scholarly journals Energy Consumption Analysis of Edge Orchestrated Virtualized Wireless Multimedia Sensor Networks

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 5090-5100 ◽  
Author(s):  
Tenager Mekonnen ◽  
Miika Komu ◽  
Roberto Morabito ◽  
Tero Kauppinen ◽  
Erkki Harjula ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Wireless Multimedia Sensor Networks ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Energy Consumption Analysis

scholarly journals Fuzzy logic and image compression based energy efficient application layer algorithm for wireless multimedia sensor networks

2020 ◽  
Vol 17 (2) ◽  
pp. 509-536
Author(s):  
Arafat Senturk ◽  
Resul Kara ◽  
Ibrahim Ozcelik
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Image Compression ◽  
Data Transmission ◽  
Wireless Multimedia Sensor Networks ◽  
Environmental Data ◽  
Sensor Nodes ◽  
Wireless Multimedia ◽  
Application Layer ◽  
Multimedia Sensor Networks

Wireless Sensor Networks (WSN) are the networks that can realize data processing and computation skills of sensor nodes over the wireless channel and they have several communication devices. Wireless Multimedia Sensor Networks (WMSN) are the networks composed of low-cost sensor nodes that transmit realtime multimedia data like voice, image, and video to each other and to sink. WMSN needs more energy and bandwidth than WSN since they transmit a larger amount of data. The size of the data transmitted by the sensor nodes to each other or the sink becomes an important factor in their energy consumption. Energy consumption is a fundamental issue for WMSN. Other issues that affect the progress of WMSN are limited bandwidth and memory constraints. In these networks, for which the node battery lives are important sources, the limited sources must be effectively used by decreasing the transmitted data amount by removing the redundant data after proper processing of the environmental data. A new algorithm is developed to minimize the energy consumption during image data transmission between sensor nodes on WMSN, and so, make the nodes use their most important source, battery life effectively in this study. This algorithm is named as Energy-aware Application Layer Algorithm based on Image Compression (EALAIC). This algorithm makes use of the top three image compression algorithms for WMSN and decides instantly to which one is the most efficient based on three parameters: the distance between the nodes, total node number, and data transmission frequency. In this way, the sensor node battery lives are used efficiently. The performance analysis of the developed algorithm is also done via Network Simulator ? 2 (NS ? 2) and it is compared by the existing algorithms in terms of energy rate (consumed energy/total energy) and PSNR (Peak Signal to Noise Ratio).

scholarly journals A Distributed Image Compression Scheme for Energy Harvesting Wireless Multimedia Sensor Networks

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 667
Author(s):  
Chong Han ◽  
Songtao Zhang ◽  
Biao Zhang ◽  
Jian Zhou ◽  
Lijuan Sun
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Harvesting ◽  
Solar Energy ◽  
Image Compression ◽  
Wireless Multimedia Sensor Networks ◽  
Wireless Multimedia ◽  
Compression Scheme ◽  
Multimedia Sensor Networks ◽  
Normal Node

As an emerging technology, edge computing will enable traditional sensor networks to be effective and motivate a series of new applications. Meanwhile, limited battery power directly affects the performance and survival time of sensor networks. As an extension application for traditional sensor networks, the energy consumption of Wireless Multimedia Sensor Networks (WMSNs) is more prominent. For the image compression and transmission in WMSNs, consider using solar energy as the replenishment of node energy; a distributed image compression scheme based on solar energy harvesting is proposed. Two level clustering management is adopted. The camera node-normal node cluster enables camera nodes to gather and send collected raw images to the corresponding normal nodes for compression, and the normal node cluster enables the normal nodes to send the compressed images to the corresponding cluster head node. The re-clustering and dynamic adjustment methods for normal nodes are proposed to adjust adaptively the operation mode in the working chain. Simulation results show that the proposed distributed image compression scheme can effectively balance the energy consumption of the network. Compared with the existing image transmission schemes, the proposed scheme can transmit more and higher quality images and ensure the survival of the network.

scholarly journals Exploration of Multi-Node Collaborative Image Acquisition and Compression Techniques for Wireless Multimedia Sensor Networks

2019 ◽  
Vol 15 (01) ◽  
pp. 196
Author(s):  
Fangzhou He
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Energy Saving ◽  
Image Acquisition ◽  
Minimum Energy ◽  
Wireless Multimedia Sensor Networks ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Image Collection ◽  
Camera Nodes

<span lang="EN-US">Aiming at saving energy and maximizing the network life cycle, the multi-node cooperative image acquisition and compression technology in Wireless Multimedia Sensor Networks</span><span lang="EN-US">(</span><span lang="EN-US">WMSNs) is studied deeply. </span><span lang="EN-US">T</span><span lang="EN-US">he Minimum Energy Image Collection (MEIC) problem for multiple target domains in a certain period of time in the monitoring area is proposed, the integer linear programming for Minimum Energy Image Collection (MEIC) problem is described and proved to be NP complete; then combined with the features of image acquisition of camera node,<a name="_Hlk527549560"></a> the Local Camera Coordinative Energy-saving Strategy (LCCES) is proposed, and the performance of the Local Camera Coordinative Energy-saving Strategy (LCCES) is evaluated through a lot of simulation experiments; finally, the LBT-based Multi-node Cooperative Image Compression Scheme (LBT-MCIC) is proposed. The results show that this strategy can effectively reduce the number of active camera nodes in the process of image acquisition, thus reducing the energy consumption of image acquisition</span><span lang="EN-US">.</span><span lang="EN-US"> At the same time, it also plays a role in balancing the energy consumption of camera nodes in the network, effectively solves the problem of high cost of common nodes in the image transmission scheme of two-hop cluster structure and has the characteristics of low computational complexity and high quality of reconstructed image.</span>

scholarly journals Forest Fire Detection Using Wireless Multimedia Sensor Networks and Image Compression

2021 ◽  
Vol 20 (1) ◽  
pp. 57-63
Author(s):  
Fatima Bouakkaz ◽  
Wided Ali ◽  
Makhlouf Derdour
Keyword(s):  
Energy Consumption ◽  
Sensor Networks ◽  
Forest Fire ◽  
Forest Fires ◽  
Image Sensors ◽  
Wireless Multimedia Sensor Networks ◽  
Wireless Multimedia ◽  
Multimedia Sensor Networks ◽  
Scalar Data ◽  
Station Base

Recently, the issue of multimedia sensors received considerable critical attention, that led to the apparition of Wireless Multimedia Sensor Networks (WMSNs) WMSN that different from wireless sensor networks (WSN) by using multimedia sensors that can process video, audio, image data besides scalar data and send it to station base (SB). Multimedia data have a big volume bigger than scalar data and need more resources and consumed more energy. The ideal solution to solve the problems of WMSN (big volume, energy consumption) is data compression. Forest plays a critical role in our daily life we can summarize the importance of forests in human life. Among the most dangerous events the forest fires that happen because of natural or Man-made. Many methods used to detect forest fires the newest are: wireless multimedia sensor networks. Our system of detecting forest fire has been developed using a wireless multimedia senor network with two types of sensors (scalar, images). In the first phase when the scalar sensors detected a high temperature its announced alarm to activate the image sensors. In the second phase for detecting fire the image sensors, we used image processing tools. When the zone of fire in the image captured was detected the phase of compression started using the down sampling method. the final phase is transmission data to the station base using the grid chain transmission protocol technique, which allows a critical optimization of energy consumption. So, maximizing network life. The competence of the proposed system is achieved by minimizing size of image transmitted with grid chain routing protocol.

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