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


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.

Wireless Sensor Network (WSN) comprises of several autonomous sensor nodes communicating with each other to perform a common task. A wireless sensor node consists of a processor, sensor, communication module powered by a battery. Power efficiency is considered to be a major issue in WSN, because efficient use of energy extends the network lifetime. Energy is consumed by the sensor node during sensing, processing and transmission. But almost 80% of the energy is spent in the communication module for data transmission in sensor network
Sensor networks have a wide range of
application in temperature monitoring, surveillance, bio
medical, precision agriculture. Failure of sensor node
causes a partition of the WSN resulting in critical
information loss. Hence there is great interest shown by
the many researchers in extending the lifetime of sensor
nodes by reducing the energy required for transmission.
Several algorithms have been proposed for energy
efficient wireless sensor network in literature.
The spatio-temporal correlations among sensor
observations are a significant and unique characteristic
of the WSN which can be exploited to drastically
increase the overall network performance. The
existence of the above mentioned correlation in sensor
data is exploited for the development of energy efficient
communication protocols well suited to WSN. Recently
there is a major interest in the Distributed Source
Compression (DSC) algorithm which utilizes the spatial
correlation in a sensor network for data compression. WSN application requires dense sensor deployment and as a result of this, multiple sensors record information about a single event. Therefore it is unnecessary for every sensor node to send redundant information to the sink node due to the existence of high spatial correlation. Instead a smaller number of sensor measurements might be adequate to communicate the information to the sink with certain reliability. In a distributed way of continuously exploiting existing correlations in sensor data based on adaptive signal processing and distributed source coding principles is discussed

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