This project report presents a new data compression algorithm called K-RLE for use in wireless sensor networks. K-RLE is based on run-length encoding (RLE) but introduces a parameter K that allows runs of similar data values to be compressed together. This increases compression ratio compared to standard RLE. The project implements K-RLE compression in an FPGA with ADC, FIFOs and a compression controller block. Simulation results show K-RLE can achieve higher compression than RLE with lower hardware requirements and power consumption, making it suitable for wireless sensor networks where energy efficiency is important.

1. PROJECT REPORT ON
A NEW ALGORITHM (K-RLE )FOR DATA
COMPRESSION TECHNIQUE USING VLSI
AVR & SVR ENGINEERING COLLEGE
NANDYAL
UNDER THE GUIDANCE OF
Mr. M.MAHABOOB BASHA, M.Tech, (Ph D)
1

2. presented by……………
B.THEJESWARA REDDY (10AM1A04A9)
E.SURESH (10AM1A04A3)
T.SHABARISH (10AM1A0475)
C. RAMA KRISHNA (10AM1A0469)
N.SATYANARAYANA RAJU (102J1A0465)
S. SANTHOSH (10AM1A0493)
2
A NEW ALGORITHM
(K-RLE) FOR DATA
COMPRESSION
TECHNIQUE USING
VLSI

4. INTRODUCTION
 In this article, we want to introduce in-network
processing technique in order to save energy.
In-network processing techniques allow the
reduction of the amount of data to be
transmitted. The well known in-network
processing technique is data compression
and/or data aggregation. Data compression is
a process that reduces the amount of data in
order to reduce data transmitted and/or
decreases transfer time because the size of
the data is reduced.

5.  Recent technological breakthrough in low power processing units
and communication devices have enabled the development of distributed
autonomous nodes able to sense environmental data, compute and
transmit it using wireless communication to a base station known as Sink
for future analysis; thus, forming a Wireless Sensor Network . However,
Wireless Sensor Network is driven by a severe constraint which is power
management. This power management has led researchers to explore
scheduling sensor states. Scheduling sensor states is a technique that
decides which sensor may change its state (transmit, receive, idle, Sleep),
according to the current and anticipated communications needs.
 The most common technique for saving energy is the use of sleep
mode where significant parts of the sensor’s transceiver are switched off.
The question is how to keep the same data rate sent to the base station by
reducing the number of transmission.

6.  In computer science and information theory, data
compression or source coding is the process of
encoding information using fewer bits (or other
information-bearing units) than an un encoded
representation would use, through use of specific
encoding schemes.
 To reduce the volume of data to be transmitted
(text, fax, images) To reduce the bandwidth
required for transmission and to reduce storage
requirements (speech, audio, video) To reduce
power consumption (saving energy) Reduces data
transfer time keeping same data rate.

7. DATA COMPRESSION
ALGORITHMS
 Compression is useful because it helps reduce the
consumption of expensive resources, such as hard disk
space or transmission bandwidth On the downside,
compressed data must be decompressed to be used, and this
extra processing may be detrimental to some applications.
For instance, a compression scheme for video may require
expensive hardware for the video to be decompressed fast
enough to be viewed as it is being decompressed (the option
of decompressing the video in full before watching it may be
inconvenient, and requires storage space for the
decompressed video). The design of data compression
schemes therefore involves trade-offs among various
factors, including the degree of compression, the amount of
distortion introduced (if using a lossy compression scheme,
and the computational resources required to compress and
uncompress the data.

8. LOSSY COMPRESSION
 Lossy image compression is used in digital cameras, to
increase storage capacities with minimal degradation of
picture quality. Similarly, DVDs use the lossy MPEG-2
Video codec for video compression.
 In lossy audio compression, methods of
psychoacoustics are used to remove non-audible (or
less audible) components of the signal. Compression of
human speech is often performed with even more
specialized techniques, so that "speech compression" or
"voice coding" is sometimes distinguished as a separate
discipline from "audio compression". Different audio and
speech compression standards are listed under audio
codecs. Voice compression is used in Internet telephony
for example, while audio compression is used for CD
ripping and is decoded by audio players

9. LOSSLESS COMPRESSION
Lossless compression algorithms usually exploit
statistical redundancy in such a way as to represent
the sender's data more concisely without error.
Lossless compression is possible because most real-
world data has statistical redundancy. For example,
in English text, the letter 'e' is much more common
than the letter 'z', and the probability that the letter
'q' will be followed by the letter 'z' is very small.

10. LOSSLESS VERSUS LOSSY
COMPRESSION
 Lossless compression algorithms usually exploit
statistical redundancy in such a way as to represent the
sender's data more concisely without error. Lossless
compression is possible because most real-world data has
statistical redundancy. For example, in English text, the letter 'e'
is much more common than the letter 'z', and the probability
that the letter 'q' will be followed by the letter 'z' is very small.
Another kind of compression, called lossy data compression or
perceptual coding, is possible if some loss of fidelity is
acceptable. Generally, a lossy data compression will be guided
by research on how people perceive the data in question.
For example, the human eye is more sensitive to
subtle variations in luminance than it is to variations in color.
JPEG image compression works in part by "rounding off" some
of this less-important information. Lossy data compression
provides a way to obtain the best fidelity for a given amount of
compression. In some cases, transparent (unnoticeable)
compression is desired

11.  Run-length encoding (RLE) If a data item d occurs n
consecutive times in the input stream, we replace the n
occurrences with the single pair nd .
 For example, consider a screen containing plain black text
on a solid white background. There will be many long runs of
white pixels in the blank space, and many short runs of black
pixels within the text. Let us take a hypothetical single scan
line, with B representing a black pixel and W representing
white:
 WWWWWWWWWWWWBWWWWWWWWWWWWBBBW
WWWWWWWWWWWWWWWWWWWWWWWBWWWW
WWWWWWWWWW
 If we apply the run-length encoding (RLE) data compression
algorithm to the above hypothetical scan line, we get the
following:
 12W1B12W3B24W1B14W

12. OVERVIEW OF K-RLE DATA
COMPRESSION TECHNIQUE
 In this thesis implemented a new data compression
technique that is the K-RLE data compression
technique, In general the run length encoding algorithm
is data compression algorithm and basic idea of this: If
a data item d occurs n consecutive times in the input
stream, we replace the n occurrences with the single
pair nd and K-RLE algorithm is very efficient
compression technique which decreases the ratio of
compression when compared to traditional RLE
compression technique. the behind the new algorithm
islet K be a number, If a data item d or data between
d+K and d-K occur n consecutive times in the input
stream, we replace the n occurrences with the single
pair nd.

13. K-RUN-LEGTH-ENCODING ALGORITHM
FOR DATA COMPRESSION
 The idea behind this new algorithm is this: let K be a number, If a data item
d or data between d+K and d-K occur n consecutive times in the input
stream, we replace the n occurrences with the single pair nd.
 We introduce a parameter K which is a precision. K is defined as:
δ= σ/ K
 With a minimum estimate of the Allan standard deviation i.e. σ is a
representative of the
 instrument measurement noise below which the precision is no longer
significant.
 If K = 0, K-RLE is RLE. K has the same unit as the dataset values, in this case
degree. However, the change on RLE using the K-precision introduces data
modified. Indeed, while RLE is a lossless compression algorithm K-RLE is a
lossy compression algorithm.
 This algorithm is lossless at the user level because it chooses K considering
that there is no difference between the data item d, d+K or d-K according to
the application.

14. Sen0
Sen1
Sen2
Sen3
Sen4
Sen5
Sen6
Sen7
Ch0[7:0]
Ch7[7:0]
ADCMUX
ADCFIFO
32x8
ADCData[7:0]
Clk
ChSel[2:0]
ChCntr CntOvr ScanCntr
ScanOvr
ScanEna
SampleEna
Rst
ADCEna
FIFOWr
FIFORd
FIFOEna
WrClk
RdClk
Rst
Dreg1 Dreg2
CountDataVal
Compress Controller
KRLECompressor Block
DataI[7:0]
ComFIFO
32x8
WrClk
RdClk
Rst
DataO[7:0]
SMC

15. ADC MULTIPLEXER

18. FIFO SCHEMATIC

19. FIFO SIMULATION WAVEFORM

24. SIMULATION WAVEFORM FOR K-RLE
COMPRESSOR (PART-1)

27. CONCLUSION
 The project presented a new compression algorithm for
data compression and this data compression algorithm
is a LOW POWER COMPRESSION ALGORITHM. K-
RLE is a lossy compression algorithm it is lossless at the
user level, because it chooses K considering that there
is no difference between the data item d, d+k, d-k .
 This algorithm inspired from RLE named K-RLE which
increases the compression ratio compared to RLE. For
K=2 this algorithm increases the ratio by 40% compared
to RLE. With this approach a fast transmission of data
with MINIMUM HARDWARE REQUIREMENT and less
power consumption is possible.

28.  This project implemented an efficient
compression processes. Since RLE doesn’t
have great compression ratio K-RLE uses less
energy and high compression efficiency
compared to RLE
 Future work of this project is modifying
algorithm for LOSSLESS COMPRESSION with
equal compression efficiency and low power
consumption.

29. REFERENCES
 [1] Yang-Kiefer algorithms for data
compression En-hui ,Yang Da-ke He
 [2] A Generalized Method for Encoding
and Decoding Run-Length-Limited Binary
Sequences G. F. M. BEENKER AND K. A.
SCHOUHAMER IMMINK
 [3] N. Kimura and S. Latifi. A survey on
data compression in wireless sensor
networks. In Information Technology:
Coding and Computing,
 [4] F. Marcelloni and M. Vecchio. A simple
algorithm for data compression in wireless
sensor networks. Communications Letters,
IEEE, 12(6):411–413, June 2008.
 [5] Croce, Silvia, Marcelloni, Francesco,
Vecchio, and Massimo. Reducing power
consumption in wireless sensor networks
using a novel approach to data
aggregation. Computer Journal,
51(2):227–239, March 2008.

30. That’s all about
MY PROJECT …!!!
Thank you and queries…!!!