This paper proposes an improved data compression
technique compared to existing Lempel-Ziv-Welch (LZW)
algorithm. LZW is a dictionary-updation based compression
technique which stores elements from the data in the form of
codes and uses them when those strings recur again. When the
dictionary gets full, every element in the dictionary are removed in
order to update dictionary with new entry. Therefore, the
conventional method doesn’t consider frequently used strings and
removes all the entry. This method is not an effective compression
when the data to be compressed are large and when there are
more frequently occurring string. This paper presents two new
methods which are an improvement for the existing LZW
compression algorithm. In this method, when the dictionary gets
full, the elements that haven’t been used earlier are removed
rather than removing every element of the dictionary which
happens in the existing LZW algorithm. This is achieved by
adding a flag to every element of the dictionary. Whenever an
element is used the flag is set high. Thus, when the dictionary gets
full, the dictionary entries where the flag was set high are kept
and others are discarded. In the first method, the entries are
discarded abruptly, whereas in the second method the unused
elements are removed once at a time. Therefore, the second
method gives enough time for the nascent elements of the
dictionary. These techniques all fetch similar results when data
set is small. This happens due to the fact that difference in the way
they handle the dictionary when it’s full. Thus these
improvements fetch better results only when a relatively large data
is used. When all the three techniques' models were used to
compare a data set with yields best case scenario, the compression
ratios of conventional LZW is small compared to improved LZW
method-1 and which in turn is small compared to improved LZW
method-2.
An Energy-Saving Routing Algorithm for Wireless Body Sensor Network using Data Compression Technique
