A low-overhead adaptive image compression technique for energy-constrained WMSN

Pal, Tamal; Bit, Sipra Das

doi:10.1016/j.compeleceng.2017.09.006

Cited by 4 publications

(1 citation statement)

References 14 publications

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“…In addition, energy efficiency has been studied extensively in sensor networks [44], [45], [9], [46], [16]. [31] focuses on the energy-aware image transmission in WMSN for flood images. In this paper, the researchers applied image segmentation and the useless parts of the images were removed before transmission.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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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