Position Of Target Node Research Articles

Hybrid TOA-AOA WLS Estimator for Aircraft Network Decentralized Cooperative Localization

Cooperative localization is widely used to solve the network localization problem in global navigation satellite system (GNSS) denied environments. In particular, gathering the measurement results of all nodes on a specific node requires substantial time for aircraft networks. Therefore, methods simultaneously calculating all the target node positions are challenging to apply due to information delay. This study uses only two hops measurements information relying on the time of arrival (TOA) and angle of arrival (AOA) to propose a weighted least squares (WLS) method that decentralizes the calculation of a specific single target node position in a 3D environment. Moreover, the effect of TOA and AOA measurement errors on localization accuracy is analyzed based on the geometric relationship. Consequently, a novel weight-factor calculation method is designed. The simulation results show that the proposed cooperative localization method can improve localization accuracy compared with the decentralized non-cooperative method. Furthermore, the proposed hybrid approach provides superior localization accuracy than the weight-factor calculation method which only considers the distance.

An efficient 3D localization algorithm for compensating stratification effect in underwater acoustic sensor network

AbstractCommunication and networking plays a crucial role in data transmission in UASN. The sensor nodes in UASN are intermittently deployed randomly in the three‐dimension scenario. As a consequence, the three‐dimension based localization algorithm is the need for an hour. To fulfill the objective, we have proposed a localization algorithm I‐LASP (Improvement of localization algorithm for compensating stratification effect based on extended improved particle swarm optimization technique) in three‐dimensional UASN, based on compensation of stratification effect for the improvement of the performance parameters such as localization accuracy, ranging accuracy, convergence rate and execution time. To compute the accurate position of target nodes, EIPSO (Extended improved PSO) technique is applied, and the degree of coplanarity is checked before the calculation of distance among nodes in order to get the accurate location of target nodes. The Centroid method is used to initialize the position of sensor nodes, and the ray theory method is used to compensate the stratification effect on the layered ocean water. The proposed algorithm is compared to the existing LASP, Std PSO, and GNA‐ESSP (Gauss‐newton algorithm‐extended sound speed profile) localization algorithm. The proposed algorithm provides 34.50%, 38.87%, and 42.66% of high accuracy in terms of localization with low density of target sensor nodes and 37.96%, 29.58%, and 50.77% high accuracy in terms of localization with a high density of target sensor nodes respectively. The proposed algorithm is compared with LASP, GNA‐ESSP, and TDOA to obtain 66.84%, 71.14%, and 86.13% of high accuracy in terms of ranging with low density of target sensor nodes and 42.34%, 89.00%, and 95.08% high accuracy in terms of ranging with a high density of target sensor nodes respectively. Experimental results represents that the proposed algorithm obtains better performance in terms of localization accuracy, ranging accuracy, root mean square error, normalized localization error, execution time and convergence rate.

A localization algorithm using reliable anchor pair selection and Jaya algorithm for wireless sensor networks

In wireless sensor networks (WSNs) and large-scale IoT applications, node localization is a challenging process to identify the location of the target or unknown nodes for accurate information transmission between sensor nodes. Due to their ease of hardware implementation and suitability for large-scale WSNs, range-free localization techniques have been shown in previous studies. The existing range-free localization algorithms did not consider the anisotropy factors typically seen in WSNs, leading to poor positioning accuracy. We proposed a range-free localization solution that combines the benefits of geometric constraint and hop progress-based approaches to address this issue. Each unknown node categorizes the anchor node pairs into one of three proposed categories, and the discriminating conditions are designed using the geometric information provided by the combination of the anchor node pairs and unknown nodes. A node localization algorithm is proposed to determine the position of target nodes or unknown nodes and to reduce the effect of anisotropic factors in isotropic, O-shaped, and S-shaped anisotropic WSNs using the parameter-less Jaya algorithm (JA) and range-free method of reliable anchor pair (RAP) selection approach. In the case of anisotropic WSNs (AWSNs), finding the location of target nodes is more complicated. The presented work is compared with the existing node localization methods, including Distance Vector (DV)-maxHop, Particle Swarm Optimization (PSO), and Quantized Salp Swarm Algorithm (QSSA) based localization algorithms. The proposed approach provides improved localization accuracy compared to the existing node localization methods regarding the number of anchor nodes and node density. The proposed algorithm also looks at how the degree of irregularity and computation time affect the performance.

Execution of Multitarget Node Selection Scheme for Target Position Alteration Monitoring in MANET

In mobile network, nodes are normally placed in some locations after travelling with various speeds to another location. Packets were broadcast to some location receiver node, but they are moved to another location, due to that node is not able to receive those packets. Attacker node present in routing path should accept those packets, and it acts as original node. Communication privacy is reduced for mobile network. It improves the communication overhead and end to end delay. So, the proposed Enhanced Packet Acceptance for Target Position Alteration (EPATP) technique exactly monitors the target node position, depending on the position to assign the relay node for packet forwarding from sender to target node. Multiaccepter Assigning Algorithm is designed, and if any target node should not receive those packets, it provides another chance for packet receiving by next target node, and it assigns multiple target node for accuracte communication. It reduces communication overhead and end to end delay.

Localization by Salp Swarm Optimization with Doppler Effect in Wireless Sensor Networks

Wireless sensor networks (WSNs) have lately been widely used due to its abundant practice in methods that have to be spread over a large range. In any wireless application, the position precision of node is an important core component. Node localization intends to calculate the geographical coordinates of unknown nodes by the assistance of known nodes. In a multidimensional space, node localization is well-thought-out as an optimization problem that can be solved by relying on any metaheuristic’s algorithms for optimal outputs. This paper presents a new localization model using Salp Swarm optimization Algorithm with Doppler Effect (LOSSADE) that exploit the strengths of both methods. The Doppler effect iteratively considers distance between the nodes to determine the position of the nodes. The location of the salp leader and the prey will get updated using the Doppler shift. The performance validation of the presented approach simulated by MATLAB in the network environment with random node deployment. A detailed experimental analysis takes place and the results are investigated under a varying number of anchor nodes, and transmission range in the given search area. The obtained simulation results are compared over the traditional algorithm along with other the state-of-the-art methods shows that the proposed LOSSADE model depicts better localization performance in terms of robustness, accuracy in locating target node position and computation time.

Eavesdropper and Jammer Selection in Wireless Source Localization Networks

We consider a wireless source localization network in which a target node emits localization signals that are used by anchor nodes to estimate the target node position. In addition to target and anchor nodes, there can also exist eavesdropper nodes and jammer nodes which aim to estimate the position of the target node and to degrade the accuracy of localization, respectively. We first propose the problem of eavesdropper selection with the goal of optimally placing a given number of eavesdropper nodes to a subset of possible positions in the network to estimate the target node position as accurately as possible. As the performance metric, the Cramer-Rao lower bound (CRLB) related to the estimation of the target node position by eavesdropper nodes is derived, and its convexity and monotonicity properties are investigated. By relaxing the integer constraints, the eavesdropper selection problem is approximated by a convex optimization problem and algorithms are proposed for eavesdropper selection. Moreover, in the presence of parameter uncertainty, a robust version of the eavesdropper selection problem is developed. Then, the problem of jammer selection is proposed where the aim is to optimally place a given number of jammer nodes to a subset of possible positions for degrading the localization accuracy of the network as much as possible. A CRLB expression from the literature is used as the performance metric, and its concavity and monotonicity properties are derived. Also, a convex optimization problem and its robust version are derived after relaxation. Moreover, the joint eavesdropper and jammer selection problem is proposed with the goal of placing certain numbers of eavesdropper and jammer nodes to a subset of possible positions. Simulation results are presented to illustrate performance of the proposed algorithms.

Improving Positioning Accuracy of UWB in Complicated Underground NLOS Scenario Using Calibration, VBUKF, and WCA

Accurate localization of the shearer is key to achieving efficient, automated, unmanned mining. Unfortunately, the existing positioning algorithms usually yields low-precision, insufficient applicability, and unreliable final estimation due to the existence of non-line-of-sight (NLOS) error and also because they ignore the position error of anchor nodes (ANs). To bridge this technology gap, we developed a novel localization strategy by incorporating calibration, variational Bayesian unscented Kalman filter (VBUKF), total least squares (TLS), and water cycle algorithm (WCA) to enhance the location estimation accuracy of ultrawideband (UWB) positioning system in a complicated underground environment. First, calibration was implemented based on the selection of appropriate reference nodes (RNs) in line-of-sight (LOS) scenarios to estimate the scale-factor error, bias, and position errors for each AN; then the VBUKF smoothing with the consideration of time-variant measurement noise was used to reduce the interference of NLOS owing to the difficult separation of bias and NLOS error. Second, the TLS method was implemented to calculate the target node's position based on the smoothed distances and calibrated positions of ANs. Lastly, the WCA embedded scheme was executed to further ameliorate estimation accuracy. The experimental results demonstrated that the proposed TLS-WCA approach, after the implementation of calibration and VBUKF, was best able to improve the localization accuracy remarkably, and outperformed the other compared methods, which can efficiently attain higher estimation accuracy, highlighting the outstanding localization performance.

Network dynamic defense system based on holographic transformation

Aimed at the problem of the boundary defense easily being out of availability caused by the static network structure, a novel dynamic enterprise network defense system based on holographic transformation is designed and implemented. To increase the uncertainty of network structure and the position of target nodes, the network view is dynamically changed by constantly transforming endpoint information. Virtual endpoint mutation and hopping period selection are achieved by the hopping address generation module. It takes the advantages of anti-collision and good randomicity of the Chinese national SM3 hash algorithm. The full-cycle hopping period is managed and controlled by the hopping period management mechanism based on the ciphertext policy attribute-based broadcast holographic transformation. Experiments show that the system achieves expected security goals and has good interactivity and high stability.

Cramér-Rao lower bound analysis of RSS/TDoA joint localization algorithms based on rigid graph theory

With the popularity of Wi-Fi smart devices, location estimation has been used to improve localization accuracy in indoor environments, and the Cramér-Rao lower bound (CRLB) is used to evaluate the performance of the localization algorithm. In this paper, first, the fingerprint-based received signal strength (RSS) and the time difference of arrival (TDoA) were used to estimate the position of target nodes. Then, Wi-Fi smart devices in the indoor localization environment were chosen to be the assistant nodes (ANs) to establish the searching scope based on rigid graph theory. Finally, the CRLB of joint localization algorithm was proposed, and the performance of the algorithm was evaluated by experiments in an indoor environment of our laboratory. We analyzed the influence of several parameters on the localization errors and provided a consistent evaluation method for this type of indoor localization algorithm.

An intelligent multi-floor indoor positioning system for cloud-based environment

Optimal node positioning to ensure energy efficiency is a challenge in Wireless Sensor Networks (WSNs). Energy can be conserved in WSN when the sensors are positioned at optimum locations. In this paper, an intelligent scheme to form multi-floor clusters by subdividing the topology into regions is proposed. Some random nodes are elected as Cluster Heads (CHs). The CH determines the number of neighboring nodes in each floor of the indoor surrounding. It sends requests to the members in the cluster to measure the Received Signal Strength (RSS). The node positions are estimated by applying Extreme Learning Machine (ELM) on the recorded RSS fingerprints. Kernelized ELM provides access to high dimensional input space with less computational cost and time. The proposed scheme provides more efficient control over the network during fluctuations. Once clusters are set up, the RSS of each node is collected to create a fingerprint database. The target node position is determined by matching the currently recorded RSS with the stored fingerprint. The performance of the proposed intelligent clustering technique is analyzed for both static and mobile nodes with varying samples and reference points. Extensive experiments conducted show that the propounded scheme offers better scalability and reduced Cumulative Percentile of Error (CPE).

RSS-Based Indoor Localization Using Belief Function Theory

Received signal strength (RSS) is a simple and low-cost method of localization in wireless sensor networks (WSNs) and is of significant interest in ambient intelligence technologies. However, RSS-based indoor localization poses important challenges due to the intrinsic characteristics of RSS measurements. This paper proposes a localization approach that accounts for the imperfection of RSS measurements and the reliability of RSS sources to estimate the target node position in an indoor WSN environment. Non-Gaussian probability density functions are used to model RSS deviations more realistically in the context of indoor environments. In addition, the proposed approach uses the Dempster–Shafer theory to represent and combine separate pieces of information (evidence) provided by more or less reliable or conflicting RSS sources (anchor nodes) on the same hypotheses regarding the target node position. Experiments conducted in two different indoor environments demonstrate the effectiveness of the proposed approach in terms of its accuracy, robustness, and computation time and its superiority compared with state-of-the-art methods. Note to Practitioners —This paper was motivated by the problem of indoor localization in the context of ambient intelligence applications. The localization technique proposed in this paper exploits RSS measurements to estimate the target node position. This technology is very attractive to system designers, due to its simplicity and low cost. This paper also suggests a new approach using, on the one hand, the belief function theory to represent and manage the imperfection of RSS measurements and the reliability of the RSS sources, and, on the other hand, a more realistic modeling of the variability of RSS measurements due to interference and attenuation phenomena that strongly affect signal propagation in indoor environments. Experimental results obtained in two different indoor environments (a residential apartment and a laboratory) are provided to demonstrate the effectiveness of the proposed approach and its superiority compared to state-of-the-art localization techniques.

LEMOn: Wireless Localization for IoT Employing a Location-Unaware Mobile Unit

In recent years, much attention has been paid to wireless localization schemes that exploit receptions of messages sent by a mobile unit. However, existing methods assume an accurate knowledge of the location of the mobile unit and a precise propagation model of the actual radio environment. By getting rid of these two requirements, our proposed localization algorithms make mobility-assisted localization far more practical as we do not need to equip the mobile unit with a global positioning system or run a time-consuming campaign to survey radio environment. LEMOn estimates the position of target nodes by using known locations of a small set of fixed anchor nodes while receiving messages sent from a mobile unit from unknown arbitrary locations. LEMOn-M, on the other hand, solves the localization matching problem by mapping an arbitrary number of target nodes to the known set of locations. Both algorithms first estimate an inter-node distance using a similarity between received signal strength indicators of beacons received from the mobile unit. The conventional location estimators are then employed to localize target nodes with an unknown location. The obvious examples of real-world applications include but are not limited to unmanned aerial vehicle assisted wireless sensor networks and indoor IoT systems. The various simulations show that the two algorithms achieve a very high localization accuracy even in harsh radio environments while static localization techniques fail.

NLOS Identification and Positioning Algorithm Based on Localization Residual in Wireless Sensor Networks.

The problem of target localization in WSN (wireless sensor network) has received much attention in recent years. However, the performance of traditional localization algorithms will drastically degrade in the non-line of sight (NLOS) environment. Moreover, variable methods have been presented to address this issue, such as the optimization-based method and the NLOS modeling method. The former produces a higher complexity and the latter is sensitive to the propagating environment. Therefore, this paper puts forward a simple NLOS identification and localization algorithm based on the residual analysis, where at least two line-of-sight (LOS) propagating anchor nodes (AN) are required. First, all ANs are grouped into several subgroups, and each subgroup can get intermediate position estimates of target node through traditional localization algorithms. Then, the AN with an NLOS propagation, namely NLOS-AN, can be identified by the threshold based hypothesis test, where the test variable, i.e., the localization residual, is computed according to the intermediate position estimations. Finally, the position of target node can be estimated by only using ANs under line of sight (LOS) propagations. Simulation results show that the proposed algorithm can successfully identify the NLOS-AN, by which the following localization produces high accuracy so long as there are no less than two LOS-ANs.

A Fractional Programming Method for Target Localization in Asynchronous Networks

In this paper, we address the target device localization problem in the asynchronous networks. For the purpose of saving power resources, the target device is not synchronized with the anchor nodes, but is only required to listen to the signals transmitted from the anchors, which, however, introduces two extra nuisance parameters: the target’s clock skew and clock offset. By transforming the time-of-arrival measurements into time-difference-of-arrival measurements, the clock offset of the target’s clock is eradicated. However, there still exists the unknown clock skew, which may degrade the localization performance. Since the range of the clock skew is usually known as a priori , we assume that it follows a uniform distribution within this range. By doing so, we take it as a part of measurement noise and estimate the target node position only. To estimate the target node position, we formulate a fractional programming problem and further show that it can be solved by solving one single mixed semidefinite and second-order cone program (SD/SOCP). Simulation results illustrate the superior performance of the proposed method over the existing methods.

Upper bounds on position error of a single location estimate in wireless sensor networks

This paper studies upper bounds on the position error for a single estimate of an unknown target node position based on distance estimates in wireless sensor networks. In this study, we investigate a number of approaches to confine the target node position to bounded sets for different scenarios. Firstly, if at least one distance estimate error is positive, we derive a simple, but potentially loose upper bound, which is always valid. In addition assuming that the probability density of measurement noise is nonzero for positive values and a sufficiently large number of distance estimates are available, we propose an upper bound, which is valid with high probability. Secondly, if a reasonable lower bound on negative measurement errors is known a priori, we manipulate the distance estimates to obtain a new set with positive measurement errors. In general, we formulate bounds as nonconvex optimization problems. To solve the problems, we employ a relaxation technique and obtain semidefinite programs. We also propose a simple approach to find the bounds in closed forms. Simulation results show reasonable tightness for different bounds in various situations.

Improving Indoor Positioning Accuracy with Dense, Cooperating Beacons

We improve indoor positioning location accuracy by opportunistically enlisting wireless client devices to temporarily serve as unmanaged beacons (or access points). A two-phase positioning scheme is proposed; in the initial phase, a target node and its neighbors are located using managed APs with known locations. In the second phase, the target node's position is refined by neighbor nodes with uncertain locations serving as short-range wireless beacons themselves. Through simulations and analysis we explore the geographic conditions and noise models under which exploiting the availability of unmanaged beacons can improve location accuracy. We argue that as the spatial density of wireless devices grows, it is increasingly desirable to call on a preferred subset of neighbor nodes with uncorrelated anchor errors to serve as beacons to improve location accuracy.

Fingerprinting localisation with cruciate directional antennas for wireless sensor networks

This paper proposes a novel fingerprinting localisation scheme, called fingerprinting localisation with cruciate directional antennas (FLCDA), using only one anchor node equipped with four directional antennas whose orientations are perpendicular to adjacent ones. The proposed FLCDA consists of two phases. In the RSSI gathering phase, a sensor node is placed at reference positions to send signals. And the RSSI values of the signals received by the four antennas are stored in the anchor node as the positions' fingerprints. In the localising phase, the anchor node localises a target sensor node by receiving its signals with the four directional antennas. The position associated with the fingerprint most matched with the received signals' RSSI values is assumed to be the target node's position. We also design and implement some techniques to accelerate FLCDA without affecting the accuracy too much. FLCDA and its variants are also compared with the related ones.

Target Tracking with NLOS Detection and Mitigation in Wireless Sensor Networks

RN and SSR approaches are designed to detect NLOS propagation paths in this paper. When the NLOS propagation paths are detected, an estimation approach for the NLOS range errors is proposed by using residual-error decomposition. The approach can estimate the NLOS range errors quickly and effectively, even if there are multiple NLOS propagation paths. Combining the movement equation with observation position, the position of mobile target node can be tracked precisely with Kalman filter algorithm. Using the estimated NLOS range errors, we correct the localization result and modify Kalman filter to mitigate the NLOS propagations. The simulations demonstrate the validity with RN and SSR detection methods and analyze the impacts of NLOS range errors and number of NLOS anchor nodes. The estimated NLOS range errors are proved to be close to the true especially when the NLOS range errors are much bigger than LOS range errors. The simulation results show that the position of target node can be tracked precisely and effectively, when the NLOS mitigation is used to track the target node position with modified Kalman filter.

Third-Order Channel Propagation Model-Based Indoor Adaptive Localization Algorithm for Wireless Sensor Networks

In a changing indoor environment, it is very difficult for wireless sensor networks (WSNs) to locate adaptively the target nodes because of the complexity of the propagation path loss. In this letter, the fading characteristics of channel propagation for wireless sensor nodes are tested, the test data are fitted, and the third-order polynomial log-distance path-loss model is proposed. Using the proposed model, an indoor adaptive localization algorithm for WSNs is designed. The algorithm builds a model parameter table according to the received signal strength indication (RSSI) of fixed nodes, and the table is updated with environmental variation. In locating phase, the most suitable parameters are used to estimate the position of target node. Compared to another algorithm, the proposed algorithm improves the average localization accuracy by about 75%.

RSS-Based Indoor Localization Algorithm for Wireless Sensor Network Using Generalized Regression Neural Network

Traditional received signal strength (RSS)-based localizations are often erroneous for the low-cost WSN devices. The reason is that the wireless channel is vulnerable to so many factors that deriving the appropriate propagation loss model for the WSN device is difficult. We propose a flexible location estimation algorithm using generalized regression neural network (GRNN) and weighted centroid localization. In the first phase of the proposed scheme, two GRNNs are trained separately for x and y coordinates, using RSS data gathered at the access points from the reference nodes. The networks are then used to estimate the approximate location of the target node and its close neighbors. In the second phase, the target node position is determined by calculating the weighted centroid of the Nc-closer neighbors. Performance of the proposed algorithm is compared with some existing RSS based techniques. Simulation and experimental results indicate that the location accuracy is satisfactory. The system performance is remarkably good in comparison with its simplicity and requiring no additional hardware.