their cost-effectiveness and simplicity. Despite their popularity, the advent of 6G has prompted a shift towards exploring Deep Learning algorithms to further enhance their performance and precision. Deep Learning research typically demands large datasets, leading to reliance on data augmentation and crowdsourcing techniques for data collection. However, the traditional centralization of data in crowdsourcing poses privacy risks, and here is where Federated Learning (FL) comes into play. In light of this, our study introduces FL to bridge this divide in a decentralized way, eliminating the need for servers to acquire labeled data directly from users. This approach aims to minimize localization error in RSS fingerprints, preserve user privacy, and reduce system latency, all key goals for 6G networks. Moreover, we explore the use of power transmission techniques to further decrease the latency in the FL system. Our simulation outcomes confirm the superiority of FL over traditional Stochastic Gradient Descent (SGD) methods considering critical evaluation metrics like localization error and global loss, paving the way for efficient 6G implementation.

Anonymous authentication system enables mobile users to anonymously authenticate themselves to an authorized entity such as a Group Manager (GM) without revealing any privacy information. It provides unlinkable but accountable communications as well. These features are useful for wireless mobile networks implementation including vehicle ad-hoc networks (VANETs). However, performance of the system has to be sufficient reliable which may be existing systems have not dealt with yet. In this paper, we propose pseudonymous-based anonymous authentication between participating mobile users involved in the communications. We combine shared key generation based on received signal strength (RSS) between two involving entities and unlinkable but accountable pseudonymous-based anonymous authentication with efficient and effective pseudonym self-generation and revocation process. Our proposed shared key generation provides unique pseudonymous identities (PIDs). Based on PID at epoch time and updated revocation list obtained from GM, we achieve an efficient cost computation of revocation check. We show the measurement scenario of system performance by varying the traffic conditions either quiet or crowded to create communication impairment combined with mobile users’ speed varying on 20 km/h, 40 km/h, and 60 km/h respectively and ping time interval settings on 7 ms, 10 ms, and 20 ms, respectively. Here, the result of evaluation shows that PIDs generation works properly by the number of generated PIDs up to 11 with the highest correlation up to 0.99. Meanwhile, quantization algorithm works properly for 3000 or more ICMP packets and achieves zero key disagreement rate (KDR). Total signing and verification times are sufficient practical about 90 ms and 100 ms, respectively.

For indoor localisation, a challenge in data-driven localisation is to ensure sufficient data to train the prediction model to produce a good accuracy. However, for WiFi-based data collection, human effort is still required to capture a large amount of data as the representation Received Signal Strength (RSS) could easily be affected by obstacles and other factors. In this paper, we propose an extendGAN+ pipeline that leverages up-sampling with the Dirichlet distribution to improve location prediction accuracy with small sample sizes, applies transferred WGAN-GP for synthetic data generation, and ensures data quality with a filtering module. The results highlight the effectiveness of the proposed data augmentation method not only by localisation performance but also showcase the variety of RSS patterns it could produce. Benchmarking against the baseline methods such as fingerprint, random forest, and its base dataset with localisation models, extendGAN+ shows improvements of up to 23.47%, 25.35%, and 18.88% respectively. Furthermore, compared to existing GAN+ methods, it reduces training time by a factor of four due to transfer learning and improves performance by 10.13%. [ABSTRACT FROM AUTHOR]

The mobility of people in big cities tends to increase with the improvement in the availability of sharing access in public facilities. However, the dense environment with moving objects will allow the possibility of the object's detachment from a monitoring system. This condition will cause concern, especially if the object is a priority that needs to be protected. We propose a system for detecting moving objects in indoor environments using a fingerprint method of Received Signal Strength (RSSI) data retrieval. The work was conducted in the observation area, a part of a mall full of tenant stores and humans moving during shopping hours. The randomness of RSSI data emitted from access points in the indoor area is affected by multipath and signal reflection because of walls or furniture existing in the building. The K-NN regression algorithm was utilized to generate RSSI data based on the on-site measurement to prune this randomness. The generated data will be clustered, using the K-means algorithm and Elbow method to ensure the optimal number of K. The experiment results showed that using the best K for RSSI clustering, the positioning accuracy produced by the system reached 77% of the total expected accuracy. Meanwhile, according to the signal characteristics of indoor buildings, the entrance corner had the worst data distribution, with 37.35% of the data generated having an RSSI value close to the receiver sensitivity threshold. [ABSTRACT FROM AUTHOR]

Data security is one of the prime concerns in wireless networks. PLKG has been emerging as an attractive alternative to traditional cryptographic techniques. PLKG is more computationally efficient than cryptography. Moreover, PLKG using Principal component analysis (PCA) as pre-processing may further save computations. This paper proposes three mechanisms to select components of PCA which are based on Information content, Mean and Histfit. Bit Disagreement Rate (BDR) is compared for each mechanism. Histfit based method is found to be best. Since only two components are supposed to be processed for key generation, it is computationally efficient/ power efficient too. [ABSTRACT FROM AUTHOR]

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This paper proposes a novel Non-linear Camera Assisted-Received Signal Strength (nCA-RSS) algorithm. The basic idea of nCA-RSS is to simultaneously utilize camera and Photo-Diode (PD) to capture visual information and signal strength information to achieve both high coverage and accurate positioning. First, based on single-view geometry theorem, nCA-RSS algorithm estimates incidence angle of visible light based on visual information. Then, based on the estimated incidence angle, nCA-RSS algorithm utilizes signal strength to determine the location of the receiver. Due to the use of visual information, nCA-RSS no longer has orientation limitations. Based on Levenberg-Marquardt, nCA-RSS can use three Light Emitting Diode (LED) to achieve precise 3-Dimensional (3D) positioning. Therefore, compared with perspective n point algorithm, nCA-RSS can achieve high coverage. Simulation results show that nCA-RSS can achieve 80th percentile accuracies of less than 2.5 cm for positioning regardless of the orientation of the receiver.

One of the challenges faced by telecommunication service providers is capacity. The capacity of a cell depends on the model used in planning the cell during deployment stage. This paper presents Artificial Neural Network (ANN) Pathloss Prediction model for Long Term Evolution (LTE) network for microcells in Benin city, Edo state, Nigeria. Received signal strength data collected with respect to distance, mobile station antenna height and base station antenna height was used to create a data base with pathloss estimated from the measured data. A feature variable consisting of of measurement data collected was used to train a 4-32-1 multilevel perceptron artificial neural network with pathloss as the label using python language on Scientific python development environment interface (SPYDER). The result of the test data shows that artificial neural network is suitable for predicting pathloss in Benin City. This is as a result of root mean square error (RMSE) values and mean absolute error values of and 0.42 obtained respectively which are less than the error value allowed for network planning. It is recommended that the result of the study be adopted in designing or expanding LTE network capacity in the areas studied in this work.

This paper proposes a novel Non-linear Camera Assisted-Received Signal Strength (nCA-RSS) algorithm. The basic idea of nCA-RSS is to simultaneously utilize camera and Photo-Diode (PD) to capture visual information and signal strength information to achieve both high coverage and accurate positioning. First, based on single-view geometry theorem, nCA-RSS algorithm estimates incidence angle of visible light based on visual information. Then, based on the estimated incidence angle, nCA-RSS algorithm utilizes signal strength to determine the location of the receiver. Due to the use of visual information, nCA-RSS no longer has orientation limitations. Based on Levenberg-Marquardt, nCA-RSS can use three Light Emitting Diode (LED) to achieve precise 3-Dimensional (3D) positioning. Therefore, compared with perspective n point algorithm, nCA-RSS can achieve high coverage. Simulation results show that nCA-RSS can achieve 80th percentile accuracies of less than 2.5 cm for positioning regardless of the orientation of the receiver.

Radio emitter localization based on Received Signal Strength (RSS) is promising in large-scale Internet of Things (IoT) and wireless sensor networks (WSNs) for its low hardware and computation costs. To improve its local-ization accuracy and reduce the system energy consump-tion, we propose an improved RSS localization algorithm based on the joint sensor selection and semidefinite pro-gramming (SDP). An initial position estimate is first ob-tained using RSSs available at a random set of sensors. A refined sensor set is then selected to complete the sec-ond estimation by analyzing the geometric structure of sensing network. Performance of the method is evaluated in terms of localization accuracy and execution time, and compared with existing methods. Extensive simulations demonstrate that the proposed approach achieves a locali-zation accuracy of approximately 1.5 m with 8 to 10 sen-sors. The method outperforms the second-order cone pro-gramming (SOCP) and the least squared relative error (LSRE)-based SDP algorithms in terms of both the loca-tion and the transmit power estimation accuracy.

In this era of telecommunication, quality of service (QoS) is the prime metric for defining the performance of wireless networks. However, QoS is highly affected by the levels of signal strength. Variations in signal strength lead to several undesirable consequences, increasing wireless device's power consumption, especially during handover. In the perspective of Green Networking, to devise energy-efficient handover techniques, this paper compares the power consumption of wireless nodes under different states of affairs, leading to variations in signal strength. A vertical handover scenario between Wi-Fi and WiMAX networks was carried out to analyze the power consumption of two different sets of nodes, namely Type-I and Type-II nodes. Five different scenarios leading to signal strength fluctuations during handover were considered during the study. The initial energy levels of nodes were calculated at three different voltage levels, namely charge voltage, nominal voltage and cut-off voltage. The results explained the individual battery drainage pattern of wireless nodes with variations in signal strength under different scenarios and evaluated the cumulative power consumption of wireless nodes with variable signal strength at three voltage levels. Network performance was analyzed based on residual energy of nodes, throughput and packet delivery ratio. The cumulative analysis of power consumption presented in this paper can provide a prominent proposal of the battery drainage pattern of wireless nodes for various scenarios leading to variable signal strength, which can serve as a roadmap for devising energy-efficient mechanisms during handover in new generation ubiquitous networks. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) with energy-saving capabilities have drawn considerable attention in recent years, as they are the key for long-term monitoring and embedded applications. To improve the power efficiency of wireless sensor nodes, a wake-up technology was introduced in the research community. Such a device reduces the system's energy consumption without affecting the latency. Thereby, the introduction of wake-up receiver (WuRx)-based technology has grown in several sectors. The use of WuRx in a real environment without consideration of physical environmental conditions, such as the reflection, refraction, and diffraction caused by different materials, that affect the reliability of the whole network. Indeed, the simulation of different protocols and scenarios under such circumstances is a success key for a reliable WSN. Simulating different scenarios is required to evaluate the proposed architecture before its deployment in a real-world environment. The contribution of this study emerges in the modeling of different link quality metrics, both hardware and software metrics that will be integrated into an objective modular network testbed in C++ (OMNeT++) discrete event simulator afterward are discussed, with the received signal strength indicator (RSSI) for the hardware metric case and the packet error rate (PER) for the software metric study case using WuRx based on a wake-up matcher and SPIRIT1 transceiver. The different behaviors of the two chips are modeled using machine learning (ML) regression to define parameters such as sensitivity and transition interval for the PER for both radio modules. The generated module was able to detect the variation in the PER distribution as a response in the real experiment output by implementing different analytical functions in the simulator. [ABSTRACT FROM AUTHOR]

An indoor positioning design developed for mobile phones by integrating a single microphone sensor, an H2 estimator, and tagged sound sources, all with distinct frequencies, is proposed in this investigation. From existing practical experiments, the results summarize a key point for achieving a satisfactory indoor positioning: The estimation accuracy of the instantaneous sound pressure level (SPL) that is inevitably affected by random variations of environmental corruptions dominates the indoor positioning performance. Following this guideline, the proposed H2 estimation design, accompanied by a sound pressure level model, is developed for effectively mitigating the influences of received signal strength (RSS) variations caused by reverberation, reflection, refraction, etc. From the simulation results and practical tests, the proposed design delivers a highly promising indoor positioning performance: an average positioning RMS error of 0.75 m can be obtained, even under the effects of heavy environmental corruptions. [ABSTRACT FROM AUTHOR]

This paper is concerned with the received signal strength -based cooperative localization problem in a non-line-of-sight (NLOS) environment. A novel cooperative method in terms of the semidefinite relaxation technique is proposed to reduce the influence of the NLOS propagation on the network. First, an objective function is constructed based on various measurements in the NLOS environment, and an identical balance parameter is put forward as the overall estimate of the error under NLOS. Then, characteristics of the collaborative network and the NLOS environment are simultaneously taken into consideration to establish inequality constraints. Furthermore, by means of the semidefinite relaxation method, the localization problem is transformed into a solvable semidefinite programming problem to achieve the position estimation under cooperation. Moreover, the definite Cramer–Rao lower bound of the localization problem is given to evaluate the algorithm performance. Finally, simulation experiments show that the proposed method outperforms some existing state-of-the-art methods. [ABSTRACT FROM AUTHOR]

ABSTRAK Penentuan posisi di dalam ruangan merupakan hal yang penting untuk menjadikan petunjuk di dalam ruangan. Banyak metode lain yang sudah dilakukan penelitian tetapi ada beberapa kendala seperti tidak dapat digunakan di area sensitive dan biaya yang mahal. Pada penelitian ini kami mengusulkan untuk melakukan simulasi penentuan posisi menggunakan metode Received Signal Strength (RSS) dengan Light Emitting Diode (LED) dan Positive Intrinsic Negatif (PIN). Dengan asumsi pada ruangan tertutup yang berukuran 5 × 5 × 3 m3, kami melakukan simulasi 4 skenario posisi transmitter. Pada setiap skenario memiliki 3 buah transmitter pada level daya 1 watt yang lebih rendah dari penelitian-penelitian sebelumnya. Dari hasil simulasi yang ekstensif menyatakan bahwa, pada skenario 1 memiliki hasil akurasi 27,97%, skenario 2 sebesar 43,33%, skenario 3 sebesar 86,84%, dan skenario 4 dengan hasil 21,15%. Kata kunci: indoor positioning system, received signal strength, visible light communication  ABSTRACT Determining the position in the room is important to be a guide in the room. Many other methods have been researched, but there are some obstacles such as not being able to be used in sensitive areas and expensive. In this study, we suggest simulating buying a position using the Received Signal Strength (RSS) method with Light Emitting Diode (LED) and Positive Intrinsic Negative (PIN). Assuming that the room is 5 × 5 × 3 m3 closed, we simulated 4 transmitter position scenarios. Each scenario has 3 transmitters at a power level of 1 watt which is lower than previous studies. From the extensive simulation results stated that scenario 1 has an accuracy of 27.97%, scenario 2 is 43.33%, scenario 3 is 86.84%, and scenario 4 is 21.15%. Keywords: indoor positioning system, received signal strength, visible light communication

In large cities, the coverage and quality of telecommunication services inside buildings is low and needs more effort for its improvement. Signal attenuators like number of walls, number of floors and others affect the quality of received signal. Therefore, femtocells are used for improving the poor performance of network inside buildings and other dead zone areas. The aim of this work is to compare the received signal strength of femtocell with rectangular and circular microstrip patch antenna designed at 2.55 GHz. This work considers the received signal strength of femtocell with the designed rectangular and circular microstrip patch antennas. The performance analysis uses Multi Wall Multi Floor indoor propagation model. The result showed that the total gain of rectangular and circular microstrip patch antenna are 3.6528 dB and 2.924 dB, respectively. The received signal strength of femtocell with rectangular microstrip patch antenna is larger than that of circular microstrip patch antenna designed at the same frequency. The effect of number of walls and floors on the received signal strength of femtocell is also clearly indicated. Generally, it is found that much higher received signal strength is observed in rectangular microstrip patch antenna than in circular one. The outcome of this work shows that femtocells are capable of enhancing signal quality for indoor users.