Wireless Sensor Node Localization Research Articles

Power‐aware range‐free wireless sensor network localization using neighbor distance distribution

ABSTRACTIn large‐scale wireless sensor networks, cost‐effective and energy‐efficient localization of sensor nodes is an important research topic. In spite of their coarse accuracy, range‐free (connectivity‐based) localization methods are considered as cost‐effective alternatives to the range‐based localization schemes with specialized hardware requirements.In this paper, we derive closed‐form expressions for the average minimum transmit powers required for the localization of sensor nodes, under deterministic path loss, log‐normal shadowing, and Rayleigh fading channel models. The impacts of propagation environment and spatial density of anchor nodes on the minimum transmit power for node localization are evaluated analytically as well as through simulations. Knowledge of the minimum transmit power requirements for localizability of a sensor node enables improving energy efficiency and prolonging lifetime of the network. We also propose a novel distance metric for range‐free localization in large‐scale sensor networks. The target and anchor nodes are assumed to be positioned according to two statistically independent two‐dimensional homogeneous Poisson point processes. Analytical expression for the average distance from a target node to its kth nearest neighbor anchor node is derived and is used for estimating the target‐to‐anchor node distances for localization. The Cramér–Rao lower bound on the localization accuracy for the new distance estimator is derived. Simulation results show the accuracy of the proposed distance estimate compared with some existing ones for range‐free localization. The results of our investigation are significant for low‐cost, energy‐efficient localization of wireless sensor nodes. Copyright © 2011 John Wiley & Sons, Ltd.

Analysis of Different Ranges for Wireless Sensor Node Localization using PSO and BBO and its variants

In a Wireless Sensor Network (WSN) accurate location of target node is highly desirable as it has strong impact on overall performance of the WSN. This paper proposes the application of different migration variants of Biogeography-Based Optimization (BBO) algorithm and Particle Swarm Optimization (PSO) for distributed optimal localization of randomly deployed sensors for different ranges. Biogeography is collective learning of geographical allotment of biological organisms. BBO has a new inclusive vigor based on the science of biogeography and employs migration operator to share information between different habitats, i.e., problem solution. PSO models have only fast convergence but less mature. An investigation on distributed iterative localization is presented in this paper that shows how time consumption and error varies for different ranges. Here the nodes that get localized in iteration act as anchor node. A comparison of the performance of PSO and different migration variants of BBO in terms of number of nodes localized, localization accuracy and computation time is presented.

A Hybrid TOA-Fingerprinting Based Localization of Mobile Nodes Using UWB Signaling for Non Line-Of-Sight Conditions

Recently, Impulse Radio Ultra Wideband (IR-UWB) signaling has become popular for providing precise location accuracy for mobile and wireless sensor node localization in the indoor environment due to its large bandwidth and high time resolution while providing ultra-high transmission capacity. However, the Non-line-of-sight (NLOS) error mitigation has considerable importance in localization of wireless nodes. In order to mitigate NLOS errors in indoor localization this paper proposes and investigates a novel approach which creates a hybrid combination of channel impulse response (CIR)-based fingerprinting (FP) positioning and an iterative Time of Arrival (TOA) real time positioning method using Ultra Wideband (UWB) signaling. Besides, to reduce the calculation complexities in FP method, this paper also introduces a unique idea for the arrangement of reference nodes (or tags) to create a fingerprinting database. The simulation results confirm that the proposed hybrid method yields better positioning accuracies and is much more robust in NLOS error mitigation than TOA only and FP only and a conventional iterative positioning method.

Asymmetric Event-Driven Node Localization in Wireless Sensor Networks

Localization of wireless sensor nodes has long been regarded as a problem that is difficult to solve, especially when considering characteristics of real-world environments. This paper formally describes, designs, implements, and evaluates a novel localization system called Spotlight. The system uses spatiotemporal properties of well-controlled events in the network, light in this case, to obtain locations of sensor nodes. Performance of the system is evaluated through deployments of Mica2 and XSM motes in an outdoor environment, where 20 cm localization error is achieved. A sensor network consisting of any number of nodes deployed in a 2,500 m2 area can be localized in under 10 minutes. Submeter localization error in an outdoor environment is made possible without equipping the wireless sensor nodes with specialized ranging hardware.

Wireless sensor node localization by multisequence processing

Wireless Sensor Networks have been proposed for use in many location-dependent applications. Most of these need to identify the locations of sensor nodes, a challenging task because of severe constraints on cost, energy and effective range of sensor devices. To overcome limitations in existing solutions, we present a Multi-Sequence Positioning (MSP) method for large-scale stationary sensor node localization in outdoor environments. The novel idea behind MSP is to reconstruct and estimate two-dimensional location information for each sensor node by processing multiple one-dimensional node sequences, easily obtained through loosely guided event distribution. Starting from a basic MSP design, we propose four optimizations that work together to increase localization accuracy. We address several interesting issues such as incomplete (partial) node sequences and sequence flip, found in the Mirage test-bed we built. We have evaluated the MSP system through theoretical analysis, extensive simulation as well as two physical systems (an indoor version with 46 MICAz motes and an outdoor version with 20 MICAz motes). Evaluation demonstrates that MSP can achieve an accuracy within one foot, requiring neither additional costly hardware on sensor nodes nor precise event distribution. In fact, it provides a nice tradeoff between physical cost (anchors) and soft cost (events) while maintaining localization accuracy.

Wireless sensor node localization

For most wireless sensor network (WSN) applications, the positions of the sensor nodes need to be known. Global positioning systems have not fitted into WSNs very well owing to their price, power consumption, accuracy and limitations in their operating environment. Hence, the last decade has brought about a large number of proposed methods for WSN node localization. They show tremendous variation in the physical phenomena they use, the signal properties they measure, the resources they consume, as well as in their accuracy, range, advantages and limitations. This paper provides a high-level, comprehensive overview of this very active research area.

A Research on Circular Localization Algorithm of Wireless Sensor Network

This paper based on several common wireless sensor node localization algorithms. According to the concentric localization algorithm principle, we proposed an annular localization algorithm and its improved algorithm .The algorithm uses the anchor node to do node ring through certain rules, narrows unknown nodes estimate area continually, and until finally gets the minimum area contains unknown nodes. Then taking the minimum area centroid position as unknown node’s estimate coordinates. Through the simulation of concentric localization algorithm and its improved algorithm, circular localization algorithm and its improved algorithm, can conclude that: When the proportion of anchor node increases from 5% to 10%, the positioning accuracy is obviously improved in the situation of low energy consumption.

Real Time Relative and Absolute Dynamic Localization of Air–Ground Wireless Sensor Networks

A novel approach for relative and absolute localization of wireless sensor nodes using a potential field method is presented. The main idea of our work is to develop relative and absolute localization algorithms for the position estimate of stationary unattended ground sensor (UGS) nodes using a potential field method. A dynamical model is derived for each sensor node to estimate the relative and absolute position estimates under the influence of a certain fictitious virtual force. In the algorithm the sensor nodes do not move physically, but a virtual motion is carried out to generate optimal position estimates. The convergence of the estimator system to a least squares solution is guaranteed using Lyapunov theory. Separate control algorithms for relative and absolute localization are developed which guarantee the convergence of the position estimates. The relative localization algorithm assumes that distance (i.e. range) measurements between UGS nodes are available and for absolute localization algorithm, uninhabited aerial vehicles (UAV) are available with on board GPS such that they have absolute position information together with range measurement information. In the relative localization algorithm the UGS nodes are localized with respect to an internal co-ordinate frame. In absolute localization the UGS nodes are localized with respect to the known absolute position of UAV in the air---ground network. The effectiveness of the control algorithm is highlighted by the real time implementation results.

An analysis of error inducing parameters in multihop sensor node localization

Ad hoc localization of wireless sensor nodes is a fundamental problem in wireless sensor networks. Despite the recent proposals for the development of ad hoc localization algorithms, the fundamental behavior in systems using measurements has not been characterized. In this paper, we take a first step toward such a characterization by examining the behavior of error inducing parameters in multihop localization systems in an algorithm independent manner. We first derive the Crame Rao Bound for Gaussian measurement error for multihop localization systems using distance and angular measurements. Later on, we use these bounds on a carefully controlled set of scenarios to study the trends in the error induced by the measurement technology accuracy, network density, beacon node concentration, and beacon uncertainty. By exposing these trends, the goal of this paper is to develop a fundamental understanding of the error behavior that can provide a set of guidelines to be considered during the design and deployment of multihop localization systems.