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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]

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]

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]

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]

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]

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]

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]

Aiming at the path planning problem of unmanned aerial vehicle (UAV) base stations when performing search tasks, this paper proposes a Double DQN-state splitting Q network (DDQN-SSQN) algorithm that combines state splitting and optimal state to complete the optimal path planning of UAV based on the Deep Reinforcement Learning DDQN algorithm. The method stores multidimensional state information in categories and uses targeted training to obtain optimal path information. The method also references the received signal strength indicator (RSSI) to influence the reward received by the agent, and in this way reduces the decision difficulty of the UAV. In order to simulate the scenarios of UAVs in real work, this paper uses the Open AI Gym simulation platform to construct a mission system model. The simulation results show that the proposed scheme can plan the optimal path faster than other traditional algorithmic schemes and has a greater advantage in the stability and convergence speed of the algorithm. [ABSTRACT FROM AUTHOR]

Sensory data is only meaningful if correctly associated with originating locations, e.g., in three-dimensional wireless sensor systems inside an aircraft cabin. Received signal strength is a cost-effective option to locate low-cost sensors due to its universal availability, but suffers from coarse ranging accuracy with multiplicative errors. This paper presents a new received signal strength-based approach to locating a large number of low-cost sensors given their possible three-dimensional installation points. Our approach achieves the accuracy which has not been achieved in the literature. The approach first cooperatively locates the sensors in the continuous three-dimensional spaces, then associates the continuous location estimates to the installation points, and finally refines the association with likelihood ascent search. A new convex relaxation-based optimization is designed for cooperative localization in continuous three-dimensional spaces. The Kuhn-Munkres algorithm is generalized for the association. Cramér-Rao Lower Bounds are derived to specify the local three-dimensional regions in which refinement is carried out to improve the final accuracy with little complexity overhead. The proposed approach is validated experimentally with signal strength measurements collected in a Fokker 100 passenger plane, achieves 100% accuracy in all experiments conducted, and outperforms state-of-the-art metaheuristics significantly with much shorter execution time. Note to Practitioners—This paper is motivated by a goal to realize various aircraft automation applications through three-dimensional localization and association of wireless sensors. For example, automatic identification of seat locations can be implemented efficiently with wireless sensors, which enables effortless re-association between control buttons in the seats and the corresponding functions after cabin refurbishment. Meanwhile, automatic detection of missing/misplaced safety equipment can be carried out to improve the efficiency of flight preparation. There currently lacks an received signal strength-based three-dimensional sensor association method in indoor environments. The theoretical framework proposed in this paper aims to utilize the readily available received signal strength measurements to associate Commercial-Off-The-Shelf sensors to their possible installation points. This approach avoids the requirement of labor-intensive fingerprinting effort and high-cost equipment. The proposed approach has been tested and evaluated using both synthetic data and real data collected in a passenger plane as a proof of concept. [ABSTRACT FROM AUTHOR]

Visible light positioning (VLP) has attracted intensive attention from both academic and industrial communities thanks to its high accuracy, immunity to electromagnetic interference, and low deployment cost. In general, the receiver in a VLP system determines its own position by exploring the received signal strength (RSS) from the transmitter according to a pre-built RSS attenuation model. In such model-based methods, the LED's emission power and the receiver's height are usually required known and constant parameters to obtain reasonable positioning accuracy. However, the LED's emission power is normally time-varying due to the fact that the LED's optical output power is prone to changing with the LED's temperature, and the receiver's height is random in a realistic application scenario. To this end, we propose a height-independent three-dimensional (3D) VLP scheme based on the RSS ratio (RSSR), rather than only using RSS. Unlike existing RSS-based VLP methods, our method is able to independently find the horizontal coordinate, i.e., two-dimensional (2D) position, without a priori height information of the receiver, and also avoids the negative effect caused by fluctuation of the LED's emission power. Moreover, we can further infer the height of the receiver to achieve three-dimensional (3D) positioning by iterating the 2D results back into positioning equations. To quickly verify the proposed scheme, we conduct theoretical analysis with mathematical proof and experimental results with real data, which confirm that the proposed scheme can achieve high position accuracy without known information of the receiver's height and LED's emission power. We also implement a VLP prototype with five LED transmitters, and experimental results show that the proposed scheme can achieve very low average errors of 2.73 cm in 2D and 7.20 cm in 3D. [ABSTRACT FROM AUTHOR]

In view of the problems of high algorithm complexity and large computation in the practical application of indoor positioning system, a WiFi/PDR integrated positioning system based on EKF is designed and implemented. In the WiFi fingerprint positioning part, a fingerprint matching method based on an adaptive sliding window is proposed. The search window is obtained by calculating the RSSI Euclidean distance in the neighboring state to dynamically adjust the matching range of the fingerprint library, thereby achieving rapid convergence of the positioning results. In the fusion localization stage, the system characteristics of EKF and PDR are combined to solve the time registration problem. Based on the WiFi data update, the EKF algorithm is used for data fusion, and the PDR directly outputs the positioning result when the fusion data is not synchronized. Experimental results show that the positioning system has good operating effects and stability. The average positioning error of the proposed method is 2.27 m in the actual positioning scene, and the positioning accuracy can reach 3 m in 80% of the cases. [ABSTRACT FROM AUTHOR]

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by a set of known network nodes in a harsh indoor environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is non-trivial in the presence of multipath propagation, shadowing and radio interference. Auxiliary information, such as the indoor building map and user's orientation information, potentially can help to improve localization performance. As the indoor layout is usually known as a priori, a user's moving direction or orientation in a given indoor map may contain valuable information to assist for reducing location ambiguities at estimation, typically when the radio signal channel is corrupted with noise. In this paper, we propose a double-layer hidden Markov model (DHMM) within a Bayesian learning framework for combining user orientation information and processing RSS data in the localization process to deal with RSS fluctuations induced by human body shadowing and multipath interference. Simulation and experimental results show that incorporating user orientation can potentially provide promising indoor positioning results. [ABSTRACT FROM AUTHOR]

In wireless sensor networks, non‐linear distance information characterized by uncertainty and randomness creates the deviation and significantly target nodes position is affected. Non‐linearity effect among distance and received signal strength (RSS) of target nodes is minimized by modelling edge weights using knowledge base in fuzzy logic system. Further, optimum membership functions bases' of RSS and edge weights using application of butterfly optimization algorithm are evolved to minimize the node position error. Here, a single anchor node is used to estimate target nodes 3D coordinates in anisotropic environment using range free localization methods. The anchor node has been deployed at top layer and over beneath layers target nodes are distributed equally. In This paper, simulation results of the proposed method attain substantial performance improvement in target node 3D position accuracy than the earlier proposed range‐free methods. Proposed technique is useful for mapping of several instances like, fire hazards in forests, tracking of workers at different installation sites, solar plant tracking in smart cities and so forth. [ABSTRACT FROM AUTHOR]

In this paper, we propose a game theoretic framework for secure physical layer authentication (PLA) by applying the spatial correlations of received signal strength (RSS) in the air-to-ground channels between an unmanned aerial vehicle (UAV), a legitimate transmitter, and a spoofer. Based on our hypothesis model, we first derive the false alarm rate and miss detection rate for the UAV receiver. We then formulate a zero-sum PLA game where the UAV receiver selects its RSS detection threshold while the spoofer selects its attack probability to maximize their respective utilities. We derive the Nash equilibrium (NE) and its existence condition for the proposed PLA game. Monte Carlo simulation results accurately verify our analytical expressions for the false alarm rate and miss detection rate. Numerical results highlight the complex interplay between the detection threshold, spoofing attack probability and UAV altitude in the derived NE. [ABSTRACT FROM AUTHOR]

The ability to determine the location of a sensor node in a wireless sensor network is essential in many applications. We propose a received signal strength (RSS) based cooperative localization technique. The target nodes, whose locations are unknown during the deployment phase, communicate with the anchor nodes (whose locations are known) and the neighboring target nodes to localize themselves. In the proposed technique the target nodes behave as pseudo anchor nodes after they have localized themselves. It is observed that in such cooperative techniques the localization error propagates from one iteration to the next. We mitigate this error propagation by eliminating the network edge nodes from updating their location information after the first iteration. We propose two distributed edge node detection techniques: quadrant method (Quad) and geometric dilution of precision (GDOP) based method. Extensive simulations have been carried out, on uniform anchor and non-uniform network topologies, to analyze the performance of the proposed edge detection techniques and the iterative localization algorithm. The proposed localization algorithm is compared with state of the art localization techniques using simulations as well as experiments, and is shown to achieve improved localization accuracy. The Cramer-Rao lower bound (CRLB) is also derived and used as a performance benchmark. [ABSTRACT FROM AUTHOR]

The localization of internet of things (IoT) nodes in indoor scenarios with strong multipath channel components is challenging. All methods using radio signals, such as received signal strength (RSS) or angle of arrival (AoA), are inherently prone to multipath fading. Especially for time of flight (ToF) measurements, the low available transmit bandwidth of the used transceiver hardware is problematic. In our previous work on this topic we showed that wideband signal generation on narrowband low-power transceiver chips is feasible without any changes to existing hardware. Together with a fixed wideband receiving anchor infrastructure, this facilitates time difference of arrival (TDoA) and AoA measurements and allows for localization of the fully asynchronously transmitting nodes. In this paper, we present a measurement campaign using a receiver infrastructure based on software-defined radio (SDR) platforms. This proves the actual usability of the proposed method within the limitations of the bandwidth available in the ISM band at 2.4   GHz . We use the results to analyze the effects of possible anchor placement schemes and scenario geometries. We further demonstrate how this node-to-infrastructure-based localization scheme can be supported by additional node-to-node RSS measurements using a simple clustering approach. In the considered scenario, an overall positioning root-mean-square error (RMSE) of 2.19   m is achieved. [ABSTRACT FROM AUTHOR]

Visible light communication (VLC) channel quality depends on line-of-sight (LoS) transmission, which cannot guarantee continuous transmission due to interruptions caused by blockage and user mobility. Thus, integrating VLC with radio frequency (RF) such asWireless Fidelity (WiFi), provides good quality of experience (QoE) to users. A vertical handover (VHO) scheme that optimizes both the cost of switching and dwelling time of the hybrid VLC–WiFi system is required since blockage on VLC LoS usually occurs for a short period. Hence, an automated VHO algorithm for the VLC–WiFi system based on the hidden Markov model (HMM) is developed in this article. The proposed VHO prediction scheme utilizes the channel characterization of the networks, specifically, the measured received signal strength (RSS) values at different locations. Effective RSS are extracted from the huge datasets using principal component analysis (PCA), which is adopted with HMM, and thus reducing the computational complexity of the model. In comparison with state-of-the-art VHO handover prediction methods, the proposed HMM-based VHO scheme accurately obtains the most likely next assigned access point (AP) by selecting an appropriate time window. The results show a high VHO prediction accuracy and reduced mixed absolute percentage error performance. In addition, the results indicate that the proposed algorithm improves the dwell time on a network and reduces the number of handover events as compared to the threshold-based, fuzzy-controller, and neural network VHO prediction schemes. Thus, it reduces the ping-pong effects associated with the VHO in the heterogeneous VLC–WiFi network. [ABSTRACT FROM AUTHOR]

Summary: For the past 5 years, localization has become an emerging area in the field of wireless sensor networks (WSNs), which can be applicable for diverse applications, such as outdoor environments and monitoring of objects located in indoor environments. The major constraint in localization is allocating a location for each node as diverse sensor nodes are employed to retrieve the information in WSN. Recent research works in node localization have generally moved by multihop range‐free localization approaches for achieving high accuracy through reducing the localization error among the actual and estimated position of nodes. The conventional approaches are aimed to improve the localization accuracy without considering efficiency concerned with algorithm's convergence time and energy costs. The main intent of this paper is to model the WSN localization model, which focuses on the localization of anchor nodes concerning the target nodes. The multiobjective function is developed by considering the distance function, received signal strength (RSS), and energy. Here, a hybrid metaheuristic algorithm named hybrid cuckoo–red deer algorithm (HC‐RDA) with the integration of red deer algorithm (RDA) and cuckoo search algorithm (CSA) is adopted for localization of unknown nodes. The fitness function of the developed model is the minimization of the derived multiobjective function. Both simulation studies and theoretical analysis demonstrate that the proposed approach can improve localization performance with a better convergence rate, thus ensuring high localization accuracy compared to conventional metaheuristic models. [ABSTRACT FROM AUTHOR]

There is a wide demand for people counting and pedestrian flow monitoring in large public places such as scenic tourist areas, shopping malls, stations, squares, and so on. Based on the feedback from the pedestrian flow monitoring system, resources can be optimally allocated to maximize social and economic benefits. Moreover, trampling accidents can be avoided because pedestrian guidance is carried out in time. In order to meet these requirements, we propose a method of pedestrian flow monitoring based on the received signal strength (RSS) of wireless sensor networks. This method mainly utilizes the shadow attenuation effect of pedestrians on radio frequency (RF) signals of effective links. In this paper, a deployment structure of RF wireless sensor network is firstly designed to monitor the pedestrians. Secondly, the features are extracted from the wavelet decomposition of RSS signal series with a short time. Lastly, the support vector machine (SVM) algorithm is trained by an experimental data set to distinguish the instantaneous number of pedestrian passing through the monitoring point. In the case of dense and sparse indoor personnel density, the accuracy of the SVM model is 88.9% and 94.5%, respectively. In the outdoor environment, the accuracy of the SVM model is 92.9%. The experimental results show that this method can realize the high precision monitoring of the flow of people in the context of real-time pedestrian flow monitoring. [ABSTRACT FROM AUTHOR]

Indoor localization based on Bluetooth low energy (BLE) beacons has been rapidly developed, and many approaches have been developed to achieve higher estimation accuracy. In these methods, the received signal strength (RSS) is the input. However, the measurement of indoor environments is affected easily; the signal may be reflected and attenuated by obstacles such as the human body, walls, and furniture, which creates a challenge for methods based on signal mapping. In this study, BLE signal characteristics are investigated in an indoor localization setting. An experiment is performed using one BLE beacon and multiple receivers installed at different wall and ceiling positions. The raw RSS is observed, and the relationship between the BLE beacon signal strength characteristics against the human body effect as well as the receiver's placement in the observation area are discussed. Signal mapping is performed, where the signal strength is measured from all receivers simultaneously. The position estimation accuracy is examined based on different data scenarios. The results show that the estimation position estimated by the BLE beacon based on extensive BLE beacon data does not affect the estimation accuracy. [ABSTRACT FROM AUTHOR]

The development of smart agriculture solutions has recently become a popular trend in our lives with the wide expansion of Agriculture 4.0. Low Power Wide Area Networks (LPWANs) are an effective and useful communication tool that would help farmers monitor the status of their plantations on demand. Long Range (LoRa) is a popular LPWAN technology that features low cost, low power consumption and wide coverage. Implementing LoRa for smart agriculture applications in oases requires a prior understanding of the transmission characteristics of radio signals. This work covers an empirical study of LoRa signal propagation in date palms located in the governorate of Kebili, southern Tunisia. Measurements of the Received Signal Strength (RSS) and the Signal Noise Ratio (SNR), performed at different transmitter and receiver heights, were made using antennas operating over the 868 MHz frequency band with a bandwidth of 125 kHz. We first investigate the impact of environment, gateway height, and Spreading Factor (SF) on the distribution of the RSS. Next, we analyze the impact of SF on the Packet Delivery Ratio (PDR). Finally, we focus on the characterization of the signal path loss; we discuss the adequacy of the existing propagation models for the palm environment and propose an optimized signal attenuation model that depends on the palm tree characteristics (tree density and trunk diameter). The proposed model is then tested and validated in three different oasis sites. The results showed that the proposed model corresponds well to the measured path loss with a Root Mean Square Error (RMSE) of 4. • LoRa signal propagation was characterized in date palm oases at 868 MHz. • We studied the suitability of existing attenuation models to date palm oases. • We study the received signal strength at different heights and sites. • We evaluated path loss and packet delivery ratio for different spreading factors. • We proposed a new path loss model for date palm oases. [ABSTRACT FROM AUTHOR]

Location awareness is the key to success to many location-based services applications such as indoor navigation, elderly tracking, emergency management, and so on. Trilateration-based localization using received signal strength measurements is widely used in wireless sensor network–based localization and tracking systems due to its simplicity and low computational cost. However, localization accuracy obtained with the trilateration technique is generally very poor because of fluctuating nature of received signal strength measurements. The reason behind such notorious behavior of received signal strength is dynamicity in target motion and surrounding environment. In addition, the significant localization error is induced during each iteration step during trilateration, which gets propagated in the next iterations. To address this problem, this article presents an improved trilateration-based architecture named Trilateration Centroid Generalized Regression Neural Network. The proposed Trilateration Centroid Generalized Regression Neural Network–based localization algorithm inherits the simplicity and efficiency of three concepts namely trilateration, centroid, and Generalized Regression Neural Network. The extensive simulation results indicate that the proposed Trilateration Centroid Generalized Regression Neural Network algorithm demonstrates superior localization performance as compared to trilateration, and Generalized Regression Neural Network algorithm. [ABSTRACT FROM AUTHOR]

Summary: The innovative characteristics of different heterogeneous wireless networks provide better service. The disparate wireless access networks consist of wireless metropolitan access network (WMAN) and cellular networks. Various techniques are devised for dynamic network selection, but these techniques concentrated on attaining improved performance. To address this issue, a novel method is devised to improve the energy efficiency of disparate heterogeneous wireless networks. Here, the proposed fractional squirrel–dolphin echolocation (FrSqDE) algorithm is devised by integrating squirrel search optimization (SSA) and fractional dolphin echolocation (fractional DE) algorithm. In addition, the fitness function is considered with certain factors that include delay, bit error rate (BER), jitter, packet loss, energy consumption, probability of hot spot, average bit rate (ABR), and received signal strength (RSS). The proposed FrSqDE optimization algorithm is used for training deep belief networks (DBNs) to select optimal weights. Here, the network selection factors are given as input to the proposed FrSqDE‐based DBN, wherein optimal decision is made to handle vertical handoff. The proposed FrSqDE‐based DBN offered improved performance with a minimal call drop probability of 0.050, minimal delay of 0.013 s, minimal energy consumption of 0.393 J, and maximal throughput of 117.972 kbps. [ABSTRACT FROM AUTHOR]

Node identification based on unique hardware features like clock skews has been considered an efficient technique in wireless sensor networks (WSNs). Spoofing attacks imitating unique hardware features, however, could significantly impair or break down conventional clock-skew-based node identification due to exposed clock information through broadcasting. To defend against Spoofing attacks, we propose a new node identification scheme called node identification against Spoofing attack (NISA). It utilizes the reverse time synchronization framework, where sensor nodes’ clock skews are estimated at the head of a WSN, and the spatially-correlated radio link information to achieve simultaneous node identification and attack detection. We further provide centralized and distributed NISA for covering both single-hop and multi-hop scenarios, the former of which employs a single-input and multiple-output convolutional neural network. With a real WSN testbed consisting of TelosB sensor nodes running TinyOS, we investigate the identifiability of clock skews under temperature and voltage variations and evaluate the performance of both centralized and distributed NISA. Experimental results demonstrate that both centralized and distributed NISA could provide accurate node identification and Spoofing attack detection. [ABSTRACT FROM AUTHOR]

A device-to-device (D2D) communication permits wireless devices to communicate mutually without network assistance. It promises to enhancement in spectral and network efficiency in the next-generation networks. A D2D communication needs device discovery, which is considered as a primary task. It represents the geographic coordinates of the wireless devices in a cellular network. If the device discovery is not legitimated properly may present noteworthy latency, energy consumption, and inaccuracy. In this work, a novel device discovery procedure based on cell sector scanning algorithm (SSA) is proposed for in-band D2D communication and the device discovery is performed under two scenarios: haphazard walk and velocity. An experimental setup is established to receive signal strength (RSS) measurements in a specific sector. The coverage area is divided into eight sectors to discover the devices. The sectors are used to apply and verify the proposed solution and the algorithm for device discovery. It considers the existing radio resources for proximal device discovery and adjusts the discovery signal transmission appropriately. The discovery algorithm must be fast, accurate, and energy efficient at the device level and network level. This technique performs well and enhances the quality of discovery process more than 20% of the linear estimation and diminishes the energy consumption by 24% to accomplish the discovery in dense areas and makes it dynamic and adjustable for changing environments. A mathematical analysis based on the sector scanning attempts is also provided. Furthermore, the improved discovery scheme is evaluated and confirmed by the probability of isolation. [ABSTRACT FROM AUTHOR]

We proposed and experimentally demonstrated an ensemble learning scheme based on cascaded residual neural network (CRNN) to enhance the robustness of machine learning (ML) based visible light positioning (VLP) systems under model-shake. The training complexity of the CRNN is low. Experimental results reveal that with an acceptable complexity cost, the positioning accuracy under model-shake can be optimized from 8.31 cm to 2.04 cm and 1.56 cm with the proposed 2nd CRNN and 3rd CRNN, respectively. • Proposed and experimentally demonstrated a scheme based on CRNN to enhance the robustness of ML-based VLP systems. [ABSTRACT FROM AUTHOR]

VANET is a critical and demanding mission. Numerous methods exist, but none profits in a distributed fashion from physical layer parameters. This paper describes a method that enables individual nodes to estimate node density, independent of beacon messages, and other infrastructure-based information, of their surrounding network. In this paper, a discrete simulator of events was proposed to estimate the average number of simultaneously transmitting nodes, a functional channel model for the VANETs system, and a method of estimating node density. Proposed based on some equations to allow individual nodes to estimate their surrounding node density in real-time Optimized Node Cluster Algorithm with Network Density in which the composition of a cluster is triggered adjacent, these traffic signals is the same and has been predicated mostly on the position a vehicle might well take after crossing. Additional Ordered Tracking with Particle -Filter Routing in which receives simultaneous signal intensity versus node transmission and node density transmission. Conduct multiple location-related analyzes to test the plausibility of the neighboring singlehop nodes on mobility data. The system is designed to operate in the most complex situations where nodes have little knowledge of network topology and the results, therefore, indicate that the system is fairly robust and accurate. [ABSTRACT FROM AUTHOR]

WiFi positioning based on fingerprint has received widespread attention and practical applications. However, the fingerprints are susceptible to environmental changes, such as shadowing, multipath, temperature, humidity and obstacles. Due to the instability of received signal strength (RSS), it brings plenty of difficult for WiFi positioning with high accuracy. In this paper, a regularised online sequence extreme learning machine with forgetting parameters (FP-ELM) is adopted to solve the issue accordingly. Forgetting factor and regular factor are adopted in FP-ELM to cope with the time-varying nature of RSS and overcome the issue of irreversible matrix in OS-ELM. The fast running speed of the online sequence extreme learning machine (OS-ELM) is also maintained in FP-ELM. Extensive experiments are carried out in simulation and real experimental areas to explore the characteristics of FP-ELM. Moreover, the positioning results of FP-ELM are compared with the conventional algorithms (OS-ELM and KNN). The simulation and experimental results show when the regular factor is set properly, the positioning result based on FP-ELM algorithm is better than conventional algorithms Figures 1. [ABSTRACT FROM AUTHOR]

To improve the stability and accuracy of manoeuvring target tracking in three‐dimensional space based on the angle of arrival (AOA) and its rate of change observations, this study presents a new observation fusion method by fusing the received signal strength (RSS) with AOA and the rate of change of AOA. To enhance the adaptive ability of traditional strong tracking extended particle filter (TSTEPF) against model mismatch, this study re‐determines the position of the fading factor in the strong tracking extended Kalman filter based on the orthogonal principle and gives the calculating method. And by combining the method with the particle filter, a new strong tracking extended particle filter (STEPF) algorithm is proposed. Simulation results show that after fusing RSS into the observation model, the tracking speed and precision are both improved, especially precision, as the position root‐mean‐square error has a 58% decline on average. And it is found that STEPF proposed in this study has a more stable adaptive ability than TSTEPF, and is superior in terms of position, velocity, and acceleration estimation accuracy. [ABSTRACT FROM AUTHOR]

Natural gas, which is an important energy source, is generally transported through natural gas pipelines that also pass through underwater. Underwater natural gas pipelines can be punctured for various reasons, especially for corrosion and shipping, fishing activities. Thus, detection and localization of the leakages has a crucial role in ensuring energy reliability and preserving the natural life. In this study, a novel signal strength based curve fitting method was proposed for positioning the leakages in the underwater natural gas pipelines. The performance of this method was analyzed by simulation studies using the parameters of a real life underwater natural gas pipeline and a time-varying (because of moving sea surface, underwater currents etc.) multipath doubly selective underwater acoustic channel. Numerical results show that for this time-varying multipath underwater acoustic channel, which is quite realistic, leakages can be positioned with low errors from kilometers away depending on the ambient noise and the sea wave height. [ABSTRACT FROM AUTHOR]

This paper introduces a study on verifying received power at WLAN frequencies in indoor environments, Wireless InSite is a popular electromagnetic ray-tracing software which is widely used for predicting channel behaviour in indoor and outdoor environments. The study compares software-generated data with measurements collected through 3rd floor Chesham Building, University of Bradford, at WLAN frequencies, the paper also investigates the effect of changing settings on results accuracy and computational time, and finally, the paper presents a comparison between simulation results with empirical models. [ABSTRACT FROM AUTHOR]

The localization of an unknown narrow-beam interference source based on a high-Earth/medium-Earth orbiting satellite is considered, and two data-driven methods based on the received signal strength (RSS) are proposed. The narrow-beam interference makes multi-satellite localization scheme invalid, and conventional single-satellite localization approaches including Doppler and direction finding methods will degrade due to the high/medium orbit. In this case, the RSS-based method has the advantages as well as light-load and low-cost hardware. The pretreatment method is firstly conducted due to the discrete and anamorphic original data. Then the direct localization (DL) algorithm is proposed by fitting the change rate of RSS. Thereafter, the filtering and clustering localization (FCL) method is developed to improve the localization robustness. Additionally, the positioning error bound of FCL is analyzed using spherical geometry in some circumstances. Multiple simulations show that the proposed data-driven methods can effectively localize the interference source under such adverse but actual circumstances. [ABSTRACT FROM AUTHOR]

• A probabilistic-based approach to position a group of mobile users by the fusion of inertial and radio measurements. • Incorporating RSS measurement using sequence based similarity. • Recover from tracking failure with a dual particle filter. • Simulation and real world experiments to verify the feasibility of our approach. Positioning between multiple users without any given infrastructure is essential for many applications, such as emergency response in disaster areas. Traditional approaches based on inertial-measurement units (IMU) are able to measure position changes without any reference, but the accuracy deteriorates due to error accumulation for long terms. Particularly, it is challenging to deal with irregular walking patterns of users. This paper proposes to combine IMU and radio measurements (i.e., peer-to-peer Wifi received signal strength and peer-to-peer UWB ranging) for the positioning of a group of mobile users in emergency response, where no fixed anchors and no infrastructure are available. We incorporate the IMU and radio measurements into the particle filtering, which has the capability to cooperatively position a group of mobile users and recover from any potential tracking failures. By fusing the long-range Wifi RSS and short-range UWB ranging measurements, we can take the advantages of both sensors and achieve an accurate and robust positioning system. We have conducted experiments to validate the proposed approach both in a simulation and a real world experiment. Our experimental results show that the combination of Wifi and UWB measurements provides a positioning accuracy of 2.6 m, which is an improvement of 26% and 10% as compared with Wifi (3.5 m) and UWB (2.9 m) alone. [ABSTRACT FROM AUTHOR]

Effective indoor localization largely relies on the fingerprint database (model) of Received Signal Strength (RSS) in connection with Radio Frequency sources, such as the most widely used Bluetooth Low Energy (BLE) iBeacons. RSSs exhibit significant random variations in both the spatial and temporal domains. It is a notoriously onerous and challenging task to construct the fingerprint database for accurate localization, as the BLE RSSs must be captured via a full space scan from one point to another every few meters in a certain period of time. In order to tackle this problem, this study proposes an approach to fast fingerprints construction that only requires a sparse sampling of RSS of the space. First, a smartphone records the time series of RSS over a designated path, and a radio map for the path is then generated by a spatio-temporal mapping method using the Pedestrian Dead Reckoning algorithm. Second, the radio map of the entire space can be obtained via Gauss Process Regression (GPR), with outliers reduced to improve the reliability of the fingerprint database. Experiments have been performed in an underground carpark (38 m × 14 m), and the experimental results indicate that the proposed approach can construct the fingerprint database 300% faster than the conventional approach does. The localization accuracy of both approaches is quite similar (80% error in 2.8 m). The proposed approach offers potential for the construction of a large-scale fingerprint database for a wide-area Location Based Service (LBS) of Smart City indoor and outdoor integration, where big RSS data processing is a must. [ABSTRACT FROM AUTHOR]

To explore wireless sensor network signal transmission characteristics in through-the-earth communication, this paper focused on wireless network signal transmission attenuation by using 433MHz carrier frequency. Sensor nodes electromagnetic wave transmission characteristics were studied under the condition the different receiving node height, horizontal distance between the transmitting node and the receiving node through the wheat field experiments and computer simulation, the relationship between the received signal strength was established, transmission characteristics of field soil information collection sensor nodes in the soil under four wheat stages were put forwarded. The experiment demonstrated that 8 model goodness of fit R2 of effection of receiving node high and inter-nodes horizontal distance on RSSI(Received Signal Strength Indicator). Besides, three-dimensional surface of RSSI was built in four wheat growing periods, which the fitting model and goodness of fit of RSSI are obtained, and the model verification was conducted through SPASS software. Validation results showed that the model could better predict the received signal strength at different condition through-the-earth communication. The study can provide the technical support for sensor network node deployment and the establishment of the system in soil information acquisition. [ABSTRACT FROM AUTHOR]

In this paper, a comparative study between different indoor path loss prediction models is conducted. The investigated models include averaged wall loss model (AWM), single slope model (SSM), linear attenuation model (LM), two slope model (TSM), partitioned model (PM), and Motley-Keenan model (MKM). The models were tested in a simulated environment of the 3rd floor of Chesham building, the University of Bradford, a different set of frequencies were used including 28 GHz, 60 GHz, and 73.5 GHz, TSM shows the best performance, both AWM and MKM tend to have a similar performance at millimetrefrequencies, both models' prediction for corridor and LOS regions are pessimistic while TSM, SSM, and LM have better estimations in these regions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In this correspondence, we investigate unmanned aerial vehicle (UAV)-base stations (BSs) assisted and received signal strength (RSS) based mobile station (MS) localization. A practical air-to-ground path loss model is utilized, where the path loss exponent (PLE) varies with the elevation angle and altitude of UAV, and the accurate PLE estimate is often difficult to obtain. With unknown and unequal PLEs for different UAVs, the UAVs assisted localization problem becomes nonlinear and non-convex, which cannot be solved by the existing methods. We propose a piecewise convex approximation aided localization (PCAL) approach to convert the localization problem into linear and convex, without requiring the knowledge of PLE. The proposed PCAL approach with unknown and unequal PLEs achieves much higher accuracy than the existing methods which require perfectly known and equal PLE, due to its higher robustness against shadowing. In addition, a grid search aided ambiguity elimination (GSAE) method, which is more effective than the state-of-the-art methods, is proposed to determine the final MS localization estimate based on multiple tentative estimates derived from PCAL. The effectiveness of PCAL is also verified by the Cramer-Rao lower bound (CRLB) derived. [ABSTRACT FROM AUTHOR]

We consider the problem of online path planning for joint detection and tracking of multiple unknown radio-tagged objects. This is a necessary task for gathering spatio-temporal information using UAVs with on-board sensors in a range of monitoring applications. In this paper, we propose an online path planning algorithm with joint detection and tracking because signal measurements from these objects are inherently noisy. We derive a partially observable Markov decision process with a random finite set track-before-detect (TBD) multi-object filter, which also maintains a safe distance between the UAV and the objects of interest using a void probability constraint. We show that, in practice, the multi-object likelihood function of raw signals received by the UAV in the time-frequency domain is separable and results in a numerically efficient multi-object TBD filter. We derive a TBD filter with a jump Markov system to accommodate maneuvering objects capable of switching between different dynamic modes. Our evaluations demonstrate the capability of the proposed approach to handle multiple radio-tagged objects subject to birth, death, and motion modes. Moreover, this online planning method with the TBD-based filter outperforms its detection-based counterparts in detection and tracking, especially in low signal-to-noise ratio environments. [ABSTRACT FROM AUTHOR]

An optical indoor positioning system using an optical receiver and camera is proposed. Compared with previous works in this field, our system improved the coverage without sacrificing accuracy. Thus, the proposed indoor positioning system is more suitable under environments with dense obstacles, such as warehouses, hospitals, and factories. Since the camera is deployed at the receiver side, the angle of arrival (AoA) can be estimated by recognizing the light source in an image. The optical receiver is able to measure received signal strength (RSS). Combining the information in AoA and RSS, the relative position of the receiver to the light source can be estimated. In this system, the position of the light source can be sent to the receiver applying visible light communication. Thus, both communication and positioning can be enabled using a single light-emitting diode on the transmitter side. [ABSTRACT FROM AUTHOR]

This study mainly aims to find accurate directions in indoor environments by combining smartphone acceleration readings with WiFi received signal strength (RSS) readings. Patterns of user movement serve as the basis of this approach. These patterns are effectively utilized to detect user direction on the basis of the correlation between the RSS and acceleration values. A series of experiments was conducted to test the proposed approach. The proposed approach achieves 95% accuracy and thus shows great potential as a practical solution for estimating directions in indoor environments. [ABSTRACT FROM AUTHOR]

The accuracy of the visible light positioning (VLP) system is improved by using regression-based and interpolation-based approaches. These approaches are mixed with the received signal strength in both linear and nonlinear least squares techniques. The simplicity of the model is the key point, where a highly accurate VLP system with only one equation "interpolation" is used instead of many complex equations. Gaussian noise and reflections up to three bounces with high reflectivity are the source of noise. In addition, the strength of the methods is studied by changing some affecting parameters. In a previous work, we introduced the regression- based approach in VLP-based systems with one reflection. Here, we extend the work with three reflections, leading to an increase in noise to examine system robustness. The approaches are compared to the traditional ones, and show a remarkable enhancement, e.g., the average error has been improved by 67%. [ABSTRACT FROM AUTHOR]

The hybrid sensor network is mainly composed of static and dynamic sensor nodes. The dynamic node is the mobile robot with wireless sensor module installed. This paper proposes a robot navigation algorithm based on sensor technology and iterative maximum a posteriori estimation. It uses Kalman filter and least-squares fitting to improve RSSI measurement accuracy and the mobile robot only needs to use the received signal strength (RSSI) and odometer information to realize autonomous navigation in the sensing area. Moreover, static nodes are randomly deployed in the sensing area without a priori location information. Therefore, this algorithm has the advantages of low cost and ease of deployment. Both simulation and outdoor field experiments show the performance and effectiveness of the algorithm. [ABSTRACT FROM AUTHOR]

Highlights • The relation of transmission loss for acoustic wave propagation in inhomogeneous medium with an isogradient sound speed profile is presented. • An oversampled matched filter based method for RSS measurement (OSMF-RSS) is proposed in asynchronous transmitter–receiver scenario which, unlike DS-RSS, works on the basis of oversampling idea and does not have the drawbacks of DS-RSS. • By use of the obtained transmission loss model and OSMF-RSS, an iterative RSS based localization algorithm is derived. This algorithm, contrary to TOF method, is robust against sensor asynchrony and outperforms TOF when signal bandwidth is lower than a specific level. • The performance of the proposed RSS based algorithm is examined in fading condition. In weak flat fading, unlike sever one, OSMF-RSS performs accurate. Thus, Improved OSMF-RS for measuring RSS in presence of fading is proposed and simulation results show that in sever flat fading Improved OSMF-RSS performs more accurate. Abstract Localization is one of the fundamental tasks in underwater acoustic sensor network (UASN) which is a challenging issue due to inhomogeneity and bandwidth limitation of underwater medium. Time of flight (TOF) and received signal strength (RSS) measurements are two major techniques used for localization. The TOF based localization achieves high resolution when available bandwidth is high enough and also transmitter and receiver can be synchronized well. However, in the RSS, sensors do not need to be synchronized. In this paper we propose a new RSS based localization method for inhomogeneous underwater medium with an isogradient sound speed profile in which acoustic waves propagate along curvature path. To develop the localization algorithm, at the first step, we obtain the relation of transmission loss for acoustic wave propagation in inhomogeneous medium. Then, an oversampled matched filter based method for RSS measurement (OSMF-RSS) is proposed in asynchronous transmitter–receiver scenario. After that, an iterative localization algorithm is derived based on the RSS measurements. Moreover, we examine how the performance of our proposed algorithm affects by flat fading phenomenon which is a challenge of RSS based algorithms. We evaluate the performance of the proposed OSMF-RSS method by computer simulations under different conditions. The results show that the OSMF-RSS method outperforms the TOF based method in accuracy sense when the bandwidth of received signal is less than specific level. Note that, this achievement of the OSMF-RSS method is attained in asynchronous scenario, while in the TOF method transmitter–receiver should be synchronized. [ABSTRACT FROM AUTHOR]

In this study, a new powerful geometrical closed-form solution called Direction of Exponent Uncertainty (DEU) is proposed for received signal strength (RSS) based far-field localization when path loss exponent (PLE) and transmit power are both unknown. The uncertainty in the PLE due to environmental factors is a significant challenge for RSS based localization. DEU is built after careful investigation of geometrical behaviors of differential received signal strength circles, i.e. the locus of possible location of the emitter when transmit power is unknown. It is shown that the uncertainty in the PLE corresponds to a linear uncertainty for the location of the emitter in two dimensional space. This critical observation creates a basis for the sensor to move towards the emitter without estimating the emitter location after only three measurements. Furthermore, with only four different measurements, it is possible to effectively estimate the location of the emitter as well as the PLE by means of intersection of DEUs. Intersection of DEUs attains Cramer Rao Lower Bound with a dramatically reduced execution time compared to nonlinear least squares estimator. DEU is also proposed as an efficient route planning tool for moving sensors such as unmanned aerial vehicles. [ABSTRACT FROM AUTHOR]

Purpose This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry.Design/methodology/approach The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point.Findings Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints.Research limitations/implications Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable.Practical implications The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system.Social implications The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services.Originality/value A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function. [ABSTRACT FROM AUTHOR]

Direction of arrival (DoA) estimation has wide-ranging applications and is particularly challenging in complex propagation environments. Conventional approaches estimate the DoA based on the phase of the impinging signal on a sensor array. This approach has several challenges: for example, it requires tight synchronization among array elements and the estimated DoA is sensitive to multipath and to correct specification of the array geometry. In this paper, we develop a principled Bayesian DoA estimation algorithm based on the received signal strength gradient. We present algorithms for clustering, spatial- and model-based outlier detection and removal, and proper uncertainty quantification, which yield an estimator robust to non-uniform shadowing, multipath, and errors in antenna position. Physics-based full-wave simulations and experiments in the 2.4 GHz and 40 MHz bands illustrate the utility of our approach. [ABSTRACT FROM AUTHOR]