Implementation of Modified Huffman Coding in Wireless Sensor Networks

Abstract

WSNs are composed of many autonomous devices using sensors that are capable of interacting, processing information, and communicating wirelessly with their neighbors. Though there are many constraints in design of WSNs, the main limitation is the energy consumption. For most applications, the WSN is inaccessible or it is unfeasible to replace the batteries of the sensor nodes makes the network power inefficient. Because of this, the lifetime of the network which has maximum operational time is also reduces. To make the network power efficient, different power saving/reduction algorithms are proposed by different authors. Some of the authors have achieved the low power consumption by modifying like encryption & decryption algorithms, Routing algorithms, Energy Efficient Algorithms, Compression and decompression algorithms, minimizing control packets, and many other different power reduction algorithms. Among many algorithms, we have chosen data compression techniques aiming different targets like memory, power & bandwidth reduction. To achieve our objectives, we have worked on Huffman coding -compression algorithm and updated the algorithm by including one's complement, XOR operations and finally named as Modified Huffman Coding. The performance of the proposed model is analyzed and it is observed that, information is maximum compressed which consumes less computational power, thereby increasing the battery life.

CRSDF: Improved Network Lifespan through Chain-routing Scheme and Data Fusion in Wireless Sensor Network

“…Sadler et al [15] proposed the sensor Lempel Ziv Welch (S-LZW) algorithm based on the LZW algorithm for sensor systems by balancing three parameters: the dictionary size, and the data size for compression and processes with a full dictionary. In [4], Malleswari et al introduced the implementation of modified Huffman coding for WSN, where the Huffman algorithm is employed for the compression and decompression of data in order to minimize the size of the data packet and save energy.…”

“…A. Distributed Source Coding DSC based on the Slepian-Wolf theorem is one of the most efficient techniques, to compress correlated data sources [4,16]. In DSC, the correlated signals from a few sensor nodes are compressed with a totality rate greater than or equal to the joint entropy thus they decrease data packet size.…”

“…If the chain-based clustering routing method enormously reduces the distance communication among nodes in the network, then data aggregation will remove the data redundancy gathered from surrounds in various sensors to obtain more accurate and concise information. Furthermore, data pressure also is a suitable solution for maintaining battery energy by abating the number of bits to be transferred, however, the normal compression method as Lempel-Ziv; Huffman is difficult acceptance for sensor networks because they need a strong processor and large storage capability [4,5] for activities. Distributed Source Coding (DSC) technique [6,7], which is proposed by Slepian-Wolf, perform compresses the lossless data of two correlated sources employing side information.…”