Hidden Markov Model with Computational Intelligence for Dynamic Clustering in Wireless Sensor Networks

2013 ◽  
pp. 19-30
Author(s):  
K. N. Veena ◽  
B. P. Vijaya Kumar
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Computational Intelligence ◽  
Hidden Markov ◽  
Wireless Sensor ◽  
Dynamic Clustering

Hidden Gaussian Markov model for distributed fault detection in wireless sensor networks

2017 ◽  
Vol 40 (6) ◽  
pp. 1788-1798 ◽  
Author(s):  
Marwa Saihi ◽  
Ahmed Zouinkhi ◽  
Boumedyen Boussaid ◽  
Mohamed Naceur Abdelkarim ◽  
Guillaume Andrieux
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Markov Model ◽  
Hidden Markov Model ◽  
Sensor Network ◽  
Hidden Markov ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Normal Operation

Wireless sensor networks are based on a large number of sensor nodes used to measure information like temperature, acceleration, displacement, or pressure. The measurements are used to estimate the state of the monitored system or area. However, the quality of the measurements must be guaranteed to ensure the reliability of the estimated state of the system. Actually, sensors can be used in a hostile environment such as, on a battle field in the presence of fires, floods, earthquakes. In these environments as well as in normal operation, sensors can fail. The failure of sensor nodes can also be caused by other factors like: the failure of a module (such as the sensing module) due to the fabrication process models, loss of battery power and so on. A wireless sensor network must be able to identify faulty nodes. Therefore, we propose a probabilistic approach based on the Hidden Markov Model to identify faulty sensor nodes. Our proposed approach predicts the future state of each node from its actual state, so the fault could be detected before it occurs. We use an aided judgment of neighbour sensor nodes in the network. The algorithm analyses the correlation of the sensors’ data with respect to its neighbourhood. A systematic approach to divide a network on cliques is proposed to fully draw the neighbourhood of each node in the network. After drawing the neighbourhood of each node (cliques), damaged cliques are identified using the Gaussian distribution theorem. Finally, we use the Hidden Markov Model to identify faulty nodes in the identified damaged cliques by calculating the probability of each node to stay in its normal state. Simulation results demonstrate our algorithm is efficient even for a huge wireless sensor network unlike previous approaches.

Sign in / Sign up

Export Citation Format

Share Document