scholarly journals Video Compression Schemes Using Edge Feature on Wireless Video Sensor Networks

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Phat Nguyen Huu ◽  
Vinh Tran-Quang ◽  
Takumi Miyoshi
Keyword(s):  
Energy Consumption ◽  
Video Compression ◽  
Cluster Head ◽  
Low Complexity ◽  
Sensor Nodes ◽  
Wireless Video ◽  
Compression Process ◽  
Wireless Video Sensor Networks ◽  
Balance Energy ◽  
Video Sensor

This paper puts forward a low-complexity video compression algorithm that uses the edges of objects in the frames to estimate and compensate for motion. Based on the proposed algorithm, two schemes that balance energy consumption among nodes in a cluster on a wireless video sensor network (WVSN) are proposed. In these schemes, we divide the compression process into several small processing components, which are then distributed to multiple nodes along a path from a source node to a cluster head in a cluster. We conduct extensive computational simulations to examine the truth of our method and find that the proposed schemes not only balance energy consumption of sensor nodes by sharing of the processing tasks but also improve the quality of decoding video by using edges of objects in the frames.

Wireless Video Sensor Networks

2011 ◽  
pp. 40-58 ◽  
Author(s):  
Abdelrahman Elamin ◽  
Varun Jeoti ◽  
Samir Belhouari
Keyword(s):  
Video Coding ◽  
Low Complexity ◽  
Video Codec ◽  
Sensor Nodes ◽  
Wireless Video ◽  
Wireless Video Sensor Networks ◽  
Video Application ◽  
Video Sensors ◽  
Video Sensor Networks ◽  
Video Sensor

Wireless Video Sensors Networks (WVSNs) generally suffer from the constraint that their sensor nodes must consume very little power. In this rapidly emerging video application, the traditional video coding architecture cannot be used due to its high encoding complexity. Thankfully, some theorems from Information Theory suggest that this problem can be solved by shifting the encoder tasks, partially or totally, to the decoder. These theorems are employed in the design of so-called Distributed Video Coding (DVC) solutions, the subject matter of this chapter. The chapter not only introduces the DVC but also reviews some important developments of the popular Stanford Wyner-Ziv coding architecture and caps it with latest research trends highlighting a Region-Based-Wyner-Ziv video codec that enables low-complexity encoding while achieving high compression efficiency.

Performance Analysis of RNC Clustering Protocol in Wireless Sensor Network

2019 ◽  
Vol 10 ◽  
Author(s):  
Piyush Rawat ◽  
Siddhartha Chauhan
Keyword(s):  
Energy Consumption ◽  
Network Performance ◽  
Energy Levels ◽  
Cluster Head ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Threshold Function ◽  
Efficient Manner ◽  
Clustering Protocol

Background and Objective: The functionalities of wireless sensor networks (WSN) are growing in various areas, so to handle the energy consumption of network in an efficient manner is a challenging task. The sensor nodes in the WSN are equipped with limited battery power, so there is a need to utilize the sensor power in an efficient way. The clustering of nodes in the network is one of the ways to handle the limited energy of nodes to enhance the lifetime of the network for its longer working without failure. Methods: The proposed approach is based on forming a cluster of various sensor nodes and then selecting a sensor as cluster head (CH). The heterogeneous sensor nodes are used in the proposed approach in which sensors are provided with different energy levels. The selection of an efficient node as CH can help in enhancing the network lifetime. The threshold function and random function are used for selecting the cluster head among various sensors for selecting the efficient node as CH. Various performance parameters such as network lifespan, packets transferred to the base station (BS) and energy consumption are used to perform the comparison between the proposed technique and previous approaches. Results and Discussion: To validate the working of the proposed technique the simulation is performed in MATLAB simulator. The proposed approach has enhanced the lifetime of the network as compared to the existing approaches. The proposed algorithm is compared with various existing techniques to measure its performance and effectiveness. The sensor nodes are randomly deployed in a 100m*100m area. Conclusion: The simulation results showed that the proposed technique has enhanced the lifespan of the network by utilizing the node’s energy in an efficient manner and reduced the consumption of energy for better network performance.

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