This article analyzes the localization performance of fingerprinting positioning system from the perspectives of the signal measurement and positioning algorithm. Unlike the existing works which have not taken the influence of grid size into account, first of all, we construct a novel derivation model involving the grid size information. Then, from the signal measurement's perspective, the localization performance is analyzed based on our new model under two cases: with specific and nonspecific signal distributions. For the first case, we utilize the traditional knowledge of Cramér-Rao lower bound (CRLB) to rededuce it. For the second case, a Gaussian-Markov theorem method is introduced to conduct derivation. Furthermore, from the latter perspective, we first analyze the localization performance of the mostly used k-nearest neighbors (KNN) algorithm leveraging the probability density function (PDF) of these nearest neighbors. Then, a novel adaptive KNN algorithm is designed based on the derivation result, which has improved the location accuracy by about 20%. Finally, extensive simulations and real experiments are conducted to show the effectiveness of our claims.

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.

Smart indoor living advances in the recent decade, such as home indoor localization and positioning, has seen a significant need for low-cost localization systems based on freely available resources such as Received Signal Strength Indicator by the dense deployment of Wireless Local Area Networks (WLAN). The off-the-shelf user equipment (UE’s) available at an affordable price across the globe are well equipped with the functionality to scan the radio access network for hearable single strength; in complex indoor environments, multiple signals can be received at a particular reference point with no consideration of the height of the transmitter and possible broadcasting coverage. Most effective fingerprinting algorithm solutions require specialized labor, are time-consuming to carry out site surveys, training of the data, big data analysis, and in most cases, additional hardware requirements relatively increase energy consumption and cost, not forgetting that in case of changes in the indoor environment will highly affect the fingerprint due to interferences. This paper experimentally evaluates and proposes a novel technique for Received Signal Indicator (RSSI) distance prediction, leveraging transceiver height, and Fresnel ranging in a complex indoor environment to better suit the path loss of RSSI at a particular Reference Point (RP) and time, which further contributes greatly to indoor localization. The experimentation in different complex indoor environments of the corridor and office lab during work hours to ascertain real-life and time feasibility shows that the technique’s accuracy is greatly improved in the office room and the corridor, achieving lower average prediction errors at low-cost than the comparison prediction algorithms. Compared with the conventional prediction techniques, for example, with Access Point 1 (AP1), the proposed Height Dependence Path–Loss (HEM) model at 0 dBm error attains a confidence probability of 10.98%, higher than the 2.65% for the distance dependence of Path–Loss New Empirical Model (NEM), 4.2% for the Multi-Wall dependence on Path-Loss (MWM) model, and 0% for the Conventional one-slope Path-Loss (OSM) model, respectively. Online localization, amongst the hearable APs, it is seen the proposed HEM fingerprint localization based on the proposed HEM prediction model attains a confidence probability of 31% at 3 m, 55% at 6 m, 78% at 9 m, outperforming the NEM with 26%, 43%, 62%, 62%, the MWM with 23%, 43%, 66%, respectively. The robustness of the HEM fingerprint using diverse predicted test samples by the NEM and MWM models indicates better localization of 13% than comparison fingerprints.

With the pervasive deployments of Access Point (AP) in Wireless Local Area Network (WLAN), rogue AP has emerged as such a large threat to user's privacy, that it is expected to be detected and located accurately. Hence, in this paper, we propose a novel rogue AP localization scheme leveraging sparse recovery technique, which consists of three steps: 1) coarse localization, which is the result of comparing the online records with clustered fingerprint database. A novel Object Weighting Affinity Propagation (OWAP) clustering method is proposed to group the offline fingerprints. When computing the similarities, unlike traditional affinity propagation clustering method which views each object equally, we utilize the prior physical coordinates information to assign weight to each object. 2) compressive sensing (CS) kernel optimization, in which the minimum number requirement of monitors in localization system is deduced through analyzing the problem formulation theoretically, and an Equiangular Tight Frame (ETF) based monitors selection scheme is presented to achieve higher location accuracy. 3) joint fine rogue AP localization and outlier detection through a formulation of an improved CS based sparse recovery model. It could localize the rogue AP and identify the monitors whose readings are either not available or erroneous simultaneously, which increases the localization robustness. We operate simulations as well as experiments to verify the superiority of the proposed scheme both theoretically and practically.

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.

The deep learning technique plays an important role in Wi-Fi localization systems as it could mine deep features of measurement data. The main challenges are to combat the signal fluctuation resulting in decrease of sample discrimination, and to leverage the broadest information of sample measurements during the training phase, since they are directly related to the location accuracy and robustness. Hence, to address the above issues, this paper proposes an indoor Wi-Fi localization scheme which mainly contains two modules. Firstly, an improved contrastive learning is introduced to handle the sample signal measurements to increase the discrimination. It is from the perspective of learning and encoding, and avoids the drawbacks brought by traditional processing methods. Then, we build a parallel fusion network named as PaCNN-LSTM based on convolutional neural network (CNN) and long short-term memory network (LSTM). Compared with existing networks, PaCNN-LSTM connects neural networks in parallel rather than serial, which improves the generalization performance of model when extracting the spatial and temporal features of signal measurements. In addition, it also considers the large amount of middle layer information that is always ignored. By adding a Flatten layer after the pooling layer, the available information of samples has been broadened. Extensive experimental results show that the localization performance of the proposed scheme is outperformed than others, where the location accuracy is improved by about 22%.