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首页> 外文期刊>International Journal of Monitoring and Surveillance Technologies Research >Optimization of Power Allocation in Multimedia Wireless Sensor Networks
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Optimization of Power Allocation in Multimedia Wireless Sensor Networks

机译:多媒体无线传感器网络中的功率分配优化

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Wireless Sensor Networks (WSN) have been widely applied in monitoring and surveillance fields in recent years and have dramatically changed the methodologies and technologies in monitoring and surveillance. However, the sensor nodes in WSN have very limited computing resources and power supply, and thus the maximization of network life is a very critical issue. In the newly-emerging Wireless Multimedia Sensor Network (WMSN), the high volume of sensed video data needs to be compressed before transmission. Different video coding schemes have been developed and applied to wireless multimedia sensor networks, and there exists a tradeoff between the power consumption of data compression and that of data transmission. Video compression will reduce the amount of data that needs to be transmitted and thus the amount of power consumed for data transmission; however, too much video compression will consume excessive power which outweighs the power savings on data transmission. Thus, how to reach an optimized balance between compression and transmission and maximize network life becomes a challenging research issue. In this paper, the authors propose mathematical models which describe power consumptions of data compression and transmission of sensor nodes in hexagon-shaped clusters. Under the proposed model, they have achieved the optimized data compression ratio which can minimize the overall power consumption of the whole cluster.
机译:近年来,无线传感器网络(WSN)已广泛应用于监视和监视领域,并且极大地改变了监视和监视的方法和技术。但是,WSN中的传感器节点具有非常有限的计算资源和电源,因此,网络寿命的最大化是一个非常关键的问题。在新兴的无线多媒体传感器网络(WMSN)中,需要在传输之前压缩大量感测到的视频数据。已经开发了不同的视频编码方案并将其应用于无线多媒体传感器网络,并且在数据压缩的功耗与数据传输的功耗之间存在折衷。视频压缩将减少需要传输的数据量,从而减少数据传输所消耗的功率;但是,过多的视频压缩将消耗过多的功率,这超过了数据传输所节省的功率。因此,如何在压缩和传输之间达到最佳平衡并最大程度地延长网络寿命成为具有挑战性的研究问题。在本文中,作者提出了数学模型,该模型描述了六边形簇中数据压缩和传感器节点传输的功耗。在提出的模型下,他们实现了优化的数据压缩率,可以使整个群集的总功耗降至最低。

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