Localization In Wireless Networks Research Articles

Hybrid TOA/AOA Localization for Indoor Multipath-assisted Next-Generation Wireless Networks

The next-generation wireless communication systems (NGWCs) necessitate integrating effective communication and localization techniques. However, the multipath badly affects both communication and localization, especially in indoor NGWCs. Therefore, we propose a novel hybrid localization method based on time and angle-based ranging estimation to address this issue. This method employs an improved matrix enhancement matrix pencil algorithm (IMEMP) to obtain the time of arrival (TOA) and corresponding angle of arrival (AOA) of each path. Two distinct localization methods are presented: First arrival path-based maximum likelihood (ML) and scatterer-assisted. Both methods are designed to mitigate the adverse effects of serious non-line-of-sight (NLOS) scenarios. The proposed scatterer-assisted method demonstrates a noteworthy 20% improvement in performance compared to scenarios where only the first path is considered, particularly in specific areas. These findings highlight the potential of the proposed approach to enhance localization accuracy in challenging wireless environments, offering valuable insights for the advancement of NGWCs.

Resource Optimization Using a Step-by-Step Scheme in Wireless UWB Sensing and Localization Networks

Wireless localization and target sensing both rely on precise extraction of parameters such as signal amplitude, propagation delay, and Doppler shift from the received signals. Due to the high multi-path resolution and strong penetration, both localization and sensing can be achieved through identical UWB waveforms. In this paper, we try to properly allocate resources for localization and sensing to fully exploit the potential of UWB systems. Considering the complexity of the multi-slot networks, we derive the Fisher Information Matrix (FIM) expressions for single-slot and dual-slot integrated sensing and localization (ISAL) networks, respectively, and propose two resource optimization schemes, namely the step-by-stepscheme and the integrated scheme, respectively. The numerical results show that: (i) for the sensing-resource-limited networks with relatively uniform node distribution, the energy allocated to each step in the step-by-step scheme satisfies the relationship energy for clock offset < energy for radar localization < energy for target sensing; (ii) in the multi-slot ISAL networks, more energy will be allocated to the time slots where the networks are relatively sensing-resource-limited; (iii) the step-by-step scheme is more suitable for the sensing-resource-abundant networks, while the integrated scheme is more suitable for the sensing-resource-limited networks.

Accurate Localization in LOS/NLOS Channel Coexistence Scenarios Based on Heterogeneous Knowledge Graph Inference

Accurate localization is one of the basic requirements for smart cities and smart factories. In wireless cellular network localization, the straight-line propagation of electromagnetic waves between base stations and users is called line-of-sight (LOS) wireless propagation. In some cases, electromagnetic wave signals cannot propagate in a straight line due to obstruction by buildings or trees, and these scenarios are usually called non-LOS (NLOS) wireless propagation. Traditional localization algorithms such as TDOA, AOA, etc. , are based on LOS channels, which are no longer applicable in environments where NLOS propagation is dominant, and in most scenarios, the number of base stations with LOS channels containing users is often small, resulting in traditional localization algorithms being unable to satisfy the accuracy demand of high-precision localization. In addition, some nonideal factors may be included in the actual system, all of which can lead to localization accuracy degradation. Therefore, the approach developed in this paper uses knowledge graph and graph neural network (GNN) technology to model communication data as knowledge graphs, and it adopts the knowledge graph inference technique based on a heterogeneous graph attention mechanism to infer unknown data representations in complex scenarios based on the known data and the relationships between the data to achieve high-precision localization in scenarios with LOS/NLOS channel coexistence. We experimentally demonstrate a spatial 2D localization accuracy level of approximately 10 meters on multiple datasets and find that our proposed algorithm has higher accuracy and stronger robustness than the state-of-the-art algorithms.

A Min-Max Optimization-Based Approach for Secure Localization in Wireless Networks

A Min-Max Optimization-Based Approach for Secure Localization in Wireless Networks

Integrated Sensing, Localization, and Communication in Holographic MIMO-Enabled Wireless Network: A Deep Learning Approach

Integrated Sensing, Localization, and Communication in Holographic MIMO-Enabled Wireless Network: A Deep Learning Approach

Topology-based multi-jammer localization in wireless networks

Jamming attacks and unintentional radio interference are some of the most urgent threats harming the dependability of wireless communication and endangering the successful deployment of pervasive applications built on top of wireless networks. Unlike the traditional approaches focusing on developing jamming defense techniques without considering the location of jammers, we take a different viewpoint that the jammers’ position should be identified and exploited for building a wide range of defense strategies to alleviate jamming. In this paper, we address the problem of localizing multiple jamming attackers coexisting in wireless networks by leveraging the network topology changes caused by jamming. We systematically analyze the jamming effects and develop a framework that can partition network topology into clusters and can successfully estimate the positions of multiple jammers even when their jamming areas are overlapping. Our experiments on a multi-hop network setup using MicaZ sensor nodes validate the feasibility of real-time collection of network topology changes under jamming and our extensive simulation results demonstrate that our approach is highly effective in localizing multiple attackers with or without the prior knowledge of the order that the jammers are turned on.

Scheduling in Wireless Cooperative Localization Networks with Intelligent Eavesdroppers

Location-based service based on wireless cooperative localization networks is becoming ubiquitous nowadays. However, since the fact that most location network nodes are resource-limited, recent investigations focus on the proper network scheduling strategies that can significantly enhance the system performance, including but not limited to localization accuracy and energy efficiency. In addition to the current efficient nondata-aided strategies, we find that some silent nodes, called “eavesdroppers,” can be helpful to the localization task without transmitting any signals. In this paper, we first formulate the eavesdropping scheduling policy in practical asynchronous cooperative wireless localization networks. Then, we perform resource optimization in different eavesdropping-based strategies. Both two-slot and multislot strategies are considered, and three types of listening modes are designed from a practical point of view. Numeric results show that, for the scenario with a blocked propagation path, the localization error of networks with dedicated eavesdroppers is only 21% of the conventional networks. Besides, the system with eavesdropping anchors could improve the localization performance by 70%. The result could provide meaningful insights into the practical low-complexity location network deployment and development.

Understanding LoRa-Based Localization: Foundations and Challenges

LoRa is a wireless technology for the Internet of Things useful for smart city applications that require node location for monitoring or management. The literature presents multiple surveys for localization in wireless networks but not a comprehensive analysis regarding LoRa localization. Therefore, this article presents different localization techniques along with the metrics employed. Then, the paper presents the challenges that LoRa technology faces when using these techniques and possible solutions from the literature. Next, the paper presents a comparative analysis of the reviewed works with unresolved issues. The results reveal that systems using time as a localization metric exhibit smaller localization error than those using signal levels. However, each method was tested under different conditions. Hence, this article is useful for comparing and selecting a localization system for LoRa in a given work scenario.

Joint Localization and Communication Study for Intelligent Reflecting Surface Aided Wireless Communication System

The intelligent reflecting surface (IRS) is promising in assisting user localization and wireless communication in the future wireless networks. In this paper, a novel IRS-aided joint localization and communication (L&C) scheme is designed in a millimeter-wave transmission system. For the proposed scheme, the user position/orientation estimation error bound (POEB) and the effective achievable data rate (EADR) are derived in closed-form as L&C performance metrics, which reveal the inherent trade-off between L&C capabilities. To achieve the joint optimal point of the POEB and EADR in consideration of the localization errors, a worst-case robust beamforming and time allocation optimization problem is formulated. To solve the original non-convex problem, a novel joint optimization approach is developed. Specifically, from an equivalent minimax problem, the local optimal solutions of the transceiver beamformers, the IRS phase-shift matrix, and the time allocation ratio between user localization stage (ULS) and effective data transmission stage (EDTS), are obtained in closed-form with respect to the localization errors. Then, the worst-case localization error is iteratively found by a dedicated majorize-minimization (MM) based algorithm. Subsequently, potential extensions to general wireless channels and discrete phase-shift models are discussed in detail. Finally, simulations are carried out to show the optimization results and the L&C performance trade-off. In comparison with the conventional non-robust method, the proposed approach is validated to be robust against the user localization uncertainty.

Mobile Node Localization in Wireless Networks: Path-Loss Model, Trilateration, and Error Mitigation in a 5G Sub-6 GHz Scenario

In this paper, we proffer a novel technique designed for low-cost and computationally light localization of mobile nodes in an urban terrain, by leveraging the extended COST 231 Hata Path-Loss (PL) model and the Trilateration technique. Our approach accounts for the possibility of a Non-Line-of-Sight (NLoS) scenario in a medium-sized city, wherein one of the three reference nodes required for the trilateration approach encounters NLoS impediments. Our proposed method proceeds with localization by utilizing solely two Line-of-Sight (LoS) reference nodes, while integrating the localization system simulator with an Extended Kalman Filter (EKF). The simulation results presented herein demonstrate a marked enhancement in performance, surpassing that of trilateration in scenarios where three LoS nodes cannot be established.

Localization in Cellular and Heterogeneous Networks for 5G and Beyond: A Review

Localization in modern and future wireless networks has been established as an important field of research work due to the requirements of location-based applications and services with variety of accuracy requirements. These are driven by the strong heterogeneity in terms of processing power, size and range of the nodes in beyond Fifth Generation (5G) telecommunications. Thus, localization methods in cellular and heterogeneous networks (Het-Nets) diversify in their application scenario (terrestrial and based on aerial platforms) and bands (licensed and unlicensed). They are categorized, according to the methodology used to perform the positioning, into three groups – fingerprinting (learning-based location estimation), trilateration and triangulation (distance or angular based), and hybrid (combining two geometric features of the received signals) methods. For each category, a summary of the methods’ design features and achieved accuracy is presented in tabular form. On the basis of the review, directions for future research are outlined, that will facilitate the further advancements in the design and application of localization methods for wireless communications.

To Hide Anchor’s Position in Range-Based Wireless Localization via Secret Sharing

The anchors in wireless network localization system suffer from the risk of location leakage, which may result in some catastrophic dangers. To preserve the location privacy of anchors, this letter proposes to hide the anchor’s position in range-based localization through a secret sharing method. By decomposing the private input of anchors into several secret shares and concealing it in the constant term of the secret polynomial, the location information of anchors cannot be disclosed by any other node in the network including the server, during the entire localization process. The performance of the proposed scheme in privacy preservation, positioning accuracy and efficiency improvement (i.e., low communication and computation costs) are evaluated through simulations.

Indoor Localization in Wireless Networks Using Received Signal Strength: A Model using Bregman Distance to Increase Public Utility Benefits

Indoor Localization in Wireless Networks Using Received Signal Strength: A Model using Bregman Distance to Increase Public Utility Benefits

Cooperative Localization in Massive Networks

Network localization is capable of providing accurate and ubiquitous position information for numerous wireless applications. This paper studies the accuracy of cooperative network localization in large-scale wireless networks. Based on a decomposition of the equivalent Fisher information matrix (EFIM), we develop a random-walk-inspired approach for the analysis of EFIM, and propose a position information routing interpretation of cooperative network localization. Using this approach, we show that in large lattice and stochastic geometric networks, when anchors are uniformly distributed, the average localization error of agents grows logarithmically with the reciprocal of anchor density in an asymptotic regime. The results are further illustrated using numerical examples.

An Improved Sparrow Search Algorithm for Node Localization in WSN

Wireless sensor networks (WSN) comprise a set of numerous cheap sensors placed in the target region. A primary function of the WSN is to avail the location details of the event occurrences or the node. A major challenge in WSN is node localization which plays an important role in data gathering applications. Since GPS is expensive and inaccurate in indoor regions, effective node localization techniques are needed. The major intention of localization is for determining the place of node in short period with minimum computation. To achieve this, bio-inspired algorithms are used and node localization is assumed as an optimization problem in a multidimensional space. This paper introduces a new Sparrow Search Algorithm with Doppler Effect (SSA-DE) for Node Localization in Wireless Networks. The SSA is generally stimulated by the group wisdom, foraging, and anti-predation behaviors of sparrows. Besides, the Doppler Effect is incorporated into the SSA to further improve the node localization performance. In addition, the SSA-DE model defines the position of node in an iterative manner using Euclidian distance as the fitness function. The presented SSA-DE model is implanted in MATLAB R2014. An extensive set of experimentation is carried out and the results are examined under a varying number of anchor nodes and ranging error. The attained experimental outcome ensured the superior efficiency of the SSA-DE technique over the existing techniques.

Review on Unmanned Aerial Vehicle Assisted Sensor Node Localization in Wireless Networks: Soft Computing Approaches

Node positioning or localization is a critical requisite for numerous position-based applications of wireless sensor network (WSN). Localization using the unmanned aerial vehicle (UAV) is preferred over localization using fixed terrestrial anchor node (FTAN) because of low implementation complexity and high accuracy. The conventional multilateration technique estimates the position of the unknown node (UN) based on the distance from the anchor node (AN) to UN that is obtained from the received signal strength (RSS) measurement. However, distortions in the propagation medium may yield incorrect distance measurement and as a result, the accuracy of RSS-multilateration is limited. Though the optimization based localization schemes are considered to be a better alternative, the performance of these schemes is not satisfactory if the distortions are non-linear. In such situations, the neural network (NN) architecture such as extreme learning machine (ELM) can be a better choice as it is a highly non-linear classifier. The ELM is even superior over its counterpart NN classifiers like multilayer perceptron (MLP) and radial basis function (RBF) due to its fast and strong learning ability. Thus, this paper provides a comparative review of various soft computing based localization techniques using both FTAN and aerial ANs for better acceptability.

Pairwise Node Localization From Differences in Their UWB Channels to Observer Nodes

We consider the problem of localization and distance estimation between a pair of wireless nodes in a multipath propagation environment, but not the usual way of processing a channel measurement between them. We propose a novel paradigm which compares the two nodes' ultra-wideband (UWB) channels to other nodes, called observers. The main idea is that the dissimilarity between the channel impulse responses (CIRs) increases with d and allows for an estimate <formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex>$\hat{d}$</tex></formula> . Our approach relies on extracting common multipath components (MPCs) from the CIRs. This is realistic in indoor or urban scenarios and if d is considerably smaller than the distances to the observers. We present distance estimators which utilize the rich location information contained in MPC delay differences. Likewise, we present estimators for the relative position vector which process both MPC delays and MPC directions. We do so for various important cases: with and without time synchronization, delay measurement errors, and knowledge of the MPC association between the two CIRs. The estimators exhibit great technological advantages: they do not require line-of-sight conditions or knowledge about the observer locations or the environment. We study the estimation accuracy with a numerical evaluation based on random sampling and, additionally, with an experimental evaluation based on measurements in an indoor environment. The proposal shows the potential for great accuracy in theory and practice. We describe how the paradigm could be incorporated into established cooperative localization frameworks to provide them with novel inter- and intra-node measurements for spatio-temporal tracking. This could enable low-cost localization of wireless users or networks in dynamic multipath settings.

Robust localization in wireless networks from corrupted signals

We address the problem of timing-based localization in wireless networks, when an unknown fraction of data is corrupted by non-ideal propagation conditions. While timing-based techniques can enable accurate localization, they are sensitive to corrupted data. We develop a robust method that is applicable to a range of localization techniques, including time-of-arrival, time-difference-of-arrival and time-difference in schedule-based transmissions. The method is distribution-free, is computationally efficient and requires only an upper bound on the fraction of corrupted data, thus obviating distributional assumptions on the corrupting noise. The robustness of the method is demonstrated in numerical experiments.

Transfer Learning Promotes 6G Wireless Communications: Recent Advances and Future Challenges

In the coming 6G communications, network densification, high throughput, positioning accuracy, energy efficiency, and many other key performance indicator requirements are becoming increasingly strict. In the future, how to improve work efficiency while saving costs is one of the foremost research directions in wireless communications. Being able to learn from experience is an important way to approach this vision. Transfer learning (TL) encourages new tasks/domains to learn from experienced tasks/domains for helping new tasks become faster and more efficient. TL can help save energy and improve efficiency with the correlation and similarity information between different tasks in many fields of wireless communications. Therefore, applying TL to future 6G communications is a very valuable topic. TL has achieved some good results in wireless communications. In order to improve the development of TL applied in 6G communications, this article performs a comprehensive review of the TL algorithms used in different wireless communication fields, such as base stations/access points switching, indoor wireless localization and intrusion detection in wireless networks, etc. Moreover, the future research directions of mutual relationship between TL and 6G communications are discussed in detail. Challenges and future issues about integrate TL into 6G are proposed at the end. This article is intended to help readers understand the past, present, and future between TL and wireless communications.

AIGSA-based multi-jammer localization in wireless networks

Jamming attacks, which exploit the shared and openness nature of the wireless mediums, are one of the major threats to wireless networks since they may significantly degrade the performance of or even corrupt wireless networks by continuously preventing wireless channels from serving intended users. To facilitate conducting anti-jamming countermeasures, a few single jammer localization algorithms have been introduced and evaluated. In contrast, only few efforts have been made for locating multiple jammers, for instance X-ray and Cluster algorithms. They are developed based on the assumption that a jammer’s affected area is roughly a circle; however, their localization accuracy is unsatisfactory if multiple jammers’ affected areas are overlapped. In this backdrop, a Multi-jammer Localization Algorithm based on Alternating Iteration and Gravitational Search Algorithm (MLA-AIGSA) is put forward in this paper. On the contrary to existing proposals, MLA-AIGSA neither relies on the mapping between the received signal power and two nodes’ mutual distance nor counts on the ideal assumption about the shape of a jammer’s affected area. MLA-AIGSA contains two steps, i.e. estimating the number of jammers and Multi-jammer localization. At first, Region Growth Algorithm (RGA) is adopted to derive the number of jammers based on collected Received Jamming Signal Strength (RJSS) values. Then, Alternating Iteration (AIt) and GSA are combined to estimate and refine multiple jammers’ positions in an iterative way. The overall time complexity of MLA-AIGSA is in quadratic time to the number of the particles and is in linear time to the number of AIt’s and GSA’s iterations. Extensive experiments are conducted to validate MLA-AIGSA’s correctness and effectiveness, and the results show that MLA-AIGSA’s average localization error (around 3 m) is nearly 70% lower than state-of-the-art proposals, including X-ray (17 m) and Cluster (10 m) algorithms.