Visual sensor network lifetime maximization by prioritized scheduling of nodes

2013 ◽  
Vol 36 (1) ◽  
pp. 409-419 ◽  
Author(s):  
M. Hooshmand ◽  
S.M.R. Soroushmehr ◽  
P. Khadivi ◽  
S. Samavi ◽  
S. Shirani
Keyword(s):  
Sensor Network ◽  
Network Lifetime ◽  
Visual Sensor ◽  
Lifetime Maximization ◽  
Visual Sensor Network

scholarly journals An effective method for the abnormal monitoring of stage performance based on visual sensor network

2018 ◽  
Vol 14 (4) ◽  
pp. 155014771876957 ◽  
Author(s):  
Fuquan Zhang ◽  
Gangyi Ding ◽  
Lin Xu ◽  
Bo Chen ◽  
Zuoyong Li
Keyword(s):  
Sensor Network ◽  
Detection Efficiency ◽  
Abnormal Behavior ◽  
Visual Sensor ◽  
Monitoring Method ◽  
Visual Sensor Network ◽  
Manifold Structure ◽  
Local Linear Embedding ◽  
Abnormal Behavior Detection ◽  
Stage Performance

Abnormal monitoring of stage performance plays a vital role in the stage performance. For the real-time stage performance, detection efficiency and accuracy are particularly important. As the traditional monitoring method based on sparse description model to realize abnormal behavior of stage performance did not realize the manifold structure during the performance, the behavior characteristics are sparse, and the decomposition has higher volatility, the recognition accuracy of abnormal behavior is low. Therefore, an abnormal monitoring method of stage performance based on visual sensor network is proposed, the overall structure of the abnormal monitoring system of stage performance based on the vision sensor network is analyzed, the hardware structure and software composition of the system are designed, and the method of monitoring the abnormal behavior of the system is analyzed emphatically. Through the background subtraction, the weighted threshold-based segmentation of the target image from the background image, the chaotic search particle swarm optimization algorithm based on image target detection and tracking algorithm for target tracking by mean shift, the abnormal behavior of local linear embedding and detection method based on sparse representation, a comprehensive analysis of the local manifold structure of sample is set. Enhance the stage performance of abnormal behavior detection efficiency and accuracy. The experimental results show that the proposed method has higher detection efficiency and accuracy and has higher robustness.

Visual Sensor Network Processing and Preventative Steganalysis

2011 ◽  
pp. 340-357
Author(s):  
Julien Sebastien Jainsky ◽  
Deepa Kundur
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
High Rate ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Visual Sensor Networks ◽  
Steganographic Attacks ◽  
Wireless Visual Sensor Networks ◽  
The Moment ◽  
Rate Of Success

In this chapter, we discuss the topic of security in wireless visual sensor networks. In particular, attention is brought to steganographic security and how it can be discouraged without challenging the primary objectives of the network. We motivate the development and implementation of more lightweight steganalytic solutions that take into account the resources made available by the network’s deployment and its application in order to minimize the steganalysis impact on the WVSN workload. The concept of preventative steganalysis is also introduced in this chapter as a means to protect the network from the moment it is deployed. Preventative steganalysis aims at discouraging any potential steganographic attacks by processing the WVSN collected data such that the possibility of steganography becomes very small and the steganalysis leads to high rate of success.

Low Level Representation of Data for Visual Sensor Network

2011 ◽  
pp. 94-115
Author(s):  
Ruth Aguilar-Ponce ◽  
J. Luis Tecpanecatl-Xihuitl ◽  
Alfonso Alba-Cadena
Keyword(s):  
Sensor Network ◽  
Research Field ◽  
Data Filtering ◽  
Visual Data ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Data Encoding ◽  
Active Research ◽  
Future Direction ◽  
Camera Sensor

Wireless Sensor Network future direction is going towards more complex sensor such as camera sensor. Therefore, a very active research field is Visual Sensor Network. This type of network brings new challenges such as processing and transmitting a massive amount of data generated by the camera sensor. The efforts into decreasing the amount of data to be transmitted are going towards two directions: data encoding and data filtering. This chapter introduces an algorithm for each direction. Visual data encoding is performed by means of Predictive Video Encoding using Phase-Only Correlation function to achieve motion estimation. Visual data filtering is done at the lowest level of abstraction and is performed in three phases: pixel classification, background update and detection. The algorithms involved in each phase are light in terms of complexity and memory resources.

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