Linear Decentralized Estimation of Correlated Data for Power-Constrained Wireless Sensor Networks

Behbahani, Alireza S.; Eltawil, Ahmed M.; Jafarkhani, Hamid

doi:10.1109/tsp.2012.2208961

Cited by 70 publications

(91 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…They are similar to (17) and (18), with the difference that D a is replaced by is a decreasing function of P k s and P tot (see Appendix X-B). Therefore, solving (SP3) for P k s, we find that…”

Section: Coupled Scheme For Minimizing D Bmentioning

confidence: 87%

Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks

Sani

Vosoughi

2016

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

We consider the distributed estimation of a Gaussian vector with a linear observation model in an inhomogeneous wireless sensor network, in which a fusion center (FC) reconstructs the unknown vector using a linear estimator. Sensors employ uniform multi-bit quantizers and binary PSK modulation, and they communicate with the FC over orthogonal power-and bandwidth-constrained wireless channels.We study transmit power and quantization rate (measured in bits per sensor) allocation schemes that minimize the mean-square error (MSE). In particular, we derive two closed-form upper bounds on the MSE in terms of the optimization parameters and propose "coupled" and "decoupled" resource allocation schemes that minimize these bounds. We show that the bounds are good approximations of the simulated MSE and that the performance of the proposed schemes approaches the clairvoyant centralized estimation when the total transmit power or bandwidth is very large. We investigate how the power and rate allocations are dependent on the sensors' observation qualities and channel gains and on the total transmit power and bandwidth constraints. Our simulations corroborate our analytical results and demonstrate the superior performance of the proposed algorithms. Index TermsDistributed estimation, Gaussian vector, upper bounds on MSE, power and rate allocation, ellipsoid method, quantization, linear estimator, linear observation model.

show abstract

Section: Coupled Scheme For Minimizing D Bmentioning

confidence: 87%

Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks

Sani

Vosoughi

2016

IEEE Trans. Signal Process.

View full text Add to dashboard Cite

show abstract

“…However, the solution is provided only for a coherent MAC channel with no intersymbol interference between transmissions over each link by using semidefinite programming (SDP). In [6], the authors extend this work and provide an iterative solution for the case of coherent MAC channels with intersymbol interference. In contrast, the authors in [7] propose a wireless sensor network where each sensor observes a different scalar parameter and sends its encoded observation to the fusion center for the final estimation of all parameters.…”

Section: Introductionmentioning

confidence: 93%

“…The K sensors transmit their information to a fusion center through a coherent MAC. Note that in the work provided in [6], unlike here, all sensors observe the same vector source (θ). The observation at Sensor i can be described as…”

Section: Notationmentioning

confidence: 99%

Linear Estimation of Correlated Vector Sources for Wireless Sensor Networks with Fusion Center

Behbahani

Eltawil

Jafarkhani

2012

IEEE Wireless Commun. Lett.

View full text Add to dashboard Cite

In this letter, we consider decentralized estimation of unknown random vectors by using wireless sensors and a fusion center (FC). We adopt a linear model for decentralized estimation of vector sources where both observation models and sensor operations are linear and the multiple access channel (MAC) is coherent. Each sensor observes a different vector source, where vector sources can be correlated. Sensors are designed to minimize the total mean square error (MSE) at the fusion center subject to transmit power constraints at the sensors. While a closed form solution cannot be found, an iterative algorithm is proposed. Simulations are provided to verify the analysis and present the performance of the proposed scheme.

show abstract

“…Here, a normal idea is to reduce communication traffic between sensors and the FC at each period. Therefore, two common methods have been used in existing research: the dimensionality reduction method [8,9] and the quantization method [10]. The idea of the dimensionality reduction method is used to convert high-dimensional data into low-dimensional data through specific mechanisms.…”

Section: Introductionmentioning

confidence: 99%

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

Zhang

Chao

2017

Sensors

View full text Add to dashboard Cite

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance.

show abstract

Linear Decentralized Estimation of Correlated Data for Power-Constrained Wireless Sensor Networks

Cited by 70 publications

References 27 publications

Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks

Distributed Vector Estimation for Power- and Bandwidth-Constrained Wireless Sensor Networks

Linear Estimation of Correlated Vector Sources for Wireless Sensor Networks with Fusion Center

Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

Contact Info

Product

Resources

About