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

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.

2020 ◽  
Vol 17 (2) ◽  
pp. 509-536
Author(s):  
Arafat Senturk ◽  
Resul Kara ◽  
Ibrahim Ozcelik

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).


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