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Design of Modified Adaptive Huffman Data Compression Algorithm for Wireless Sensor Network

机译:无线传感器网络的改进自适应霍夫曼数据压缩算法设计

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Problem statement: Efficient utilization of energy has been a core area of research in wireless sensor networks. Sensor nodes deployed in a network are battery operated. As batteries cannot be recharged frequently in the field setting, energy optimization becomes paramount in prolonging the battery-life and, consequently, the network lifetime. The communication module utilizes a major part of the energy expenditure of a sensor node. Hence data compression methods to reduce the number of bits to be transmitted by the communication module will significantly reduce the energy requirement and increase the lifetime of the sensor node. The present objective of the study contracted with the designing of efficient data compression algorithm, specifically suited to wireless sensor network. Approach: In this investigation, the natural correlation in a typical wireless sensor network data was exploited and a modified Huffman algorithm suited to wireless sensor network was designed. Results: The performance of the modified adaptive Huffman algorithm was analyzed and compared with the static and adaptive Huffman algorithm. The results indicated better compression ratio. Conclusion: Hence the proposed algorithm outperformed both static and adaptive Huffman algorithms, in terms of compression ratio and was well suited to embedding in sensor nodes for compressed data communication.
机译:问题陈述:能源的有效利用已成为无线传感器网络研究的核心领域。网络中部署的传感器节点由电池供电。由于在现场无法对电池进行频繁充电,因此能源优化对于延长电池寿命以及网络寿命至关重要。通信模块利用了传感器节点能量消耗的主要部分。因此,减少通信模块要发送的位数的数据压缩方法将显着降低能量需求并增加传感器节点的寿命。本研究的当前目标与高效数据压缩算法的设计相吻合,特别适合于无线传感器网络。方法:在这项研究中,利用了典型无线传感器网络数据中的自然相关性,并设计了一种适合无线传感器网络的改进型霍夫曼算法。结果:分析了改进的自适应霍夫曼算法的性能,并与静态和自适应霍夫曼算法进行了比较。结果表明更好的压缩比。结论:因此,在压缩率方面,所提出的算法优于静态和自适应霍夫曼算法,并且非常适合嵌入在传感器节点中以进行压缩数据通信。

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