Sensor Localization Algorithm Research Articles

Failed Sensor Localization Using Amplitude-Only Near-Field Data

Phase measuring is cumbersome and expensive, especially when operation frequency of array is high. To avoid phase measuring, a novel failed sensor localization algorithm using amplitude-only near-field data is proposed. In perspective of Bayesian theory, the maximum a posteriori estimation of array excitation leads to a mixed-norm minimization problem subject to a quadratic constraint. Iterative procedure based on alternating directions method of multipliers is used to solve this problem. Computer simulations show that the proposed algorithm is able to give accurate estimation of array excitation and precisely locate the failed sensors of the array using amplitude-only near-field data.

A two-phase ranging algorithm for sensor localization in structural health monitoring

Information about the positions of the sensors in sensor networks is very important, and the deployment of more and more sensors is increasing the need for automatic sensor localization. This article therefore describes a novel two-phase ranging algorithm that first obtains rough estimate of the distance to a sensor’s position using time difference of arrival or time of arrival methods and then obtains a high-resolution estimate based on the rough one using a phase-based ranging scheme. This algorithm can easily resolve the otherwise intractable integer ambiguity that often appears in localization systems, and experimental results show that it can greatly decrease the ranging error in a decentralized distance-based localization system having transmitter beacons and receivers in the nodes. Related problems such as signal filtering and multipath effect are also discussed. This algorithm can make the deployment of large numbers of sensors very simple and the determination of their positions so accurate that it would be feasible to use dense networks of sensors to monitor the structural integrity of large structures.

A Reliable and Reconfigurable Signal Processing Framework for Estimation of Metabolic Equivalent of Task in Wearable Sensors

Wearable motion sensors are widely used to estimate metabolic equivalent of task (MET) values associated with physical activities. However, one major obstacle in widespread adoption of current wearables is that any changes in configuration of the network requires new data collection and re-training of the underlying signal processing algorithms. For any wearable-based MET estimation framework to be considered a viable platform, it needs to be reconfigurable, reliable, and power-efficient. In this paper, we aim to address the issues of sensor misplacement, power efficiency, and new sensor addition and propose a reliable and reconfigurable MET estimation framework. We introduce a power-aware sensor localization approach that allows users to wear the sensors on different body locations without need for adhering to a specific installation protocol. Furthermore, we propose a novel transductive transfer learning approach, which gives end-users the ability to add new sensors to the network without need for collecting new training data. This is accomplished by transferring the knowledge of already trained sensors to the untrained sensors in real-time. Our experiments demonstrate that our sensor localization algorithm achieves an accuracy of $90.8$ % in detecting location of the wearable sensors. The integrated model of sensor localization and MET calculation achieves an $R^2$ of $0.8$ in estimating MET values using a regression-based model. Furthermore, our transfer learning algorithm improves the $R^2$ value of MET estimation up to $60\%$ .

Improved MDS-based Sensor Localization Algorithm in Wireless Sensor Networks

Sensor localization technology is the principle problem for configuration and operation of wireless sensor network. Since existing multidimensional scaling localization algorithm has its limitation in localization accuracy, a novel method based on node distance correction (DCMDS) is put forward. The improved multidimensional scaling-based sensor localization algorithm partly divides the location areas of the whole network. It achieves the relative position by distance information among the nodes. Then the distance estimations are corrected according to the distance information of anchor nodes. When the corrected estimations distance matrix is completed, relative position is recalculated with classical MDS algorithm. The optimized particle swarm optimization is used to acquire the final localization results. The performances of DCMDS algorithm were validated in different scenario. The experiments revealed that improved method has high accuracy in localization and low complexity for calculation and communication. It also enhances the extensibility of localization algorithms

Detour Path Angular Information Based Range-Free Localization in Wireless Sensor Network

Range-free localization algorithm continues to be an important and challenging research topic in anisotropic Wireless Sensor Networks (WSNs). Designing range-free localization algorithms without considering obstacles or holes inside the network area does not reflect the real world conditions. In this paper, we have proposed Detour Path Angular Information (DPAI) based sensor localization algorithm to accurately estimate the distance between an anchor node and a sensor node. We utilized the Euclidean distance and transmission path distance among anchor nodes to calculate the angle of the transmission path between them one by one. Then the estimated hop distance is adjusted by the angle between the anchor pairs. Based on the angle of the detoured path (which is the key factor for accuracy), our algorithm determines whether the path is straight or detoured by anisotropic factors. Our proposed algorithm does not require any global knowledge of network topology to tolerate the network anisotropy nor require high sensor node density for satisfactory localization accuracy. Extensive simulations are performed and the results are observed to be in good agreement with the theoretical analysis. DPAI achieved average sensor localization accuracy better than 0.3r in isotropic network and 0.35r in anisotropic network when the sensor density is above 8.

Sensor models and localization algorithms for sensor networks based on received signal strength

Received signal strength (RSS) can be used in sensor networks as a ranging measurement for positioning and localization applications. This contribution studies the realistic situation where neither the emitted power nor the power law decay exponent be assumed to be known. The application in mind is a rapidly deployed network consisting of a number of sensor nodes with low-bandwidth communication, each node measuring RSS of signals traveled through air (microphones) and ground (geophones). The first contribution concerns validation of a model in logarithmic scale, that is, linear in the unknown nuisance parameters (emitted power and power loss constant). The parameter variation is studied over time and space. The second contribution is a localization algorithm based on this model, where the separable least squares principle is applied to the non-linear least squares (NLS) cost function, after which a cost function of only the unknown position is obtained. Results from field trials are presented to illustrate the method, together with fundamental performance bounds. The ambition is to pave the way for sensor configuration design and more thorough performance evaluations as well as filtering and target tracking aspects.

Timing-Based Mobile Sensor Localization in Wireless Sensor and Actor Networks

In this paper, localization problem in wireless sensor and actor networks (WSAN) is addressed. In WSAN, the performance of event detection and tracking highly depends on the exact location information of the events that must be reported along with the event features. Having precise location information of the sensor nodes, actors are able to execute actions more effectively in the region of detected events. In this context, the accurate localization of sensor nodes is essential with respect to the actors. Particularly, the problem becomes much more complicated when the sensor nodes as well as the anchor nodes (actors) are mobile. In order to localize the mobile sensor nodes relative to the actors, a novel Timing-based Mobile Sensor Localization (TMSL) algorithm is introduced. In TMSL, sensor nodes determine their distance from actors by using propagation time and speed of RF signal. In order to determine distance from the actors, actors actively broadcast reference beacons in a pattern of intervals adaptively defined according to the mobility of sensor nodes and the required level of localization accuracy. These reference beacons carry the interval numbers in which they were transmitted. The interval numbers are then used by the sensor nodes to calculate the start time of the beacons locally which is then used to determine the propagation time. TMSL does neither require nor assume any time synchronization among the sensor nodes or with the actors. Performance evaluations clearly show that TMSL is adaptive to velocity of mobile sensor and actor nodes and can be configured according to the required localization accuracy in order to avoid overhead raised due to high velocity.

Energy Efficient Hidden Markov ModelBased Target Tracking Mechanism in Wireless Sensor Networks

Problem statement: Target tracking is a challenging application in Wireless Sensor Networks (WSNs) because it is computation-intensive and requires real-time location processing. This study proposes a practical target tracking system based on the Hidden Markov Model in a distributed signal processing framework. In this framework, wireless sensor nodes perform target detection and tracking, whereas target localization requires the collaborative signal processing between wireless sensor nodes for improving the location accuracy and robustness. Approach: For carrying out target tracking under the constraints imposed by the limited transmission capabilities of the wireless sensor nodes, the HMM model and the particle filter approach are adopted in single wireless sensor node due to their outstanding performance and light computational calculations. Furthermore, a progressive multi sensor localization algorithm is proposed in distributed wireless sensor network considering the tradeoff between the localization accuracy of the target and the resource constraints of sensor nodes. Results: Finally, a real world target tracking experiment had been illustrated for static and mobile targets. Here blind child is considered as the target to be tracked within the sensor network. Conclusion: Mathematical analysis and the real world results showed that the target tracking system based on a distributed WSN make efficient use of the communication resources and achieve accurate target tracking successfully.

DILAND: An Algorithm for Distributed Sensor Localization With Noisy Distance Measurements

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We present an algorithm for distributed sensor localization with noisy distance measurements (DILAND) that extends and makes the DLRE more robust. DLRE is a distributed sensor localization algorithm in <emphasis emphasistype="italic"><formula formulatype="inline"><tex Notation="TeX">$\BBR ^{m}~(m\geq 1)$</tex></formula></emphasis> introduced in our previous work (<emphasis emphasistype="boldital">IEEE Trans. Signal Process.</emphasis>, vol. 57, no. 5, pp. 2000–2016, May 2009). DILAND operates when: 1) the communication among the sensors is noisy; 2) the communication links in the network may fail with a nonzero probability; and 3) the measurements performed to compute distances among the sensors are corrupted with noise. The sensors (which do not know their locations) lie in the convex hull of at least <emphasis emphasistype="italic"><formula formulatype="inline"><tex Notation="TeX">$m+1$</tex></formula></emphasis> anchors (nodes that know their own locations). Under minimal assumptions on the connectivity and triangulation of each sensor in the network, we show that, under the broad random phenomena described above, DILAND converges almost surely (a.s.) to the exact sensor locations. </para>

Sensor Localization under Limited Measurement Capabilities

In this article we survey the latest progress on sensor localization, focusing on distance measurement techniques and localization algorithms for a sensor to determine its own location. We illustrate why inaccurate distance measurements, arising from tight hardware design constraints, raise challenges in sensor localization and how proposed approaches strive to overcome these challenges. We conclude that sensor localization algorithms must be designed to accommodate different application requirements in terms of costs, energy consumption, and localization accuracy

Locating the nodes: cooperative localization in wireless sensor networks

Accurate and low-cost sensor localization is a critical requirement for the deployment of wireless sensor networks in a wide variety of applications. In cooperative localization, sensors work together in a peer-to-peer manner to make measurements and then forms a map of the network. Various application requirements influence the design of sensor localization systems. In this article, the authors describe the measurement-based statistical models useful to describe time-of-arrival (TOA), angle-of-arrival (AOA), and received-signal-strength (RSS) measurements in wireless sensor networks. Wideband and ultra-wideband (UWB) measurements, and RF and acoustic media are also discussed. Using the models, the authors have shown the calculation of a Cramer-Rao bound (CRB) on the location estimation precision possible for a given set of measurements. The article briefly surveys a large and growing body of sensor localization algorithms. This article is intended to emphasize the basic statistical signal processing background necessary to understand the state-of-the-art and to make progress in the new and largely open areas of sensor network localization research.

Active array sensor localization

Sensor array processing algorithms for source localization often require precise knowledge of the sensor locations, which is often not available in practice. Errors in the sensor locations are known to degrade severely the performance of these algorithms. This paper proposes iterative damped Gauss-Newton maximum likelihood array sensor localization algorithms using known calibration source signals. Cramér-Rao bound (CRB) expressions for the sensor location parameters are found for this problem. Two calibration source signal models are considered. One assumes that each signal waveform is known up to a scaling constant and phase, while the other requires complete knowledge of the signal waveform. For uncorrelated, completely known calibration signals, a necessary and sufficient condition for the optimal choice of the source bearings that minimizes the sensor position CRB variances is presented. Numerical results illustrate that the maximum likelihood calibration algorithms attain the CRB asymptotically.