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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.

Device-free localization (DFL) based on wireless sensor networks (WSNs) is a technology that can detect and locate a person by measuring the changes in received signals without the need for any wireless devices. As an emerging important technology in WSNs, radio tomographic imaging (RTI) has received increasing attention. However, there is much room to improve localization accuracy in RTI. To address this issue, an enhanced channel-selection method and a new distance-based elliptical model are proposed to improve the localization accuracy. The enhanced frequency channel-selection method selects two channels with the lowest received signal strength (RSS) variances to collect data. This approach is more robust to environmental change. The new distance-based elliptical model is based on the distance between the voxels and sensors. Meanwhile, the communication links are divided into line-of-sight (LOS) paths and nonline-of-sight (NLOS) paths. Experimental results demonstrate that the proposed algorithm improves the accuracy of positioning by up to 44.8% over some state-of-the-art RTI methods with low cost.

With the fast development of new array technology and intelligent antenna, it is easier to obtain angle of arrival (AOA) measurements. Hybrid received signal strength (RSS) and AOA measurement techniques are proposed for the position computing in sensor networks. By converting the measurement equations and relaxing the optimization function, range-based square semidefinite programming (RLS-SDP) and squared range-based square semidefinite programming (SRLS-SDP) algorithms are put forward to obtain the source position estimate by considering the transmit power to be known or unknown. The proposed RLS-SDP and SRLS-SDP algorithms provide accurate solution to the source position estimate and avoid the initialization process of numerical calculation. The simulations show that the proposed RLS-SDP and SRLS-SDP algorithms perform better than the linear estimator and provide the accuracy performance which is very close to the Cramér-Rao Lower Bound (CRLB) of position estimation. The proposed SRLS-SDP algorithm shows its advantages in the computational complexity compared with the RLS-SDP, since the complexity of SRLS-SDP is independent of the number of anchor nodes.

This paper addresses a target localization problem in 3-D wireless sensor networks using a hybrid system that fuses received signal strength and angle of arrival measurements. First, we formulate the received signal strength and angle of arrival measurement models as the pseudo-linear equations. Then, the bias is derived from the 3-D angle of arrival measurements that take the measurement noise into account to improve the localization performance. Furthermore, a non-convex estimator is derived based on the Least Squares criterion. Finally, semi-definite relaxation and second-order cone relaxation are applied to transform the derived non-convex estimator into a convex one. We propose a semi-definite relaxation and second-order cone relaxation-based estimator which yields the best performance under a large measurement noise or a small measurement noise. The generalization of the proposed method for known transmit power can also be applied to the case when transmit power is not know. Theoretical analysis and computer simulations corroborate the superior performance of the proposed localization methods over the existing ones.

The use of millimeter wave technologies offer a promising solution for dense small cell networks, despite having to contend with challenging propagation characteristics. In particular, user-induced effects can lead to significant channel variations depending on the user equipment (UE) usage mode which in turn, can impact the quality of service. Estimation of UE operating conditions is therefore critical for optimal radio resource management. We propose a new approach to user activity recognition which makes use of both supervised and unsupervised machine learning. In particular, using information extracted from the received signal strength (RSS), a common metric readily available from many receiver chipsets, we perform a classification of user state (static or mobile relative to an access point) and UE mode of operation (voice call, using an app or in pocket). To develop and then train our classification system, measured RSS data was obtained using a custom 60 GHz measurement system for a range of indoor office scenarios which considered various UE to ceiling mounted access point configurations. In our approach, differentiation between static and mobile states is performed in preprocessing using a k-means algorithm. Small-scale fading features are then estimated from the RSS data and, using different feature scaling mechanisms, various supervised learning approaches are applied to investigate the optimal classification accuracy for the considered use cases in this work. We compare the classification performance of various window sizes and types, and show that a sliding window length of 1s without overlap performs best for time series segmentation at 60 GHz for the activities considered in this study. Among the different supervised learning approaches, the Decision Tree (DT) classifier performs best for both the user static and mobile cases with an accuracy of 100% and 98.0%, respectively. For static cases, user orientation, i.e., line-of-sight (LOS), quasi-LOS, and non-LOS, can also be classified and here the DT classifier also performs best with an accuracy of 98.2%, 97.6% and 100% for the voice call, using an app or in pocket use cases. Additionally, a feature ranking algorithm, called ReliefF, is adopted to determine the small-scale fading features that have the most significant influences on the classification accuracy and three different feature sets, namely Full, Reduced and Constrained sets, are then proposed based on feature ranking results. This allows the proposed techniques to be deployed on wireless platforms with different levels of processing capability.

To address three-dimensional (3D) localization problems in the wireless sensor networks (WSNs) of mobile communications, a closed-form algorithm by utilizing hybrid information extracted from the received-signal-strength (RSS) with unknown signal powers and the angle-of-arrival (AOA) is proposed in this paper. Firstly, the distance of the target is expressed as the linear equation with respect to coordinates of target and trigonometric functions of angles. Then, the RSS measurement is rewritten as the ratios of distances between the target and different receiving sensors. Furthermore, the set of pseudo-linear equations for the location of target is derived with respect to the AOA and nonlinear RSS measurements. Under the condition of the small variance of measurements, the first-order Taylor expansion around the value of measurement is taken to the set of pseudo-linear equations and the first-order perturbation errors is also obtained. Finally, the solution of the target localization is derived utilizing the weighted least square (WLS). Theoretical analysis and simulation results show that the performance of proposed algorithm converge to the CRLB in the situation of the moderate measurement errors.

The development of an indoor location information system using ubiquitous resources available in the environment is a challenging problem in the field of Geo-Location technologies, these days. Therefore, instead of relying on a single resource, the fusion of location information from multiple resources into an indoor positioning system (IPS) becomes important. The IPS in which information from multiple sources such as Wi-Fi, geomagnetism, and motion sensors is fused to harvest the next level of accuracy is commonly known as hybrid IPS. The initial estimate of the position with high accuracy is very critical for the hybrid IPS. Wi-Fi fingerprinting is one of the potential candidates for providing the initial position in such systems, whereas due to the multipath, absorption, and fading characteristics of the indoor environment, the accuracy of the Wi-Fi fingerprinting techniques is limited. Many algorithms and techniques have been proposed to improve the accuracy of Wi-Fi-based IPSs. However, most of the solution requires high computing resources and specialized hardware. This article proposes an empirical approach in which the important features present in the received signal strength vector (RSSV) of the Wi-Fi device are selected to exploit the similarity measure and index order of the Access Points (APs). The experimental results show that these features make it possible to avoid long distances outliers and to improve the positioning accuracy of the Wi-Fi fingerprinting technique without the use of specialized hardware.

Target localization is an important research field in wireless sensor networks and the received signal strength (RSS)-based method is of particular interest. Since traditional RSS-based method is invalid for simultaneously localizing multiple targets, the compressive sensing theory has been applied to develop an effective localization framework in recent years. However, most existing works implicitly assume the transmit powers of targets are known and constant, which contradicts the practical application scenario as the power might change with time and usually difficult to be known. In this paper, we consider the challenging problem of multiple target localization with unknown and time-varying transmit powers by exploiting multiple measurement vectors (MMVs). Since the conventional MMV formulation is incapable of the time-varying environment, we develop a novel MMV framework, in which the temporal correlation of varying powers can be further exploited. In this way, the problem is transformed to reconstruct a block-sparse signal and learn its inner correlation structure. To address this, a block-sparse reconstruction algorithm is designed, based on the novel application of the variational Bayesian approximation and the expectation-maximization methodology. The proposed algorithm can jointly estimate locations and varying powers of multiple targets with high accuracy. The extensive simulation results demonstrate the superiority of the proposed method in comparison with the state-of-the-art algorithms.

The received signal strength (RSS)-based target localization is an important field of research with numerous applications in wireless sensor networks. By exploiting the sparsity of localization, the compressive sensing (CS) can be applied to develop an effective localization framework for multiple targets. However, the existing CS-based method considers localization directly from real-valued sensor measurements, which implicitly assume that the measurements have infinite bit precision. When the assumption is violated, the localization performance will deteriorate dramatically, especially at low quantization bit rates. In this paper, we first design a quantizer for efficiently processing the raw RSS measurements and then develop a novel Bayesian CS framework for estimating target locations from the quantized measurements. By modeling the quantization errors as independent random variables, the non-linear localization problem is formulated in a linear CS observation model. Following this idea, we solve the problem from a Bayesian perspective and design some sophisticated prior distributions to guarantee the performance. To address this, we resort to the variational Bayesian inference methodology and propose a novel iterative algorithm for jointly estimating target locations and dealing with quantization errors. The extensive simulation results demonstrate the superiority of the proposed algorithm in comparison with the state-of-the-art methods.

At present, WiFi fingerprinting indoor positioning technology is a research hotspot, and the construction of a radio map based on crowdsourcing can significantly reduce the amount of labor required. However, when users collect fingerprint information using crowdsourcing data in practical applications, the received signal strength (RSS) information collected by user phones updates slowly and will remain the same for a certain distance and time. Therefore, crowdsourcing data is inaccurate, and a radio map thereby established will also lead to inaccurate indoor positioning. In order to address this issue, this paper proposes a method for extracting effective RSS information from crowdsourcing data in order to establish the radio map and achieve positioning. By analyzing the crowdsourcing data characteristics, effective RSS information is identified and extracted. Based on inertial sensors data, the position of effective RSS information is determined. The effective crowdsourcing data associated with the position is weighted and fused in order to establish the radio map. Aimed at the issue of the RSS collected by phones updating slowly, a combined positioning method for position calculation is proposed in order to achieve effective positioning. Experiments were conducted in a real environment to validate the algorithm, and the positioning accuracy could reach 1.5 m by using crowdsourcing data to construct the radio map and executing an indoor positioning algorithm, which is close to the accuracy achieved when using manual data collected by professionals at a certain distance and saves a great deal of labor.

The emerging location-based applications depend on the fast and accurate positioning of mobile targets. Wi-Fi received signal strength (RSS) fingerprinting provides a promising solution to localize an object in indoor environments. Among the factors challenging the RSS fingerprinting based algorithms are the site survey cost and the time-varying environment, given the unreliable signal qualities. Here we present a novel approach to indoor object positioning using the manifold assumption on the radio map in RSS-location space. Thinking the measured RSS from one access point (AP) in different locations are randomly drawn from a nonlinear manifold (ground truth), we propose an expectation-maximization (EM) style algorithm to reconstruct the sparse representation of this manifold from the noisy RSS observations. Motivated by the observation that the radio map has a strong local correlation in the RSS-location space, we introduce a multi-scale constrained quadratic programming to approximate the manifold. Within limited iterations, we can estimate the ground truth RSS values and parameters simultaneously. As a result, the learned manifold is exploited to predict the object's position: we develop a positioning algorithm by minimizing the manifold distortion effort which integrates both measurement error and manifold shape preservation. We conducted extensive simulations and experiments in different settings, testing the datasets collected in a building in the last 8 months. The results showed that the proposed approach was adaptive to the varying environmental noise levels, presenting robust positioning performance.

To improve localization accuracy and reduce system costs, a new range-free localization algorithm for wireless sensor networks using connectivity and received signal strength (RSS) rank is proposed. An unknown node is first localized in an initial residence area according to the connectivity constraints. Then, the residence area is refined using the RSS rank vector. The RSS rank vector of an unknown node is formed from the ranks of the RSS values obtained from neighboring anchors. The estimated location of the unknown node is the centroid of the refined residence area. The proposed localization algorithm uses a grid scan approach to avoid the complex geometric computations. This algorithm is improved by using an adaptive strategy to determine the grid size. The performance of these methods is examined via analysis and simulation. The results obtained verify that the proposed approaches provide better localization accuracy than competing algorithms in the literature.

Source localization is an important field of research and the received signal strength (RSS)based method is of particular interest. Further, by exploiting the sparsity of localization problem, the compressive sensing (CS) can be applied to considerably decrease the number of RSS measurements, especially in multi-source scenario. However, most existing CS-based localization methods usually neglect some practical issues. In particular, the sensor positions are assumed be known exactly, while in practice they may not be accurate. Additionally, a uniform Gaussian noise assumption is made that the noise variances of sensors are identical, but in practice the noise should be nonuniform. When these assumptions are violated, the localization performance will deteriorate dramatically. To address such issues, in this paper, we formulate the source localization based on superimposed RSS measurements as a sparse signal recovery problem. Moreover, by regarding the sensor positions as adjustable parameters, the inaccurate sensor positions can be refined through the adjustment of parameters. Following this idea, we develop a novel iterative algorithm for joint signal recovery and parameter optimization based on the variational expectation-maximization algorithm. Consequently, the sensor position uncertainty can be alleviated and thus the signal recovery performance will be improved greatly. Meanwhile, it is also capable of learning the variance of nonuniform noise. Extensive simulation results demonstrate the superiority of the proposed method.

Indoor target tracking is a crucial application of indoor localization systems. Wi-Fi fingerprint-based target tracking/positioning has been extensively studied due to its deployability under pervasive indoor wireless local area networks. However, the majority of fingerprint-based schemes adopted the static radio map and did not make full use of continuous motion information of the target. Therefore, we build a ubiquitous indoor tracking/localization system that can maintain a dynamic fingerprint database at low cost in this paper. First, we propose an optimal target tracking algorithm exploiting the historical data of the target location information to help improve the accuracy, which adopts a modified particle filter algorithm to reduce the number of samples and computing overhead. Second, we present a database self-update method according to trajectory continuity, which is employed to update fingerprint database dynamically for keeping the system robust. Finally, we tested the optimal target tracking based on dynamic fingerprint algorithm (OTTDF) in a complex laboratory area with diverse target motion conditions and various obstacles, our experimental results indicate that the OTTDF scheme successfully handles complex indoor structure, including different target motion state, signal fingerprint changes caused by obstacles, simultaneously provides better performance in localization cost, and localization/tracking accuracy in indoor wireless network.

One of the notable aspects of public safety applications in 5G networks is location awareness, which is a feature of contemporary technology that imparts details about a user`s geographical location to another user or application. Mobile users take their cell phones and other gadgets with them almost everywhere. Thus, integrating location awareness in mobile applications gives users a much more real experience. Therefore, this paper presents an algorithm to predict user location in 5G networks by using received signal strength measurements. Initially, the relative coordinates of users are computed using Isomap. Then the relative coordinates of users are transformed by Procrustes analysis. In order to evaluate the performance of the proposed algorithm, the Cramer-Rao lower bound is derived, which is the lower bound on error variance. It can be concluded from our results that the proposed approach outperforms those found in the existing literature.