Design and implementation of smart sensor nodes for wireless disaster monitoring systems

2004 ◽  
Author(s):  
Yih-Fan Chen ◽  
Wen-Jong Wu ◽  
Chun-Kuang Chen ◽  
Chih-Min Wen ◽  
Ming-Hui Jin ◽  
...  
Keyword(s):  
Sensor Nodes ◽  
Monitoring Systems ◽  
Smart Sensor ◽  
Design And Implementation ◽  
Disaster Monitoring

Demonstration of the plug and play of smart sensor nodes

2010 ◽  
Author(s):  
Ashwin Juvva ◽  
D. Gurkan ◽  
Suman Gumudavelli ◽  
Ray Wang
Keyword(s):  
Sensor Nodes ◽  
Smart Sensor ◽  
Plug And Play

scholarly journals Autonomous Terrestrial Image Segmentation and Sensor Node Localization for Disaster Management using Plant Growth Simulation Algorithm

2017 ◽  
Author(s):  
Deblina Bhattacharjee ◽  
Anand Paul ◽  
Won-Hwa Hong ◽  
HyunCheol Seo ◽  
Karthik S.
Keyword(s):  
Plant Growth ◽  
Emergency Response ◽  
Sensor Nodes ◽  
Simulation Algorithm ◽  
Sources Of Information ◽  
Node Localization ◽  
Growth Simulation ◽  
Aerial Vehicle ◽  
Disaster Monitoring ◽  
Plant Growth Simulation Algorithm

The use of unmanned aerial vehicle (UAV) during emergency response of a disaster has been widespread in recent years and the terrain images captured by the cameras on board these vehicles are significant sources of information for such disaster monitoring operations. Thus, analyzing such images are important for assessing the terrain of interest during such emergency response operations. Further, these UAVs are mainly used in disaster monitoring systems for the automated deployment of sensor nodes in real time. Therefore, deploying and localizing the wireless sensor nodes optimally, only in the regions of interest that are identified by segmenting the images captured by UAVs, hold paramount significance thereby effecting their performance. In this paper, the highly effective nature-inspired Plant Growth Simulation Algorithm (PGSA) has been applied for the segmentation of such terrestrial images and also for the localization of the deployed sensor nodes. The problem is formulated as a multi-dimensional optimization problem and PGSA has been used to solve it. Furthermore, the proposed method has been compared to other existing evolutionary methods and simulation results show that PGSA gives better performance with respect to both speed and accuracy unlike other techniques in literature.

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