LOSSLESS DATA COMPRESSION FOR ENERGY EFFICIENT TRANSMISSION OVER WIRELESS NETWORK
Saurabh Mittal · 2012
Wireless networks are very fast replacing the traditional wired networking for the small and long distance area. With the rapid expansion in the data generation in the digital world and need for information sharing between the different stakeholders / collaborative members for business and scientific needs, today the volume of data being transmitted is also facing exponential growth. Wireless transmission of a single bit can require over 1000 times more energy than a single 32-bit computation. It can therefore be beneficial to perform additional computation to reduce the number of bits transmitted. If the energy required to compress data is less than the energy required to send it, there is a net energy savings and an increase in battery life for portable computers. This paper proposes energy savings possible by losslessly compressing data prior to transmission. The proposed work focuses that, with several typical compression algorithms, there is actually a net energy increase when compression is applied before transmission. Reasons for this increase are explained and proposal will be to avoid it. Key words: Compression, Lossless, Transmission, Wireless, Data. Wireless communication is an essential component of mobile computing, but the energy required for transmission of a single bit has been measured to be over 1000 times greater than for a single 32-bit computation. Thus, if 1000 computation operations can compress data by even 1 bit, energy should be saved. Compression algorithms which once seemed too resource or time-intensive might be valuable for saving energy. Implementations which made concessions in compression ratio to improve performance might be modified to provide an overall energy saving. Ideally, the effort exerted to compress data should be variable to allow a flexible tradeoff between speed and energy. Earlier work has considered lossy compression techniques which sacrifice the quality of compressed audio or video data streams to reduce the bit rate and energy required. In proposed work, we consider the challenge of reducing wireless communication energy for data that must be transmitted faithfully. We will provide detailed survey of the energy requirements of several lossless data compression schemes. Several families of compression algorithms are analyzed and characterized, and it is shown that compression prior to transmission may actually cause an overall energy increase. We will focus on behaviors and resource usage patterns which allow for energy-efficient lossless compression of data. When applied to Unix compress, these optimizations improve energy efficiency by 51%. We also explore the fact that, for many usage models, compression and decompression need not be performed by the same algorithm. By choosing the lowest-energy compressor and decompressor on the test platform, rather than using default levels of compression, overall energy to send compressible web data can be reduced 31%. Energy to send harder-to-compress English text can be reduced 57%. Compared with a system using a single optimized application for both compression and decompression, the asymmetric scheme saves 11% or 12% of the total energy depending on the dataset. Proposed work focuses on asymmetric compression i.e. the use of one compression algorithm on the transmit side and a different algorithm for the receive path.