Abstract
Wireless Sensor networks are battery powered due to which their lifetime is precisely limited. In this type of network the nodes commonly have very limited resources in terms of processing power, bandwidth and energy. Efficient coding of the multimedia content is therefore important. One possible way of achieve maximum utilization of those resource is applying compression on sensor event Usually, processing data consumes much less power than transmitting data in wireless medium, so it is effective to apply compression before transmitting data for reducing total power consumption by a sensor node. In this paper various energy efficient image compression techniques Collaborative image transmission using Sobal edge-detection, JPEG2000 image compression, Image Subtraction with Quantization of image; Adaptive Compression and Spatial Correlation-Based Image Compression are discussed.
Highlights
Advances in wireless communication have enabled the development of low-cost, low-power visual multihop wireless networks, which have recently emerged for a variety of applications, including environmental and habitat monitoring, target tracking, surveillance and emergency response [1]
A Wireless Sensor Network is a self-configuring network of small sensor nodes communicating among themselves using radio signals, and deployed in quantity to sense, monitor and understand the physical world
Energy consumption is one of the most important performance metrics for wireless sensor networks because it directly relates to the operational lifetime of the network
Summary
Advances in wireless communication have enabled the development of low-cost, low-power visual multihop wireless networks, which have recently emerged for a variety of applications, including environmental and habitat monitoring, target tracking, surveillance and emergency response [1]. If two devices cannot communicate directly, other nodes, located between those two nodes, will transmit a data packet from the source node to the destination node A node does not have sufficient computation power to completely compress a large raw image. In this case, a distributed method to share the processing task is required to overcome the computation power limitation of each single node. Even if nodes are not extremely computation powers constrained, but are battery operated; distributing the computation load of processing every raw image among otherwise idle processors of other nodes extends the overall lifetime of the network. In this paper some of image compression schemes designed for WSNs is presented and discussed
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