Distributed online localization in sensor networks using a moving target

SUMMARYLocation estimation of sensor nodes is an essential part of most applications for wireless sensor and actor networks. The ambiguous location information often makes the collected data useless in these applications. Environmental monitoring relies on an accurate position estimation to process or evaluate the collected data. In this paper, we present a novel and scalable approach for positioning of mobile sensor nodes with the goal of monitoring the Amazon river. The actors in the scenario are stationary and positioned at reachable spots on the land alongside the river whereas sensor nodes are thrown into the river to collect data such as water temperature, depth, and geographical features. The actors are not equipped with positioning adaptors, and they are only aware of their distances from the other actors. The sensor nodes collect data and forward it to the actors. While floating in the river, sensor nodes are often multiple hops away from the actors, which makes it challenging to apply traditional positioning techniques. Through extensive simulations, we show that the nodes can be efficiently positioned using a multi‐hop approach with local information exchange only. The introduced approach is also applied to a scenario, where monkey swarm monitoring is simulated, to test the generalizability of the algorithm. Copyright © 2011 John Wiley & Sons, Ltd.

Multi-sensor data fusion methods for indoor localization under collinear ambiguity

Internet of Things (IoT) is constructed from billions of sensor devices connected over Internet. Wireless sensor networks (WSNs) are very important communication technologies of IoT for providing large scale data from the environment. Since the data collected grows exponentially, the design of cloud based big data analysis techniques is crucial. Localization in sensor networks is finding the location of a node based on the reference nodes’s coordinates. In many applications such as target tracking and military surveillance, providing localization is necessary. Manually entering the locations of nodes during the deployment phase is not applicable especially for large scale sensor networks. Besides, integrating a GPS receiver to each sensor node is a very costly solution and may not be affordable for large scale networks. Since sensor nodes are mostly battery-powered, design of an energy-efficient localization method is highly desirable to prolong the lifetime of the network. Existing localization techniques may require many message transmission which causes high energy consumption. To tackle with this problem, we propose an energy-efficient localization framework in this paper. A mobile robot is traversed along the sensing area and communicate with sensor nodes to localize these nodes. Different than the previous approaches, our proposed approach requires only 3 messages per node. Besides, most of the execution needed for the localization is not carried by ordinary nodes. We simulate our proposed approach in ns2 simulator. We measure the localization quality and energy consumptions of our proposed approach with its counterparts. Also, we measure the localization quality and energy consumption against varying node counts and degrees. From extensive simulation results, we obtain that the localization qualities of our proposed approach is significantly better than its competitors. Besides, the energy consumption of our proposed algorithm is just 0.06 J per node and far more better than the distributed algorithm. Conclusively, our proposed framework is a significant candidate for IoT and big data applications requiring energy-efficient localization.

We propose evolutionary computation to estimate positions of nodes within a sensor network. The approach uses signal strength measurements between nodes and given positions for a subset of these nodes (anchor nodes). The signal strength measurements and routing requests take place simultaneously. A data collecting unit (sink node) receives distance estimates which are input to the evolutionary algorithm projecting node positions. This evolutionary approach can sort out data outliers and hence produce robust estimates of node positions. The present work contributes to decrease the cost and complexity of applying sensor networks. The approach also provides redundancy for the node positioning where alternative methods fail. The present simulations show examples of network generation and routing combined with estimation of node positions.

Recent works show that the Hilbert curve path for mobile-beacon-based localization in sensor networks has better accuracy than other approaches, such as randomized and circular paths. In this paper, the modified Hilbert curve path is introduced, and its theoretical lower bound error, compared with the basic Hilbert curve path, has been shown using Cramér–Rao lower bound analysis. It is shown that the proposed approach has a more uniform localization accuracy across all the nodes in the environment than the basic Hilbert approach. The simulation results show that this approach, combined with inter-node communication, provides better and close to uniform accuracy compared with previously proposed approaches.

In this paper, we introduce a rank-one Semi-Definite Programming (SDP) solution method for mobile source localization in sensor networks. The position and velocity of mobile source are jointly estimated using Time Delay (TD) measurements. To obtain the position and velocity of mobile source, a Relaxed Semi-Definite Programming (RSDP) algorithm is firstly designed by dropping the rank-one constraint. However, dropping the rank-one constraint leads to produce a suboptimal solution. To improve the performance, we further put forward the Penalty Function Semi-Definite Programming (PF-SDP) method to obtain the rank-one solution of the estimation problem by introducing the penalty terms. By adaptively choosing the penalty coefficient, an Adaptive Penalty Function Semi-Definite Programming (APF-SDP) algorithm is also proposed to avoid the excessive penalty. We also conduct experiments in both a simulated environment and a real system to demonstrate the effectiveness of the proposed methods. The results have demonstrated that the proposed APF-SDP outperforms the PF-SDP in terms of the position and velocity estimation whether the noise level is large or not.

Accurate, distributed localization algorithms are needed for a wide variety of wireless sensor network applications. This article introduces a scalable, distributed weighted-multidimensional scaling (dwMDS) algorithm that adaptively emphasizes the most accurate range measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors, updates its position estimate by minimizing a local cost function and then passes this update to neighboring sensors. Derived bounds on communication requirements provide insight on the energy efficiency of the proposed distributed method versus a centralized approach. For received signal-strength (RSS) based range measurements, we demonstrate via simulation that location estimates are nearly unbiased with variance close to the Cramér-Rao lower bound. Further, RSS and time-of-arrival (TOA) channel measurements are used to demonstrate performance as good as the centralized maximum-likelihood estimator (MLE) in a real-world sensor network.

One typical use of sensor networks is monitoring targets. The sensor networks classify, detect, locate, and track targets. The ML (Maximum likelihood) algorithm is one of the estimation algorithms of target location and has high accuracy to estimate target location. However, the calculation amount of the ML estimation algorithm is large. Energy-Ratios Source Localization Nonlinear Least Square (ER-NLS) is proposed to realize the ML algorithm. ER-NLS is the algorithm of estimating source location by using the ratio of sensors' receiving energies. However, ER-NLS has to search all the areas, so that the calculation amount of ER-NLS is large. In this paper we propose a method of reducing search area for localization. The proposed method uses the ratio of sensors' receiving energies. It can be used with the ML algorithm. We show that the proposed method with the ML algorithm can reduce the search areas to estimate the target location and thus reduce the complexity, while achieving the RMSE (root mean square error) close to that of the ML algorithm.

Localization in an ad hoc mobile sensor network is an important requirement as most of the applications that use sensor data require sensor location information to complete the processing. A typical sensor network has over hundred to thousand sensor nodes, and considering the size and cost of a sensor, using only GPS for localization is not very attractive. The mobility of sensor nodes could lead to network topologies wherein accurate computation of absolute position of all the sensor nodes may not be possible. In this paper, we propose a topology based localization approach that suggests a best possible approximate position for sensor nodes for which computation of exact absolute position is not possible. We have identified four basic topological configurations that help compute position with varied degree of accuracy. These atomic configurations have been identified keeping in mind the simplicity of the computational procedures associated with these configurations. In order to put less demand on a computational capability of a sensor node, we suggest that only a pre-defined number of sensor nodes are compute-enabled (c-nodes) in the sense that they have adequate computational power. Similarly, only a pre-defined number of sensor nodes are GPS-enabled. In such a sensor network, the distributed computation of localization is achieved by distributing the computational requirements of individual sensor nodes across the c-nodes. Each sensor node strives to improve its localization by constantly monitoring its neighborhood and requesting an associated c-node to recompute position whenever neighborhood topology changes. We provide some initial results that bring out the merits of the proposed approach.

Sensor placement of a wireless sensor network has a significant impact on the network performance, such as sensing coverage, communication costs and connectivity. Depending on the application domains, the deployment method of sensor networks may vary. In autonomous sensor networks, the deployment is typically realized via cooperative movement among the nodes themselves. In this paper, we propose a sensor deployment protocol, LODICO, to optimize the sensor placement with arbitrary initial positions. In essence, it is a distributed evolutionary algorithm executed cooperatively among all sensor nodes via local information exchange. The simulation results show that LODICO can provide a very high coverage rate in a short convergence time.

The problem of quickly detecting and locating a statistical change point using multiple sensors is considered. Specifically, a statistical change point occurs at a random time and at a random location in the coverage region (or the region of interest) of a multi-sensor network. Each sensor node in the network has a limited detection range hence the observations of each sensor would be affected by the change point only if the change point occurs in its vicinity. The region of interest is partitioned by the detection ranges of all the sensors in the network. One is required to detect the presence of the change and to locate the partition of the change based on the sequential observations from all sensor nodes. Three performance metrics, namely the average detection delay, the false alarm probability and the false location probability, are of interest. The goal is to find a stopping time τ, at which the decision maker stops taking more observations and claims the change has occurred, and a terminal decision rule δ, by which the decision maker locates the partition that the change occurs at, to minimize a weighted sum of these three performance metrics. We obtain the optimal solution by first converting the proposed problem to a Markovian stopping time problem and then solving the problem using the tools from the optimal stopping theory.

Target localization and tracking are two of the critical tasks of sensor networks in many applications. Conventional localization and tracking techniques developed for wireless systems that rely on direction‐of‐arrival (DOA) or time‐of‐arrival (TOA) information are not suitable for low‐power sensors with limited computation and communication capabilities. In this paper, we propose a low‐complexity and energy‐efficient method for target localization and tracking in noisy binary sensor networks, where the sensors can only perform binary detection, and the physical links are characterized by additive white Gaussian noise (AWGN) channels. The proposed method is based on known spatial topology. An efficient wake‐up strategy is used to activate a particular group of sensors for cooperative localization and tracking. We analyze the localization error probability and tracking miss probability in the presence of prediction errors. Simulation results validate the theoretic analysis and demonstrate the effectiveness of the proposed approach. Copyright © 2008 John Wiley & Sons, Ltd.

The Permanent Magnet-Assissted Synchronous Reluctance Motor (PMA-SynRM) drive has become one of the most interesting replacements for the high efficiency variable speed drive. Herein, sensorless predictive torque control of a PMA-SynRM with non-sinusoidal back electromotive force (Back-EMF) is introduced. In order to control PMA-SynRM, finite control set-model predictive control (FCS-MPC) is implemented by means of a two-level inverter. Furthermore, an improved form of FCS-MPC, i.e., direct mean torque control (DMTC), is utilized as a second method to control PMA-SynRM. For improving the sensorless the combination of Extended Kalman Filter (EKF), Adaptive Filter (AF) and quadrature Phase-Locked Loop (PLL) are used for better estimation of the non-sinusoidal back EMF, elimination of the high order harmonics, and the accurate estimation of position and speed rotor, respectively. The simulation result in effectively minimizing torque ripples compared to conventional FCS-MPC. The outcomes of the observer simulation are successfully guaranteed the accurate estimation of speed and rotor position.

Localization in sensor networks is the process of obtaining geographical location information for all deployed sensors. Localizing each sensor node is becoming increasingly important as more and more algorithms and protocols in the disciplines of routing, energy management, and security have been proposed that rely on (semi-) accurate location information. In this paper, we study a particular approach to sensor node localization that uses sequentially deployed beacons to localize sensors. Our model assumes that the localization information can be obtained by observing beacons 1-hop away from the sensors. We show that under this simplified model the beacon deployment problem, i.e., to find the minimum number of beacons, is (still) NP-complete. We then provide an approximation algorithm with a logarithmic approximation ratio based on a set cover greedy heuristic; we study both offline and online versions of this approximation. Finally, we incorporate the localization accuracy into the beacon deployment strategy by explicitly specifying the Cramer Rao bound (CRB) as the condition. We verify the performance of the proposed algorithms using simulations, and show that accurate localization can be effectively achieved using the proposed algorithms.

Accurate and sufficient node location information is crucial for Wireless Sensor Networks (WSNs) applications. However, the existing range-based localization methods often suffer from incomplete and detorted range measurements. To address this issue, some methods based on low-rank matrix recovery have been proposed, which usually assume noises follow single Gaussian distribution or/and single Laplacian distribution, and thus cannot handle the case with wider noise distributions beyond Gaussian and Laplacian ones. In this paper, a novel Anomaly-aware Node Localization (ANLoC) method is proposed to simultaneously impute missing range measurements and detect node anomaly in complex environments. Specifically, by utilizing inherent low-rank property of Euclidean Distance Matrix (EDM), we formulate range measurements imputation problem as a Robust -norm Regularized Matrix Decomposition (RRMD) model, where complex noise is fitted by Mixture of Gaussian (MoG) distribution, and node anomaly is sifted by -norm regularization. Meanwhile, an efficient optimization algorithm is designed to solve proposed RRMD model based on Expectation Maximization (EM) method. Furthermore, with the imputed EDM, all unknown nodes can be easily positioned by using Multi-Dimensional Scaling (MDS) method. Finally, some experiments are designed to evaluate performance of the proposed method, and experimental results demonstrate that our method outperforms three state-of-the-art node localization methods.