Node Position Estimation Research Articles

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

A novel distance vector hop localization method for wireless sensor networks

Abstract Wireless sensor networks (WSNs) require accurate localization of sensor nodes for various applications. In this article, we propose the distance vector hop localization method (DVHLM) to address the node dislocation issue in real-time networks. The proposed method combines trilateration and Particle Swarm Optimization techniques to estimate the location of unknown or dislocated nodes. Our methodology includes four steps: coordinate calculation, distance calculation, unknown node position estimation, and estimation correction. To evaluate the proposed method, we conducted simulation experiments and compared its performance with state-of-the-art methods in terms of localization accuracy with known nodes, dislocated nodes, and shadowing effects. Our results demonstrate that DVHLM outperforms the existing methods and achieves better localization accuracy with reduced error. This article provides a valuable contribution to the field of WSNs by proposing a new method with a detailed methodology and superior performance.

Node position estimation based on optimal clustering and detection of coverage hole in wireless sensor networks using hybrid deep reinforcement learning

Node position estimation based on optimal clustering and detection of coverage hole in wireless sensor networks using hybrid deep reinforcement learning

A Distance Vector Hop-Based Secure and Robust Localization Algorithm for Wireless Sensor Networks

Location information of sensor nodes in a wireless sensor network is important. The sensor nodes are usually required to ascertain their positions so that the data collected by these nodes can be labeled with this information. On the other hand, certain attacks on wireless sensor networks lead to the incorrect estimation of sensor node positions. In such situations, when the location information is not correct, the data may be labeled with wrong location information that may subvert the desired operation of the wireless sensor network. In this work, we formulate and propose a distance vector hop-based algorithm to provide secure and robust localization in the presence of malicious sensor nodes that result in incorrect position estimation and jeopardize the wireless sensor network operation. The algorithm uses cryptography to ensure secure and robust operation in the presence of adversaries in the sensor network. As a result of the countermeasures, the attacks are neutralized and the sensor nodes are able to estimate their positions as desired. Our secure localization algorithm provides a defense against various types of security attacks, such as selective forwarding, wormhole, Sybil, tampering, and traffic replay, compared with other algorithms which provide security against only one or two types. Simulation experiments are performed to evaluate the performance of the proposed method, and the results indicate that our secure localization algorithm achieves the design objectives successfully. Performance of the proposed method is also compared with the performance of basic distance vector hop algorithm and two secure algorithms based on distance vector hop localization. The results reveal that our proposed secure localization algorithm outperforms the compared algorithms in the presence of multiple attacks by malicious nodes.

Self-Localization of IoT Devices Using Noisy Anchor Positions and RSSI Measurements

Location-enabled Internet of things (IoT) has attracted much attention from the scientific and industrial communities given its high relevance in application domains such as agriculture, wildlife management, and infectious disease control. The frequency and accuracy of location information plays an important role in the success of these applications. However, frequent and accurate self-localization of IoT devices is challenging due to their resource-constrained nature. In this paper, we propose a new algorithm for self-localization of IoT devices using noisy received signal strength indicator (RSSI) measurements and perturbed anchor node position estimates. In the proposed algorithm, we minimize a weighted sum-square-distance-error cost function in an iterative fashion utilizing the gradient-descent method. We calculate the weights using the statistical properties of the perturbations in the measurements. We assume log-normal distribution for the RSSI-induced distance estimates due to considering the log-distance path-loss model with normally-distributed perturbations for the RSSI measurements in the logarithmic scale. We also assume normally-distributed perturbation in the anchor position estimates. We compare the performance of the proposed algorithm with that of an existing algorithm that takes a similar approach but only accounts for the perturbations in the RSSI measurements. Our simulation results show that by taking into account the error in the anchor positions, a significant improvement in the localization accuracy can be achieved. The proposed algorithm uses only a single measurement of RSSI and one estimate of each anchor position. This makes the proposed algorithm suitable for frequent and accurate localization of IoT devices.

Localization in Wireless Sensor Networks with Mobile Anchor Node Path Planning Mechanism

Determining the accurate locations of randomly deployed sensors in a given geographical region is a critical problem in the field of Wireless Sensor Networks. Various approaches have been proposed in the past to tackle this problem. Most of the existing techniques rely on the use of a beacon node which traverses throughout the region with limited power sources. Sensors use the beacon node positions for self localization. The trajectory of the beacon node has an influence on the accuracy, time and efficiency of the localization algorithm. In this paper, we propose Cosine Rule based Localization (CRL) algorithm for static trajectories of the mobile beacon node. Existing schemes use trilateration for position estimation of sensor nodes. The proposed method uses the Cosine rule on received beacon positions and distances obtained from RSSI in such a way that lines representing distances intersect at one point for the application of the Cosine rule. Simulation results reveal that CRL enables the unknown sensors to locate themselves with better accuracy in comparison with the existing trilateration technique for all the trajectories.

Raccoon optimization algorithm-based accurate positioning scheme for reliable emergency data dissemination under NLOS situations in VANETs

In emergency situations, cooperative positioning of vehicular nodes is essential for facilitating precise and stable information for achieving reliable data dissemination in Vehicular Ad hoc NETworks (VANETs). However, the existence of Non-Line-Of-Sight (NLOS) nodes degrades the accuracy in estimating ranging measurements introduced by the blockages from tall vehicles and buildings. In this paper, Raccoon Optimization Algorithm-based Accurate Positioning Scheme (ROA-APS) is proposed for improving the accuracy in the estimation of ranging measurements in order to determine the exact position of NLOS nodes. It is proposed for ensuring maximized reliability and reduced latency in the event of warning message exchange. It inherits the food rummaging style of real raccoons for speeding and strengthening the local and global search process involved in the estimation of NLOS node positions. It utilizes maximum probability of acquiring higher adaptability through active learning to attain better localization of NLOS nodes. It inherits the distance information for calculating the position accuracy associated with vehicle trajectory, distance information error and the number of vehicles. It also uses the method of weighted average to enforce more confidence to the distance information provided by neighboring nodes. The simulation experiments of the proposed ROA-APS using EstiNet simulators are conducted to determine its significance with respect to positioning accuracy, emergency message dissemination rate, positioning error, neighbor vehicles awareness rate and positioning time. The results confirm an increased mean emergency message dissemination rate, positioning accuracy and neighbor vehicles awareness rate by 16.21%, 14.38% and 15.16% when compared to the benchmarked schemes.

Wireless Node Localization under Hostile Radio Environment using Smart Antenna

This paper presents a resilient localization scheme for wireless sensor networks (WSNs). It suits well in estimation of node position under a corrupted radio environment. Position computation is based on information of angle-of-arrivals (AoA) and references obtained from a few mobile anchors. In the network, anchors are equipped with smart antennas and global positioning system receivers. They broadcast signals in a synchronous and periodic fashion. The neighboring nodes having the signals with received signal strength values above a prescribed threshold level, respond with their respective IDs. Anchors evaluate AoA information from these signals using estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm. Next, they forward beacon messages, containing their references and estimated angles, to the corresponding nodes and move along random trajectories. After receiving three sets of such data, at least, nodes can initiate selective segregation of the inconsistent position estimations. Simulation results attaining higher degree of localization accuracy validate its competency over the existing schemes.

Design and Implement Beacon based scheme for Node Localization in Underwater Acoustic Network

A network that can sense the surroundings and collected all the information from the sensor nodes and passed it to the base station is known as a wireless sensor network. The underwater acoustic networks are the type of network deployed under the oceans and passed information to the base station. Due to the dynamic nature of the network, nodes change their location at any time. To maximum aggregate information from the sensor nodes, to estimate exact node location is very important. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node, localization has a high delay, which reduces its efficiency. The technique needs to be designed, which localizes more nodes in less amount of time. This research is based on the advancement of the range based scheme for node localization. In the proposed scheme, mobile beacons are responsible for node localization. The beacon nodes send beacon messages in the network, and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node, the sensor nodes which are already localized will not respond back to the beacon messages, which reduce delay in the network for node localization.

A novel statistical manifold algorithm for position estimation

In this paper, a novel statistical manifold algorithm is proposed for position estimation of sensor nodes in a wireless network, making full use of distance information available among unknown nodes and simultaneous localization of multiple unknown nodes. To begin, a ranging model including the distance information among unknown nodes is established. With the reparameterization of the natural parameter and natural statistic, the solution problem of the ranging model is transformed into a parameter estimation problem of the curved exponential family. Then, a natural gradient method is adopted to deal with the parameter estimation problem of the curved exponential family. To ensure the convergence of the proposed algorithm, a particle swarm optimization method is utilized to obtain initial values of the unknown nodes. Experimental results indicate that the proposed algorithm can improve the positioning accuracy, compared with the traditional algorithm.

Considerations about the Signal Level Measurement in Wireless Sensor Networks for Node Position Estimation.

Wireless Sensor Networks (WSN) are widely used in different monitoring systems. Given the distributed nature of WSN, a constantly increasing number of research studies are concentrated on some important aspects: maximizing network autonomy, node localization, and data access security. The node localization and distance estimation algorithms have, as their starting points, different information provided by the nodes. The level of signal strength is often such a starting point. A system for Received Signal Strength Indicator (RSSI) acquisition has been designed, implemented, and tested. In this paper, experiments in different operating environments have been conducted to show the variation of Received Signal Strength Indicator (RSSI) metric related to distance and geometrical orientation of the nodes and environment, both indoor and outdoor. Energy aware data transmission algorithms adjust the power consumed by the nodes according to the relative distance between the nodes. Experiments have been conducted to measure the current consumed by the node depending on the adjusted transmission power. In order to use the RSSI values as input for distance or location detection algorithms, the RSSI values can’t be used without intermediate processing steps to mitigate with the non-linearity of the measured values. The results of the measurements confirmed that the RSSI level varies with distance, geometrical orientation of the sensors, and environment characteristics.

Advancement in range based scheme for node localization of underwater acoustic networks

The underwater acoustic network is the type of network which is deployed under the deep sea or ocean to gather underwater information. The sensor node position estimation is a major issue of the underwater acoustic network. The process of estimating node position is called node localization. In the existing RSSI based approach for the node localization has a high delay which reduces its efficiency. The technique needs to be designed which localize a number of nodes in less amount of time. This research is based on the advancement of the range-based scheme for node localization. In the proposed scheme mobile beacons are responsible for the node localization. The beacon nodes send beacon message in the network and sensor nodes respond back with a reply message. When two beacons receive the reply of a sensor node that is considered as a localized node. The sensor nodes which are already localized will not respond back to the beacon messages which reduce delay in the network for node localization. The simulation of proposed modal is performed in MATLAB and it shows that proposed scheme performs well in terms of a number of nodes localized.

Relative Self-Calibration of Wireless Acoustic Sensor Networks Using Dual Positioning Mobile Beacon

Sensor nodes deployed for event localization are required to be location aware. When event of interest is acoustic, node calibration (involving localization and orientation) during deployment phase become significant for accurate event localization during normal operation. This paper proposes a novel approach for node calibration in an acoustic surveillance network. Each node is equipped with three microphones and an RF transceiver. Nodes are calibrated for their relative positions and orientations by transmitting acoustic and RF signals, simultaneously, from beacon nodes at unknown locations. The acoustic signal, received by microphone array, is used for angle of arrival measurement. While an RF signal is transmitted for time difference of arrival measurement, which is used to quantify distance between beacon and sensor nodes. Proposed approach is attractive as it does not require expensive on board GPS module and compass. The effectiveness of proposed scheme is demonstrated using experimental setup comprising five sensor nodes and one beacon node. Maximum likelihood estimation is employed to determine network configuration under noisy measurements. Simulations are performed to observe the effect of network parameters, such as node density on node position estimation accuracy.

LS-SVM Based WSN Location Algorithm in NLOS Environments

Location methods based on learning theory perform well in wireless cellular networks. These methods may be further improved since range measurements among all nodes are not taken into consideration, while these range measurements in the WSN location system are generally available. In this paper, we propose an improved LS-SVM based location algorithm to solve mobile location problem in a NLOS environment. We extend LS-SVM method from wireless cellular networks to WSN location system. Compared with LSSVM in wireless cellular networks only using the range measurements between anchor nodes and blind nodes, the proposed method can improve the positioning accuracy by using all the range measurements among the nodes. Moreover, steepest descent method is used in the proposed method to iterative search the optimal position estimation of blind nodes. The simulations results in different cases illustrate that the proposed algorithm outperforms the kernel method and LS-SVM method on location accuracy.

Vertex Projection and Maximum Likelihood Position Location in Reconfigurable Networks

A low-complexity vertex-projection method based on a pyramidal trilateration technique suitable for node position estimation in ad-hoc/sensor network scenarios is presented. Also a maximum likelihood position location is introduced to compare position location performance. The proposed methodology relies solely on range estimate between neighboring nodes on the connecting path linking the node of interest to the reference location access points. Feasibility is studied in the presence of measuring and routing noise and path dispersion that depend on the node density and reachability of radius. Results show that adequate proximity information can be achieved for a wide variety of multi-hop scenarios. Number of reference nodes is increased and shown that three references are sufficient for position location. Noisy environments where iid and non-iid random variables are generated are also shown, where the proposed algorithm achieves good performance in terms of the rms error position.

An Improved Three-dimensional DV-Hop Localization Algorithm Optimized by Adaptive Cuckoo Search Algorithm

Aiming at the low accuracy of DV-Hop localization algorithm in three-dimensional localization of wireless sensor network, a DV-Hop localization algorithm optimized by adaptive cuckoo search algorithm was proposed in this paper. Firstly, an improved DV-Hop algorithm was proposed, which can reduce the localization error of DV-Hop algorithm by controlling the network topology and improving the method for calculating average hop distance. Meanwhile, aiming at the slow convergence in traditional cuckoo search algorithm, the adaptive strategy was improved for the step search strategy and the bird's nest recycling strategy. And the adaptive cuckoo search algorithm was introduced to the process of node localization to optimize the unknown node position estimation. The experiment results show that compared with the improved DV-Hop algorithm and the traditional DV-Hop algorithm, the DV-Hop algorithm optimized by adaptive cuckoo search algorithm improved the localization accuracy and reduced the localization errors.

Robust node position estimation algorithms for wireless sensor networks based on improved adaptive Kalman filters

The Kalman filter (KF) is an optimal state estimator for position observation systems with noise; therefore, it is typically used to estimate the location of a node in a wireless sensor network (WSN) in a noisy environment. The precision of noise statistics largely determines the localization accuracy of the KF, and the statistics of noise are often unknown or time-varying in a real WSN. Therefore, the adaptive Kalman filter (AKF) is utilized for awareness of the statistical parameters of noise. However, the node position estimation (NPE) algorithm based on the state-of-the-art AKF typically lacks robustness and becomes inaccurate in the case of simultaneously perceiving the statistics of process noise and measurement noise. This study proposes a robust NPE algorithm based on an improved adaptive extended Kalman filter (RNPE-IAEKF) and another robust NPE algorithm based on an improved adaptive unscented Kalman filter (RNPE-IAUKF). The RNPE-IAEKF algorithm has low computing complexity, while the RNPE-IAUKF algorithm has high positioning accuracy. Our proposed algorithms solve the problems of poor robustness and low accuracy of the NPE algorithm based on the adaptive extended Kalman filter (NPE-AEKF) and the NPE algorithm based on the adaptive unscented Kalman filter (NPE-AUKF). In addition, the RNPE-IAEKF and the RNPE-IAUKF do not lose robustness upon simultaneous perception of the statistics of process noise and measurement noise, which is strictly proven in theory. The results of practical experiments and numerical simulations demonstrate that regardless of the placement of a static target node, the mobility of a mobile target node, and the number of anchor nodes, the RNPE-IAEKF improves upon the positioning accuracy and convergence speed of the NPE-AEKF by at least 28% and 29%, respectively and that the RNPE-IAUKF increases the localization accuracy and convergence rate of the NPE-AUKF by at least 32% and 37%.

Noise-aware localization algorithms for wireless sensor networks based on multidimensional scaling and adaptive Kalman filtering

The range-based MDS-MAP (multidimensional scaling-MAP) localization algorithm has been widely applied to the estimation of node position in wireless sensor networks (WSNs). However, the range for the MDS-MAP is often influenced by measurement noise so that there is an error, which will greatly reduce the positioning precision of MDS-MAP. Although the current improved MDS-MAP algorithms, such as MDS-MAP(P) and MDS-MAP(P,R), and the algorithms based on the theory of the rigid graph can obtain higher accuracy compared to the MDS-MAP, they are not suitable for the occasion where there are large errors in distance measurements between most nodes, namely the non-rigid graph. Therefore, Kalman filter (KF) is employed to refine the node coordinates from the MDS-MAP on this occasion, but it requires obtaining accurate noise statistics in advance and cannot adapt to the change of noise statistics. In the real WSN localization scenario with unknown or time-varying noise statistics, the inaccurate statistical parameter of noise will seriously weaken the refinement effect of KF on the MDS-MAP, especially under large noise-statistic bias. In this work, we propose two types of two-stage noise-aware localization algorithms for WSNs based on MDS-MAP and adaptive KF (AKF), i.e. an existing adaptive extended KF and an innovative adaptive unscented KF. The positioning accuracy and the time complexity of the AKF for the proposed algorithms are better than those of the spring relaxation for the rigid-graph based localization and the least square optimization for the improved MDS-MAP in the noisy environment. The results of extensive simulations show that compared with the present algorithms for refining the MDS-MAP, our proposed algorithms can always achieve higher positioning accuracy and lower time complexity regardless of the placement way of node, the shape of network topology, the communication radius of node, the node degree of network, and the deviation of noise statistics.

Applying location estimation for reliable routing in tactical unmanned ground vehicle networks

An unmanned ground vehicle (UGV) network operates as an ad hoc autonomous tactical network. In this paper, we study the deployment of a UGV network and its ability to use location estimation to provide reliable data forwarding. This analysis involves (1) location estimation to obtain realistic estimates of the node’s (UGV) spatial positions and (2) real-time path planning to obtain reliable routing among nodes using the estimates of the node positions. Conventional approaches commonly use the popular Random Waypoint (RWP) model (or its variants) as the mobility model. The RWP approach uses randomly selected node positions and has no way to cope with nodes moving in and out of transmission range. These deficiencies cause the route reliability to suffer. Our approach (1) uses an Extended Kalman Filter (EKF) for node mobility/location estimation and (2) uses a modified Ad Hoc On Demand Distance Vector (AODV) algorithm for route selection using a new routing parameter called contact time. We refer to this combined approach as the AODV-LocPred algorithm. We compare the AODV-LocPred algorithm with AODV using the RWP model. This is referred to as AODV-RWP. Our simulations show that the AODV-LocPred algorithm significantly outperforms the AODV-RWP algorithm in terms of packet delivery ratio (PDR), because it provides realistic node position estimates and it copes in a reasonable way with nodes moving in and out of transmission range. However, the AODV-LocPred incurs an increase in end-to-end delay. Nonetheless, we expect that in many real-world settings, this tradeoff is acceptable.

Analysis of Some Feedforward Artificial Neural Network Training Algorithms for Developing Localization Framework in Wireless Sensor Networks

Wireless sensor networks (WSNs) have gained global attention of both, the research community and various application users. Localisation in WSN plays a crucial role in implementing myriad of applications such as healthcare management, disaster management, environment management, and agriculture management. Localization algorithms have become an essential requirement to enhance the effectiveness of WSNs demonstrating relative estimation of sensor node position of anchor nodes with their absolute coordinates. We have done a comprehensive performance evaluation of some feedforward artificial neural networks (FFANNs) training algorithms for developing efficient localization framework in WSNs. The proposed work utilizes the received signal strength observed by anchor nodes by means of some multi-path propagation effects. This paper aims for best training algorithm output while comparing results of different training algorithms. The FFANNs is designed with 3-dimensional inputs and one hidden layer with variable neurons and two outputs. For hidden layer tansigmoid transfer function while for output layer linear transfer function is used. The best training algorithm of FFANNs based model can provide better position accuracy and precision for the future applications. We have analysed and proposed the usage of training algorithms that improves the accuracy and precision of localization algorithms. The simulation results demonstrate the effectiveness and huge potential in optimizing hardware for localization module in sensor nodes.