Bandwidth-constrained distributed estimation for wireless sensor networks-part II: unknown probability density function

2006 ◽  
Vol 54 (7) ◽  
pp. 2784-2796 ◽  
Author(s):  
A. Ribeiro ◽  
G.B. Giannakis
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Distributed Estimation ◽  
Wireless Sensor ◽  
Unknown Probability

Robust Decentralized and Distributed Estimation of a Correlated Parameter Vector in MIMO-OFDM Wireless Sensor Networks

2021 ◽  
pp. 1-1
Author(s):  
Kunwar Pritiraj Rajput ◽  
Mohammad Faisal Ahmed ◽  
Naveen K. D. Venkategowda ◽  
Aditya K. Jagannatham ◽  
Govind Sharma ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Distributed Estimation ◽  
Wireless Sensor ◽  
Parameter Vector ◽  
Mimo Ofdm

scholarly journals Performance Analysis of Distributed Estimation for Data Fusion Using a Statistical Approach in Smart Grid Noisy Wireless Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 567 ◽  
Author(s):  
Chatura Seneviratne ◽  
Patikiri Arachchige Don Shehan Nilmantha Wijesekara ◽  
Henry Leung
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Smart Grid ◽  
Communication Networks ◽  
Smart Grids ◽  
Measurement Errors ◽  
Power Grid ◽  
Distributed Estimation ◽  
Wireless Sensor ◽  
Grid Infrastructures

Internet of Things (IoT) can significantly enhance various aspects of today’s electric power grid infrastructures for making reliable, efficient, and safe next-generation Smart Grids (SGs). However, harsh and complex power grid infrastructures and environments reduce the accuracy of the information propagating through IoT platforms. In particularly, information is corrupted due to the measurement errors, quantization errors, and transmission errors. This leads to major system failures and instabilities in power grids. Redundant information measurements and retransmissions are traditionally used to eliminate the errors in noisy communication networks. However, these techniques consume excessive resources such as energy and channel capacity and increase network latency. Therefore, we propose a novel statistical information fusion method not only for structural chain and tree-based sensor networks, but also for unstructured bidirectional graph noisy wireless sensor networks in SG environments. We evaluate the accuracy, energy savings, fusion complexity, and latency of the proposed method by comparing the said parameters with several distributed estimation algorithms using extensive simulations proposing it for several SG applications. Results prove that the overall performance of the proposed method outperforms other fusion techniques for all considered networks. Under Smart Grid communication environments, the proposed method guarantees for best performance in all fusion accuracy, complexity and energy consumption. Analytical upper bounds for the variance of the final aggregated value at the sink node for structured networks are also derived by considering all major errors.

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