Unscented summation information‐weighted consensus filter for distributed sensor networks with incomplete information

2019 ◽  
Vol 33 (7) ◽  
pp. 1097-1117 ◽  
Author(s):  
Peng Yao ◽  
Gang Liu ◽  
Yanfei Liu ◽  
Qi Tian
Keyword(s):  
Sensor Networks ◽  
Incomplete Information ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Consensus Filter

scholarly journals Consensus-Based Filter for Distributed Sensor Networks with Colored Measurement Noise

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3678 ◽  
Author(s):  
Jinran Wang ◽  
Peng Dong ◽  
Zhongliang Jing ◽  
Jin Cheng
Keyword(s):  
Sensor Networks ◽  
The State ◽  
Measurement Noise ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Distributed State Estimation ◽  
Consensus Filter ◽  
Simulation Results ◽  
Augmented State ◽  
Distributed Filter

Consensus filtering is an effective method for distributed state estimation of distributed sensor networks and the assumption of white measurement noise is widely used. However, when the measurement noise is colored, the traditional consensus filter cannot work well. In this paper, we first propose a consensus-based distributed filter for colored measurement noise by augmenting the state to include the colored measurement noise. To improve the efficiency of the filter, only local colored measurement noise is integrated into the augmented state for each local filter. Furthermore, another consensus-based distributed filter based on measurement differencing scheme is developed to eliminate the ill-conditioned computations of the augmented state approach. In addition, this method does not need to augment the state and thus has lower dimension than the augmented state filter. Simulation results demonstrate the superiority of the proposed methods.

scholarly journals Distributed Filter with Consensus Strategies for Sensor Networks

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
Xie Li ◽  
Huang Caimou ◽  
Hu Haoji
Keyword(s):  
Sensor Networks ◽  
Research Problem ◽  
Consensus Algorithm ◽  
Distributed Sensor Networks ◽  
Distributed Sensor ◽  
Consensus Filter ◽  
Information Consensus ◽  
Distributed Filter ◽  
Important Research Problem ◽  
Better Than

Consensus algorithm for networked dynamic systems is an important research problem for data fusion in sensor networks. In this paper, the distributed filter with consensus strategies known as Kalman consensus filter and information consensus filter is investigated for state estimation of distributed sensor networks. Firstly, an in-depth comparison analysis between Kalman consensus filter and information consensus filter is given, and the result shows that the information consensus filter performs better than the Kalman consensus filter. Secondly, a novel optimization process to update the consensus weights is proposed based on the information consensus filter. Finally, some numerical simulations are given, and the experiment results show that the proposed method achieves better performance than the existing consensus filter strategies.

scholarly journals Clique Size in Sensor Networks with Key Pre-Distribution Based on Transversal Design

2005 ◽  
Vol 1 (3-4) ◽  
pp. 345-354 ◽  
Author(s):  
Dibyendu Chakrabarti ◽  
Subhamoy Maitra ◽  
Bimal Roy
Keyword(s):  
Sensor Networks ◽  
Distributed Computing ◽  
Secure Communication ◽  
Sensor Nodes ◽  
Transversal Design ◽  
Secret Key ◽  
Distributed Sensor Networks ◽  
Distributed Sensor

Key pre-distribution is an important area of research in Distributed Sensor Networks (DSN). Two sensor nodes are considered connected for secure communication if they share one or more common secret key(s). It is important to analyse the largest subset of nodes in a DSN where each node is connected to every other node in that subset (i.e., the largest clique). This parameter (largest clique size) is important in terms of resiliency and capability towards efficient distributed computing in a DSN. In this paper, we concentrate on the schemes where the key pre-distribution strategies are based on transversal design and study the largest clique sizes. We show that merging of blocks to construct a node provides larger clique sizes than considering a block itself as a node in a transversal design.

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