Motion–Related Resource Allocation in Dynamic Wireless Visual Sensor Network Environments

2014 ◽  
Vol 23 (1) ◽  
pp. 56-68 ◽  
Author(s):  
Angeliki V. Katsenou ◽  
Lisimachos P. Kondi ◽  
Konstantinos E. Parsopoulos
Keyword(s):  
Resource Allocation ◽  
Sensor Network ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Network Environments

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

Dynamic Resource Allocation for Compressive-Sensing-Based Wireless Visual Sensor Networks with Energy Harvesting

2013 ◽  
Vol 303-306 ◽  
pp. 187-190
Author(s):  
Lei You ◽  
Xin Su ◽  
Yu Tong Han
Keyword(s):  
Resource Allocation ◽  
Energy Harvesting ◽  
Compressive Sensing ◽  
Information Acquisition ◽  
Visual Information ◽  
Sensor Nodes ◽  
Visual Sensor ◽  
Visual Sensor Network ◽  
Available Bandwidth ◽  
Potential Applications

Wireless visual sensor network (WVSN) is emerging with many potential applications. The lifetime of a WVSN is seriously dependent on the energy shored in the battery of its sensor nodes as well as the adopted compression and resource allocation scheme. In this paper, we use the energy harvesting to provide almost perpetual operation of the networks and compressed-sensing-based encoding to decrease the power consumption of acquiring visual information at the front-end sensors. We propose a dynamic algorithm to jointly allocate power for both compressive-sensing-based visual information acquisition and data transmission, as well as the available bandwidth under energy harvesting and stability constraints. A virtual energy queue is introduced to control the resource allocation and the measurement rate in each time slot. The algorithm can guarantee the stability of the visual data queues in all sensors and achieve near-optimal performance.

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.

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