Data transmission is the most critical operation for mobile sensors networks in term of energywaste. Particularly in pervasive healthcare sensors network it is paramount to preserve the quality of servicealso by means of energy saving policies. Communication and data transmission are among the most criticaloperation for such devises in term of energy waste. In this paper we present a novel approach to increasebattery life-span by means of shorter transmission due to data compression. On the other hand, since thislatter operation has a non-neglectable energy cost, we developed a compression efficiency estimator basedon the evaluation of the absolute and relative entropy. Such algorithm provides us with a fast mean for theevaluation of data compressibility. Since mobile wireless sensor networks are prone to battery discharge-related problems, such an evaluation can be used to improve the electrical efficiency of data communication.In facts the developed technique, due to its independence from the string or file length, is extremelyrobust both for small and big data files, as well as to evaluate whether or not to compress data beforetransmission. Since the proposed solution provides a quantitative analysis of the source’s entropy and therelated statistics, it has been implemented as a preprocessing step before transmission. A dynamic thresholddefines whether or not to invoke a compression subroutine. Such a subroutine should be expected to greatlyreduce the transmission length. On the other hand a data compression algorithm should be used only whenthe energy gain of the reduced transmission time is presumably greater than the energy used to run thecompression software. In this paper we developed an automatic evaluation system in order to optimize thedata transmission in mobile sensor networks, by compressing data only when this action is presumed tobe energetically efficient. We tested the proposed algorithm by using the Canterbury Corpus as well asstandard pictorial data as benchmark test. The implemented system has been proven to be time-inexpensivewith respect to a compression algorithm. Finally the computational complexity of the proposed approach isvirtually neglectable with respect to the compression and transmission routines themselves.
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