Location Of Mobile Node Research Articles

A Range-Based Monte Carlo Box Algorithm for Mobile Nodes Localization in WSNs

Fast and accurate localization of randomly deployed nodes is required by many applications in wireless sensor networks (WSNs). However, mobile nodes localization in WSNs is more difficult than static nodes localization since the nodes mobility brings more data. In this paper, we propose a Range-based Monte Carlo Box (RMCB) algorithm, which builds upon the Monte Carlo Localization Boxed (MCB) algorithm to improve the localization accuracy. This algorithm utilizes Received Signal Strength Indication (RSSI) ranging technique to build a sample box and adds a preset error coefficient in sampling and filtering phase to increase the success rate of sampling and accuracy of valid samples. Moreover, simplified Particle Swarm Optimization (sPSO) algorithm is introduced to generate new samples and avoid constantly repeated sampling and filtering process. Simulation results denote that our proposed RMCB algorithm can reduce the location error by 24%, 14% and 14% on average compared to MCB, Range-based Monte Carlo Localization (RMCL) and RSSI Motion Prediction MCB (RMMCB) algorithm respectively and are suitable for high precision required positioning scenes.

Adaptive Neuro-Fuzzy Location Indicator in Wireless Sensor Networks

Indoor localization is a basic process in Wireless Sensor Networks (WSN) monitoring. This paper presents a new approach for localization of mobile nodes in WSNs. The proposed approach is based on the design of an adaptive fuzzy localization system. First proposed contribution is to consider the rooms of the target environment as a fuzzy sets made by adjacent zones described by a Fuzzy Location Indicator (FLI). FLI provides a fuzzy linearization of the building map hence the creation of a fuzzy linguistic model of the system. Fingerprints of the Radio Signal Strength Indicators (RSSI) are collected from different anchors according to each FLI. A Sugeno type-0 fuzzy inference system is proposed and submitted to a supervised learning through the neuro-fuzzy ANFIS algorithm. Simulation results as well as experimentations in Cynapsys company premises have proved that a good learning process leads to high success rate. Finally, a comparative study with two fuzzy localization systems proved the lower localization error average of the proposed approach.

An Algorithm of Mobile Robot Node Location Based on Wireless Sensor Network

In the wireless sensor network, there is a consistent one-to-one match between the information collected by the node and the location of the node. Therefore, it attempts to determine the location of unknown nodes for wireless sensor networks. At present, there are many kinds of node localization methods. Because of the distance error, hardware level, application environment and application costs and other factors, the positioning accuracy of various node positioning methods is not in complete accord. The objective function is established and algorithm simulation experiments are carried out to make a mobile ronot node localization. The experimnettal results showed that the proposed algorithm can achieve higher localization precision in fewer nodes. In addition, the localization algorithm was compared with the classical localization algorithm. In conclusion, it is verified that the localization algorithm proposed in this paper has higher localization accuracy than the traditional classical localization algorithm when the number of nodes is larger than a certain number

An Efficient Localization Method for Mobile Nodes in Wireless Sensor Networks

With the development of electronic technology and communication protocols, wireless sensor network technology is developing rapidly. In a sense, the traditional static wireless sensor network has been unable to meet the needs of new applications. However, the introduction of mobile nodes extends the application of wireless sensor networks, despite the technical challenges. Because of its flexibility, the mobile wireless sensor network has attracted great attention, and even small, self-controlled mobile sensor devices have appeared. At present, mobile node localization has become one of the hotspots in wireless sensor networks. As the storage energy of wireless sensor network nodes is limited, and the communication radius is small, many scientists have focused their research direction on the location algorithm of mobile nodes. According to the continuity principle of mobile node movement, in this paper we propose an improved mobile node localization algorithm based on the Monte Carlo Location (MCL) algorithm, and the method can reduce the sampling interval effectively. First of all, this paper introduces the structure and classification of wireless sensor localization technology. Secondly, the principle of the Monte Carlo Location algorithm is described in detail. Thirdly, we propose an efficient method for mobile node localization based on the MCL algorithm. Finally, the effectiveness and accuracy of the new algorithm are verified by comparative analysis.

A Novel Approach of Efficient Localization Scheme for Wireless Sensor Network

Objectives: Exact Position detection of moving object is the utmost challenge of wireless sensor network along with minimum energy consumption of network. In past several years many localization algorithms have been recommended to give solution for WSN. Algorithms presented are not suitable for scenario where nodes are moving with constant speed and changing their locations continuously. The presented localization schemes are more appropriate for such networks. We are presenting an algorithm for the Mobile nodes in Wireless Sensor Network. Methods: In presented algorithm, for each iteration we have selected three nodes those are neighbors of each other to solve the problem by co-operative approach. By solving the trigonometric equations to find out the distances among the mobile nodes. Finding: We find out the effective Time period over what ALE of all nodes will be with minimum energy requirements. Optimize Localization point by taking minimum energy utilization with Time period where minimum ALE is found. Improvements: In future the Accuracy and performance are the issues with localization of Mobile nodes. Here with minimum energy utilization and minimum ALE can be achieved using 5 neighbor schemes for localization.

Collaborative High-Accuracy Localization in Mobile Multipath Environments

We study the problem of high-accuracy localization of mobile nodes in a multipath-rich environment where submeter accuracy values are required. We employ a peer-to-peer framework where nodes can get pairwise multipath-degraded ranging estimates in local neighborhoods, with the multipath noise correlated across time. The challenge is to enable high-accuracy positioning under severe multipath conditions when the fraction of received signals corrupted by multiple paths is significant. Our contributions are twofold. We provide a practical distributed localization algorithm by invoking an analytical graphical model framework based on particle filtering, and we validate its potential for high-accuracy localization through simulations. In a practical dedicated short-range communication (DSRC) mobile simulation setup, we show that the algorithm can achieve errors of <; 1 m 90% of the time, even when the fraction of line-of-sight (LOS) signals is less than 35%. We also address design questions such as “how many anchors and what fraction of LOS measurements are needed to achieve a specified target accuracy?” by showing that the Cramer-Rao lower bound (CRLB) for localization can be expressed as a product of two factors: a scalar function that depends only on the parameters of the noise distribution and a matrix that depends only on the geometry of node locations and the underlying connectivity graph. A simplified expression is obtained that provides an insightful understanding of the bound and that helps deduce the scaling behavior of the estimation error as a function of the number of agents and anchors in the network.

Collaborative Vehicle Location Management Service for Enhanced Hybrid Reactive and Proactive Multicast in VANETs

Delivery of information to highly dynamic nodes is a big challenge in vehicular ad hoc networks (VANETs). Existing location management protocols for VANET maintain locations of mobile nodes, but they exploit mobile vehicles as location servers, which increases location server changes and reduces stability. In this paper collaborative vehicle location management service (CVLMS) for an enhanced hybrid reactive and proactive multicast (EHRPM) facilitates knowing the location information of the moving destinations. EHRPM uses speeds, length, and densities of roads to choose the least delivery delay path. Existing information dissemination in VANETs does not discuss how location servers are used in localization. CVLMS exploits roadside units (RSUs) in cashing and dissemination of locations and dissemination of multicast messages. CVLMS guarantees stability using static RSUs that maintain locations to overcome multiple location server change problem. Location tables are stored in multiple RSUs which maintain load distribution. CVLMS provides an infrastructure platform which enables collaboration of RSUs to exchange locations. Scalability of the CVLMS is provided by increasing the number of vehicles and still gives high-performance results. Results demonstrate that CVLMS outperforms grid location service, intersection location service, Dynamic MANET On-demand with RSUs, and time-based vehicle-to-roadside unit communication in terms of packet delivery ratio, packet loss ratio, end-to-end delay, and overhead.

Localization and behaviour examination of mobile node of wireless sensor network

Localization and behaviour examination of mobile node of wireless sensor network

Transceiver Optimization for ToA-Based Localization of Mobile WSN

One of the main parameters in wireless sensor networks (WSNs) is the design of energy-efficient protocols. And accuracy is another central goal of localization. Since sensor nodes run on battery power, any WSN application and accurate localization needs to be energy-efficient. In this paper, the accuracy of localization is increased by accurate measurement of the distance between the mobile sensors. Limit error in multiple-input multiple-output (MIMO) has been calculated by CRB method. Virtual MIMO (VMIMO) technique can obtain better localization precision and the localization is energy-efficient. Optimum selection of the number of the transceiver nodes is obtained by the lowest possible energy consumption, the existent localization error, and speed of nodes. Mathematical relation between energy consumption and localization of mobile nodes is presented and then verified by simulation. VMIMO decreases power of transmitters and this in turn will result in decreasing destructive effects of electromagnetic sensitivity (EMS) on body. Furthermore, optimized localization parameters will increase the efficiency of the system and network lifetime.

Research on the Positioning of Moving Nodes in Wireless Sensor Network Based on Vector Machine

How to position node in the wireless sensor network has been a popular research topic. Based on the research to the mobile node positioning, this paper proposes position estimation method based on support vector machine (SVM) and game theory. It firstly uses support vector machine to preliminary estimate the position of the mobile node, secondly, it conducts smooth treatment to nude estimation position though game theory. At last, through fisherman algorithm, it conducts treatment to estimation position and noise. From the mobile node localization effect, ranging error, anchor node distribution density and non-line-of-sight coefficient, simulation results illustrate that the algorithm of this paper can effectively improve the effective of node positioning.

Measurement Error Observer-Based IMM Filtering for Mobile Node Localization Using WLAN RSSI Measurement

This paper proposes a mobile node (MN) localization technique using received signal strength indicator (RSSI) data of wireless local area network. Range-domain measurement converted from the raw RSSI data can be used for localizing the MN. In indoor environments, however, the measurements may contain large errors caused by wall penetrating line-of sight (LOS) signals and non-LOS signals. The measurement errors cause large localization error. In this paper, a measurement error observer (MEO) is newly developed to mitigate the localization error. In addition, the MEO is integrated with an interacting multiple model (IMM) filter to take the flexible dynamic model of the MN into consideration. The purpose of the developed MEO-based IMM filter is to decouple the measurement errors from the localization and to adapt the filter for the flexible dynamics of the MN. Through comparative simulation and experimental tests, the performance of the developed filter is evaluated.

Tracking Algorithm Based on Channel Propagating Characteristic in Wireless Sensor Network

In order to improve the mobile node tracking accuracy of indoor environment, a mobile node tracking algorithm based on channel propagating characteristic is proposed. Channel propagation model is established by actual measurement and fitting analysis in three different scenarios, which included closed corridor, open corridor and laboratory. The anchor node periodically measures the RSSI of the beacons from mobile node, to estimate the coordinates of the mobile node location, speed and direction by using the Maximum Likelihood method and channel model. The simulation results prove that the proposed scheme is effective and can meet the real-time requirements of indoor localization.

Doppler-aided localization of mobile nodes in an underwater distributed antenna system

In this paper, we consider a problem of localizing a moving object in the context of an underwater distributed antenna system. All the distributed nodes record the time-of-arrivals of a single message from the moving object and obtain the Doppler speed estimates used in the decoding process. Conventional methods have relied only on the time-of-arrival measurements to obtain position estimates, on top of which filtering methods can be further applied for tracking purposes. In this work, we explore the usefulness of Doppler speed information. The combination of time-of-arrival measurements and Doppler speed estimates associated with a single message enhances the accuracy of position estimation and provides an estimate of the velocity of the mobile object. A Kalman Filter (KF) and a Probabilistic Data Association Filter (PDAF) are further implemented processing the point estimates. Simulations show that the proposed Doppler-aided methods improve both the point estimation and tracking filter performance, which is verified using data from pool tests.

Vote selection mechanisms and probabilistic data association-based mobile node localization algorithm in mixed LOS/NLOS environments

As one of the key technologies of wireless sensor networks (WSNs), the localization of mobile nodes (MN) is one of the most significant research topics in WSNs. When a line-of-sight (LOS) channel is available, accuracy localization result can be obtained. Motivated by the fact that the non-line-of-sight (NLOS) propagation of signal is ubiquitous and decreases the accuracy of localization, we propose a MN localization algorithm in mixed LOS/NLOS environments. Considering the characteristics of NLOS error, we propose a localization algorithm based on vote selection mechanisms to filter the distance measurements and reserve the reliable measurements. Then a modified probabilistic data association algorithm is proposed to fuse the multiple measurements reserved from the vote selection. The position of the MN is finally determined by a linear least squares algorithm based on reference nodes selection. This algorithm effectively mitigates various kinds of NLOS errors and largely improves the localization accuracy of the MN in mixed LOS/NLOS environments. The simulation and experiments results show that the proposed algorithm has better robustness and higher localization accuracy than other methods.

Dynamic Deployment for Hybrid Sensor Networks Based on Potential Field-Directed Particle Swarm Optimization

For a hybrid sensor network, the effective coverage rate can be optimized by adjusting the location of the mobile nodes. For many deployments by APF (artificial potential field), due to the common problem of barrier effect, it is difficult for mobile nodes to diffuse by the weaker attraction when the nodes initially distribute densely in some places. The proposed deployment algorithm PFPSO (Potential Field-Directed Particle Swarm Optimization) can overcome this problem and guide the mobile nodes to the optimal positions. Normally the requirement is different for the effective coverage rate between the hotspot area and the ordinary area. On the basis of PFPSO, NPFPSO (Nonuniform PFPSO) algorithm was also proposed to implement nonuniform coverage according to the importance degree of the monitoring area. Simulation result illustrates that PFPSO algorithm can effectively improve the effective coverage rate of the network, and NPFPSO algorithm can obtain a balanced result of effective coverage rate for both hotspot area and ordinary area.

Analyzing The Performance of the Dynamic Position Location and Tracking (D-Pl&amp;T) of Mobile Nodes Using Omni Directional Antenna in Manet

Analyzing The Performance of the Dynamic Position Location and Tracking (D-Pl&amp;T) of Mobile Nodes Using Omni Directional Antenna in Manet

A Kalman Framework Based Mobile Node Localization in Rough Environment Using Wireless Sensor Network

Since the wireless sensor network (WSN) has the performance of sensing, processing, and communicating, it has been widely used in various environments. The node localization is a key technology for WSN. The accuracy localization results can be achieved in ideal environment. However, the measurement may be contaminated by NLOS errors in rough environment. The NLOS errors could result in big localization error. To overcome this problem, we present a mobile node localization algorithm using TDOA and RSS measurements. The proposed method is based on Kalman framework and utilizes the general likelihood ratio method to identify the propagation condition. Then the modified variational Bayesian approximation adaptive Kalman filtering is used to mitigate the NLOS error. It could estimate the mean and variance of measurement error. The simulation results demonstrate that the proposed method outperforms the other methods such as Kalman filter and [Formula: see text] filter.

Color filtering localization for three-dimensional underwater acoustic sensor networks.

Accurate localization of mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. The proportion factor of distance is also proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. The normalized nearness degrees are considered as the weighted standards to calculate the coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can localize the mobile node in a timely way. The average localization error of PCFL is decreased by about 30.4% compared to the AFLA method.

A Location Prediction Algorithm with Daily Routines in Location-Based Participatory Sensing Systems

Mobile node location predication is critical to efficient data acquisition and message forwarding in participatory sensing systems. This paper proposes a social-relationship-based mobile node location prediction algorithm using daily routines (SMLPR). The SMLPR algorithm models application scenarios based on geographic locations and extracts social relationships of mobile nodes from nodes’ mobility. After considering the dynamism of users’ behavior resulting from their daily routines, the SMLPR algorithm preliminarily predicts node’s mobility based on the hidden Markov model in different daily periods of time and then amends the prediction results using location information of other nodes which have strong relationship with the node. Finally, the UCSD WTD dataset are exploited for simulations. Simulation results show that SMLPR acquires higher prediction accuracy than proposals based on the Markov model.

Localization Algorithm for Mobile Nodes in Wireless Sensor Networks Based on Discrete-Time H∞ Filtering and Dynamic Node Model

Precise localization of mobile nodes in uncertain conditions is a fundamental and crucial topic in wireless sensor networks. In this paper a discrete-time H∞ filtering and dynamic node model based algorithm is proposed. Accurate and complicate network models are not required and no assumptions are needed for the external noise characteristics but only have to be seen as energy-limited. State and measurement equation of unknown node are built with basic kinematic property and sensor node measurement method, including the impact of environment random uncertainties and node connection failure. The position of the mobile node is estimated by the filter using an integration of position information from other assisting nodes. Complying to Linear Matrix Inequality (LMI) criterion, a theorem of H∞ filter designed for stochastic uncertain network is given. From the dynamics of the node, the solution existence of the proposed filter is obtained, and a low computational complexity method to get the optimum solution from the filter is provided in a simple motion model. The simulation results show that this method not only can achieve highly reliability but also the better localization accuracy under stochastic uncertainty wireless sensor network (WSN) conditions compared with the classic mobile MCL and MCB mobile schemes.