scholarly journals Evaluation of Video Quality in Wireless Multimedia Sensor Networks

2016 ◽  
Vol 6 (1) ◽  
pp. 223
Author(s):  
Mustafa Shakir ◽  
Obaid Ur Rehman ◽  
Zeeshan Abbas ◽  
Abdullah Masood ◽  
Wajeeha Shahid
Keyword(s):  
Sensor Networks ◽  
Signal To Noise Ratio ◽  
Video Quality ◽  
Discrete Event ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Opinion Score ◽  
Different Types ◽  
Multimedia Transmissions

<p class="Default">Simulating wireless sensor networks; there implementation and evaluation, require the use of a discrete event simulator. Omnet++ is quite a powerful simulator which supports concise and easy modeling of wired as well as wireless sensors environment. Scenarios involving multimedia transmissions with characteristics of video quality control and evaluation must be computed on the basis of Quality of Experience which relies on user’s perception to maintain the video quality. For the multimedia growth and awareness of future WMSNs, it is quite necessary that the performance should be tested for different types of radio models. So varying the radio parameters may allow for the optimization and improvement of the video quality. In this paper we have provided a test bench for the easy evaluation and optimization of the performance of WMSNs using different radio models. The performance is evaluated based on the QoE metrics; i.e. PSNR(Peak Signal-to-Noise ratio) and MoS(Mean Opinion Score), which depend on user’s perception to maintain the video quality.</p>

Evaluation of Video Quality in Wireless Multimedia Sensor Networks

2016 ◽  
Vol 6 (1) ◽  
pp. 223
Author(s):  
Mustafa Shakir ◽  
Obaid Ur Rehman ◽  
Zeeshan Abbas ◽  
Abdullah Masood ◽  
Wajeeha Shahid
Keyword(s):  
Sensor Networks ◽  
Signal To Noise Ratio ◽  
Video Quality ◽  
Discrete Event ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Opinion Score ◽  
Different Types ◽  
Multimedia Transmissions

<p class="Default">Simulating wireless sensor networks; there implementation and evaluation, require the use of a discrete event simulator. Omnet++ is quite a powerful simulator which supports concise and easy modeling of wired as well as wireless sensors environment. Scenarios involving multimedia transmissions with characteristics of video quality control and evaluation must be computed on the basis of Quality of Experience which relies on user’s perception to maintain the video quality. For the multimedia growth and awareness of future WMSNs, it is quite necessary that the performance should be tested for different types of radio models. So varying the radio parameters may allow for the optimization and improvement of the video quality. In this paper we have provided a test bench for the easy evaluation and optimization of the performance of WMSNs using different radio models. The performance is evaluated based on the QoE metrics; i.e. PSNR(Peak Signal-to-Noise ratio) and MoS(Mean Opinion Score), which depend on user’s perception to maintain the video quality.</p>

ANFIS-Based Resource Mapping for Query Processing in Wireless Multimedia Sensor Networks

2017 ◽  
Vol 26 (3) ◽  
pp. 505-522
Author(s):  
Nagesha Shivappa ◽  
Sunilkumar S. Manvi
Keyword(s):  
Sensor Networks ◽  
Sensor Node ◽  
Cluster Head ◽  
Sensor Nodes ◽  
Sink Node ◽  
Wireless Multimedia ◽  
Resource Mapping ◽  
Multimedia Sensor Networks ◽  
Node Energy

AbstractWireless multimedia sensor networks (WMSNs) are usually resource constrained, and where the sensor nodes have limited bandwidth, energy, processing power, and memory. Hence, resource mapping is required in a WMSN, which is based on user linguistic quality of service (QoS) requirements and available resources to offer better communication services. This paper proposes an adaptive neuro fuzzy inference system (ANFIS)-based resource mapping for video communications in WMSNs. Each sensor node is equipped with ANFIS, which employs three inputs (user QoS request, available node energy, and available node bandwidth) to predict the quality of the video output in terms of varying number of frames/second with either fixed or varying resolution. The sensor nodes periodically measure the available node energy and also the bandwidth. The spatial query processing in the proposed resource mapping works as follows. (i) The sink node receives the user query for some event. (ii) The sink node sends the query through an intermediate sensor node(s) and cluster head(s) in the path to an event node. A cluster head-based tree routing algorithm is used for routing. (iii) The query passes through ANFIS of intermediate sensor nodes and cluster heads, where each node predicts the quality of the video output. (iv) The event node chooses the minimum quality among all cluster heads and intermediate nodes in the path and transmits the video output. The work is simulated in different network scenarios to test the performance in terms of predicted frames/second and frame format. To the best of our knowledge, the proposed resource mapping is the first work in the area of sensor networks. The trained ANFIS predicts the output video quality in terms of number of frames/second (or H.264 video format) accurately for the given input.

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