With the continuous development of 6G communication technology and artificial intelligence (AI), reconfigurable intelligent surface (RIS) technology and generative AI (GAI) have received widespread attention. Hence, combining these two techniques to solve the problem of nonlocalizability in single access point (AP) scenarios is promising. This article proposes a novel RIS-aided localization scheme via generative artificial intelligence (GAI). Specifically, first, considering the presence of a certain amount of noise and the low discriminative features of the original collected received signal strength (RSS) brought by integrated reflective elements of RIS, we construct a generative adversarial network (GAN) model named variational autoencoder-convolutional neural network (VAE-CNN). It can effectively perform noise reduction in the data preprocessing stage to reduce unnecessary redundant information, and extract more discriminative features through convolutional networks to improve the differentiation between data. Second, the target’s location in the spatial domain is formulated as a sparse vector, and then a sparse recovery model is introduced to solve the location estimation problem in RIS-aided localization scenarios. Finally, considering the case that RIS has multiple reflective elements, which will lead to a high dimension of the measurement matrix in the sparse recovery model, we further apply the semi-tensor product (STP) sparse recovery theory on the model to reduce the storage space and high time consumption. Experimental results show that our proposed methods outperform the traditional approaches and reduce the computational complexity simultaneously.

With the increasing popularity of smartphones, smartphone photography has become convenient and common in daily life, thus making visual localization technology receive widespread attention. Due that monocular cameras are mostly used on smartphones, the depth information from a single image cannot be obtained, so the image-matching-based localization technique is widely adopted. However, this method has the problems of high time consumption and vulnerable to environmental interference. Therefore, this article proposes a low overhead and robust indoor image positioning method, which mainly consists of two modules. First, an improved siamese neural network framework is introduced to train the similarity metric model between images, which significantly reduces the workload of labeling for large amounts of sample data. Moreover, it improves the robustness when the target environment contains similar image features in the matching stage. Second, to further estimate the user’s fine location, an adaptive random sample consensus algorithm is proposed to optimize the fundamental matrix in the classical EightPoint method, which could efficiently eliminate the matching outliers to solve the camera attitude, and dynamically adapt to more complex and changeable data situations. A large number of experimental results show that the positioning performance of this scheme is better than traditional schemes, and the average positioning error of 0.50 m can be achieved.

Neural network positioning technology, as one of the mainstream in indoor Wi-Fi positioning systems, is playing an increasingly important role in location-based services. The main challenge is that the samples are prone to be outdated as the indoor environment changes or the wireless signal varies over time, i.e., the samples’ Age of Information (AoI) is large, which leads to the trained model not being available. However, recollecting data to retrain the model is both time-consuming and labor-intensive. To address the above problem, this article proposes a fast adaptation approach based on Bayesian meta-learning that makes the pretrained model acquire a learned learning capability so that it can quickly learn new tasks based on the acquisition of existing knowledge. Specifically, first, a model-agnostic learning scheme is introduced to guide the learning process, which could automatically learn the optimal model parameters and hyperparameter settings. Second, to mitigate the effects of model uncertainty, especially to prevent the overfitting situation based on a limited number of samples, we combine the Stein variational gradient descent (SVGD) with the model-agnostic learning scheme, i.e., Bayesian meta-learning. Compared with traditional meta-learning algorithms, the proposed method makes the training more robust by inferring the Bayesian posterior from a probabilistic perspective. Extensive experimental results show that the proposed approach effectively overcomes the impact of large AoI on localization performance while decreasing labor consumption significantly.

The sparsity of the localization problem makes the compression sensing (CS) theory suitable for indoor localization in wireless local area networks (WLANs). However, in practice, we find that the location errors and computing complexity increase significantly as the dimensionality of the sparse vector and measurement matrix are high in a large environment, so most CS-based localization techniques are accompanied by coarse localization and access point (AP) selection stages. Therefore, in this article, we first deduced the relationship between the number of APs and the dimensionality of the sparse vector theoretically to give the guideline that the number of subdatabases and APs should be obtained. Then an adaptive intuitionistic fuzzy C-ordered mean (AIFCOM) clustering is designed for the data with outliers in the environment with multipath effects. Finally, in the fine localization stage, we propose a semi-tensor product CS (STP-CS) model to construct the measurement matrix, compared with the traditional CS model, our model not only remains more number of APs, but also decreases the dimensionality of measurement matrix, which can reduce the storage space and improve localization accuracy simultaneously.

As one of the key technologies of the 5G, the device-to-device (D2D) can realize communication between terminals without any base station, thus achieving more convenience of cooperative positioning. In this article, we propose a D2D cooperative positioning approach via matrix completion and anchor selection, which tackles the positioning problem with inadequate distance information. Specifically, first, an incomplete Euclidean distance matrix (EDM) is constructed by using insufficient distance information between nodes, and then the singular value thresholding (SVT) algorithm is used to recovery this EDM to obtain completed information. Second, multidimensional scaling (MDS) is performed to reduce dimensions of recovered EDM, which aims to obtain the relative positions of nodes while maintaining the distance relationship among them. Third, a set of suitable anchor nodes is selected by using the Hodges–Lehmann (HL) test for position transformation. Finally, we apply the procrustes analysis (PA) to transform the relative positions to the global ones according to the selected set of suitable anchor nodes. From the extensive experimental results, it is evident that the proposed approach has high positioning accuracy even when a large proportion of elements are missing in the EDM.

Different from the social network which only focuses on the interaction between people, the integration of social networks and the internet of things (IoT) leads to multi-directional interactions of human to human, human to thing, and thing to thing. The social IoT is composed of a large number of heterogeneous devices, which can improve the scalability of resource and service. Meanwhile, the heterogeneous devices have uneven computing power and different location information measurement types (e.g., the distance, angle, and hop count). Therefore, a localization approach with easy-to-obtain measurement data and low requirements on the computing power is needed. In this article, we propose a localization approach for the social IoT by combining the fuzzy rough set theory and the ridge regression extreme learning machine (RRELM). First of all, a location fingerprint database is constructed. Different from the traditional location fingerprint database, the location fingerprint database here stores the minimum hop counts between the reference node (RN) and the anchor node (AN) instead of the received signal strength (RSS). Second, the fuzzy rough set theory is used to compute the significant degree of each AN, and the ANs that contribute little to the positioning result are removed. This approach not only relieves the storage pressure of the location fingerprint database but also reduces the computational complexity of user position estimation. Third, the RRELM is trained by using the samples in the location fingerprint database. Finally, by inputting the newly collected minimum hop counts from the user to each AN into the trained RRELM, the user’s position is estimated. From the extensive experimental results, the proposed approach has high positioning accuracy and low computational complexity, which is suitable for the social IoT.

Channel state information (CSI) can provide phase and amplitude of multichannel subcarrier to better describe signal propagation characteristics. Therefore, CSI has become one of the most commonly used features in indoor Wi-Fi localization. In addition, compared to the CSI geometric localization method, the CSI fingerprint localization method has the advantages of easy implementation and high accuracy. However, as the scale of the fingerprint database increases, the training cost and processing complexity of CSI fingerprints will also greatly increase. Based on this, this article proposes to combine backpropagation neural network (BPNN) and adaptive genetic algorithm (AGA) with CSI tensor decomposition for indoor Wi-Fi fingerprint localization. Specifically, the tensor decomposition algorithm based on the parallel factor (PARAFAC) analysis model and the alternate least squares (ALSs) iterative algorithm are combined to reduce the interference of the environment. Then, we use the tensor wavelet decomposition algorithm for feature extraction and obtain the CSI fingerprint. Finally, in order to find the optimal weights and thresholds and then obtain the estimated location coordinates, we introduce an AGA to optimize BPNN. The experimental results show that the proposed algorithm has high localization accuracy, while improving the data processing ability and fitting the nonlinear relationship between CSI location fingerprints and location coordinates.

Most of the representative lower positioning error bound (LPEB) derivation works of Wireless Local Area Network(WLAN) fingerprint-based positioning system are on the basis of Cramer-Rao Lower Bound (CRLB). However, there are some limitations, i) to the best of our knowledge, all existed works have not investigated the impact of grid size, which is one of the factors affecting the location accuracy; ii) traditional CRLB-based derivation takes the user's location coordinate as the basic estimated parameter vector, which is not exact because we actually estimate the nearest reference point (RP) to the user rather than estimating the user's location directly; iii) CRLB-based derivation has a fundamental premise that the signal obeys a specific signal distribution so as to formulate Probability Density Function (PDF) clearly, but for an irregular scenario, the signal may not obey one specific signal distribution and the PDF is unknown, which means CRLB is not available. Motivated by these limitations, this paper firstly constructs a new derivation model which takes grid size information into account, and revises the basic estimated parameter vector as the nearest RP's location. Then we deduce the LPEB in terms of the proposed new derivation model under two situations. Specifically, for a regular scenario with specific signal distribution, we re-deduce LPEB based on CRLB. Moreover, for an irregular scenario with non-specific signal distribution, we transform the observations into a linear pattern expression and apply the Gaussian-Markov theorem to conduct the LPEB derivation. Finally, the simulations and experiments are presented to support our claims.

With the pervasive infrastructures of WLAN, user's privacy has emerged as an important security and privacy problem. Rogue Access Points (AP), as one of the threat, is expected to be detected and located accurately. Therefore, in this paper, we propose a novel rogue AP localization method leveraging compressive sensing (CS) via kernel optimization. Although the CS based technique has been widely used in mobile user localization system, this is the first time to apply it to reversely localize AP. In addition, designing an appropriate kernel is the key to successful application of CS technique, however, traditional Gaussian or Bernoulli random kernels could not be utilized in rogue AP localization system, due that the kernel is related to the number and distribution of monitors, which could not randomly change every time. Hence, for CS kernel optimization, we firstly deduce the minimum number of monitors required in this system through a theoretical analysis which aims at justifying the validity of problem formulation. Then an Equiangular Tight Frame (ETF) based monitors distribution scheme is presented to achieve higher location accuracy. Finally, we perform both simulations and experiments to demonstrate the superiority of our approach as compare to other methods theoretically and practically.

With the fast growth of demand for the ubiquitous, precise, and instant indoor location information, the Received Signal Strength (RSS) based Wi-Fi indoor localization has been greeted with an avalanche of publicity. The studies in this field so far rarely consider the diversity of Wi-Fi signals, and thereby the RSS measures involving gross error on account of the complicated indoor environment deteriorate localization accuracy. In response to this compelling problem, we propose to use the concept of Asymptotic Relative Efficiency (ARE) to design a new synergetic test of Wi-Fi signal for indoor localization. Specifically, first of all, the Jarque-Bera (JB) test is conducted to test the normality of Wi-Fi signals at each Reference Point (RP). Second, the result of JB test is fed into the synergetic Mann-Whitney U and T test to construct the set of matching RPs corresponding to the newly-collected RSS data. Finally, the location coordinate of the target is obtained by calculating the K-nearest neighbor of matching RPs. Furthermore, the experimental results demonstrate that the proposed approach is featured with higher localization accuracy compared with the existing Wi-Fi indoor localization approaches.

With the widespread adoption of Wireless Local Area Network (WLAN) in indoor environment, indoor WLAN intrusion detection has become a key technique in various fields with the advantage of accomplishing intrusion detection without any requirement of special device or collaboration from the target. However, this technique is suffered by a serious problem that the offline database construction normally leads to high manpower and time cost especially for the large-scale indoor environment. To address this problem, a new indoor WLAN intrusion detection approach with low effort is proposed in this paper. Specially, first of all, the difference between the Received Signal Strength (RSS) data in source and target domains at the same locations is reduced by intra-class transfer learning with the purpose of applying the relations between the offline RSS data and their labels to the online RSS data. Second, the RSS data in target domain are classified by using the classifier trained from the relations of RSS data and the corresponding labels in source domain. Third, the iterative transfer learning between source and target domains is conducted to obtain the labels of RSS data in target domain. Finally, the experimental results demonstrate that the proposed approach is able to achieve high detection accuracy as well as the strong robustness to the number of RSS data used for database construction.

Access Point (AP) optimization is one of the most important components in indoor Wireless Local Area Network (WLAN) localization technique since the AP number and locations have significant impact on the variations of Received Signal Strength (RSS) in target environment. Different from the conventional AP optimization approaches, we propose to use the concept of adaptive channel power allocation to construct a water-filling model, and then conduct AP optimization based on the weights of candidate AP locations which are calculated by the fast discrete water-filling algorithm. The experimental results demonstrate that the proposed approach is able to achieve high localization precision, and meanwhile consume low time overhead.