Estimation In Wireless Sensor Networks Research Articles

Distributed target tracking and boundary estimation in wireless sensor networks

We study the problem of target tracking and boundary detection of a substance diffusing from a mobile source using a wireless sensor network. We present a Prediction-based Mobility Adaptive Tracking (P-MAT algorithm to study the tradeoff among energy, accuracy of tracking, coverage and boundary estimation. P-MAT minimises overall energy consumption by incorporating adaptivity in two forms: (1) the size of the active region and (2) modulation of the sampling rate. It uses adaptive Kalman filtering to predict the target's future location and velocity. The predicted target location determines a set of sensors surrounding that location to be activated known as the active region. Sensors in the active region are responsible for target tracking and boundary detection. In this article, we include dynamic boundary estimation. Boundary estimation in many situations can be performed efficiently using a subset of nodes within the vicinity of the phenomenon. This subset of nodes in our algorithm is the set of nodes in the active region. As the substance spreads, sensors in the active region determine if additional sensors outside of the active region are needed to enclose the boundary. Results from simulation experiments show that P-MAT can perform both tracking and boundary.

A Novel Manifold Learning Algorithm for Localization Estimation in Wireless Sensor Networks

We propose an accurate, distributed localization method that uses the connectivity measure to localize nodes in a wireless sensor network. The proposed method is based on a self-organizing isometric embedding algorithm that adaptively emphasizes the most accurate range of measurements and naturally accounts for communication constraints within the sensor network. Each node adaptively chooses a neighborhood of sensors and updates its estimate of position by minimizing a local cost function and then passes this update to the neighboring sensors. Simulations demonstrate that the proposed method is more robust to measurement error than previous methods and it can achieve comparable results using much fewer anchor nodes than previous methods.

Distributed Adaptive Quantization and Estimation for Wireless Sensor Networks

In this letter, the problem of distributed parameter estimation in a wireless sensor network is considered, where due to bandwidth constraint, each sensor node sends only one bit of information to a fusion center. We propose a new distributed adaptive quantization scheme by which each individual sensor node dynamically adjusts the threshold of its quantizer based on earlier transmissions from other sensor nodes. The maximum likelihood estimator (MLE) and the Cramer-Rao bound (CRB) associated with our distributed adaptive quantization scheme are derived. Numerical results depicting the performance and advantages of our approach over a fixed quantization scheme are presented.

Location estimation using received signal power and maximum likelihood estimation in wireless sensor networks

AbstractThis paper considers the sensor network based on IEEE 802.15.4, and proposes a method in which a probabilistic model of wireless propagation characteristics in a real environment is constructed, and the node position is estimated by maximum‐likelihood estimation using the model. The performance of the proposed method is evaluated by simulation and position estimation experiments based on the probabilistic model obtained from the actual indoor measurement. It is shown that the proposed method is useful for indoor personnel location management. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 1, 90(12): 62–72, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecja.20359

Hop-Distance Estimation in Wireless Sensor Networks with Applications to Resources Allocation

We address a fundamental problem in wireless sensor networks, how many hops does it take a packet to be relayed for a given distance? For a deterministic topology, this hop-distance estimation reduces to a simple geometry problem. However, a statistical study is needed for randomly deployed WSNs. We propose a maximum-likelihood decision based on the conditional pdf of . Due to the computational complexity of , we also propose an attenuated Gaussian approximation for the conditional pdf. We show that the approximation visibly simplifies the decision process and the error analysis. The latency and energy consumption estimation are also included as application examples. Simulations show that our approximation model can predict the latency and energy consumption with less than half RMSE, compared to the linear models.

An energy-efficient adaptive DSC scheme for wireless sensor networks

Distributed source coding (DSC) has been proven in theory that it can be used to compress correlated signals with or without loss. Recently this coding method has been used for the application of remote signal estimation in wireless sensor networks (WSN), where multiple sensor nodes compress their correlated observations without inter-node communications. Energy and bandwidth are therefore efficiently saved. Challenges remain, however, in the design of practical and adaptive DSC schemes for WSN. In this paper, we study the problem of a random-binning based DSC scheme for remote source estimation in WSN. We design a DSC scheme and analyze its performance on the estimated signal to distortion ratio (SDR), in which observation noise, quantization distortion, DSC decoding errors and network packet losses are all taken into account. With the introduction of a detailed power consumption model for wireless sensor communications, we quantitatively analyze the overall network energy consumption. We further propose a novel adaptive control mechanism for the DSC scheme, which flexibly optimizes the DSC performance in terms of either SDR or energy consumption by adapting the source coding and transmission parameters to the network conditions. Simulations show the proposed DSC scheme and adaptive control mechanism can either save up to 31.6% energy consumption without decreasing the SDR or maximize the SDR with saving up to 9.4% energy consumption.

On channel adaptive energy management with available bandwidth estimation in wireless sensor networks

AbstractTo enhance the lifetime of a sensor network which consists of hundreds or even thousands of resource‐limited devices, energy efficient communication is mandatory. Despite that a plethora of work has been done in designing energy efficient protocols for sensor networks, the time‐varying nature of wireless channel is largely unexplored. Indeed, we believe that a cross‐layer design on power aware communication is necessary to further optimize energy usage. In this paper, we propose a new channel adaptive power aware protocol, called CAEM, which works by dynamically adjusting the data throughput under different channel conditions with the help of an adaptive channel coding and modulation facility. Each sensor device judiciously accesses the wireless medium in that communication activity is reduced for devices under poor channel conditions. Simulation results indicate that the proposed CAEM protocol can lead to energy conservation by as much as 30 per cent. Furthermore, CAEM is also efficient in channel utilization as it generates a higher data throughput even under heavy traffic load. Copyright © 2007 John Wiley & Sons, Ltd.

Rate-Constrained Distributed Estimation in Wireless Sensor Networks

In this paper, we consider the distributed parameter estimation in wireless sensor networks where a total bit rate constraint is imposed. We study the optimal tradeoff between the number of active sensors and the quantization bit rate for each active sensor to minimize the estimation mean-square error (MSE). To facilitate the solution, we first introduce a concept of equivalent 1-bit MSE function. Next, we present an optimal distributed estimation algorithm for homogeneous sensor networks based on minimizing the equivalent 1-bit MSE function. Then, we present a quasi-optimal distributed estimation algorithm for heterogeneous sensor networks, which is also based on the equivalent 1-bit MSE function, and the upper bound of the estimation MSE of the proposed algorithm is addressed. Furthermore, a theoretical nonachievable lower bound of the estimation MSE under the total bit rate constraint is stated and it is shown that our proposed algorithm is quasi-optimal within a factor 2.2872 of the theoretical lower bound. Simulation results also show that significant reduction in estimation MSE is achieved by our proposed algorithm when compared with other uniform methods

A Distributed Source Coding Scheme for Source Estimation in Wireless Sensor Networks

This letter proposes a simple and efficient random-binning based distributed source coding (DSC) scheme for application to remote source estimation in wireless sensor networks. The scheme jointly encodes data from multiple sensors with side information. It achieves high coding efficiency and reduces power and bandwidth consumption.

Localized Recursive Estimation in Energy Constrained Wireless Sensor Networks

This paper proposes a localized recursive estimation scheme for parameter estimation in wireless sensor networks. Given any parameter of a target occurring at some location and time, a number of sensors recursively estimate the parameter by using their local measurements of the parameter that is attenuated with the distance between a sensor and the target location and corrupted by noise. Compared with centralized estimation schemes that transmit all encoded measurements to a sink (or a fusion center), the recursive scheme needs only to transmit the final estimate to a sink. When the sink is faraway from the sensors and multihop communications have to be used, using localized recursive estimation can help to reduce energy consumption and reduce network traffic load. A sensor sequence with the fastest convergence rate is identified, by which the variance of estimation error reduces faster than all other sequences. In the case of adjustable transmission power, a heuristic has been proposed to find a sensor sequence with the minimum total transmission power when performing the recursive estimation. Numerical examples have been used to compare the performance of the proposed scheme with that of a centralized estimation scheme and have also shown the effectiveness of the proposed heuristic.

Cooperative synchronization and channel estimation in wireless sensor networks

A critical issue in applications involving networks of wireless sensors is their ability to synchronize, and mitigate the fading propagation channel effects. Especially when distributed “slave” sensors (nodes) reach-back to communicate with the “master” sensor (gateway), low power cooperative schemes are well motivated. Viewing each node as an antenna element in a multi-input multi-output (MIMO) multi-antenna system, we design pilot patterns to estimate the multiple carrier frequency offsets (CFO), and the multiple channels corresponding to each node-gateway link. Our novel pilot scheme consists of non-zero pilot symbols along with zeros, which separate nodes in a time division multiple access (TDMA) fashion, and lead to low complexity schemes because CFO and channel estimators per node are decoupled. The resulting training algorithm is not only suitable for wireless sensor networks, but also for synchronization and channel estimation of single- and multi-carrier MIMO systems. We investigate the performance of our estimators analytically, and with simulations.

Relative location estimation in wireless sensor networks

Self-configuration in wireless sensor networks is a general class of estimation problems that we study via the Cramer-Rao bound (CRB). Specifically, we consider sensor location estimation when sensors measure received signal strength (RSS) or time-of-arrival (TOA) between themselves and neighboring sensors. A small fraction of sensors in the network have a known location, whereas the remaining locations must be estimated. We derive CRBs and maximum-likelihood estimators (MLEs) under Gaussian and log-normal models for the TOA and RSS measurements, respectively. An extensive TOA and RSS measurement campaign in an indoor office area illustrates MLE performance. Finally, relative location estimation algorithms are implemented in a wireless sensor network testbed and deployed in indoor and outdoor environments. The measurements and testbed experiments demonstrate 1-m RMS location errors using TOA, and 1- to 2-m RMS location errors using RSS.