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1 A New Data Compression Algorithm for Wireless Sensor Network using K-RLE S.B.Chattar *1, J.V.Chandgude *2, S.R.Dhawade *3, Prof.varsha Surwase*4 *1 (Student of ECE, Vidya Pratishthan s College of Engineering, Baramati, India) *2 (Student of ECE, Vidya Pratishthan s College of Engineering, Baramati, India) *3 (Student of ECE, Vidya Pratishthan s College of Engineering, Baramati, India) Chattersupriya11@gmail.com*1, chandgudejyoti@gmail.com*2, supriyadhawade10@gmail.com*3 Abstract Data compression is a technique used to save energy in Wireless Sensor Networks by reducing the quantity of data transmitted and the number of transmission. In this project, we propose and evaluate a new data compression algorithm inspired from Run Length Encoding called K-RLE which means RLE with a K-Precision. The K-RLE compression algorithm is basically used to compress the repeated unwanted data. This has implemented using ARM7 low cost embedded processor. To In this article, we want to introduce in-network processing technique in order to save energy. Innetwork 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 and evaluate a new data compression algorithm inspired from Run implement this project on the processor the external devices like 16X2 LCD, RESET circuit, RS-232 cable have interfaced to this controller. Length Encoding called K-RLE The choice of K influences the percentage of data modified for this lossy algorithm. While K-RLE is Keywords- Data Compression, K-RLE, Lossy, based on RLE, where the compression results depend VisualBasics6.0 Wsn, Zig-bee. 1. INTRODUCTION of the data source, in this work, we evaluate K-RLE with different statistics of data sources and different In the Wireless Sensor Network technology values of K. the two main elementary activities of Wireless Sensor 1.1 OVERVIEW OF K-RLE DATA Network are data acquisition and transmission. However, transmitting/receiving data are power consuming task. we explore data compression by processing information locally. The most common technique for saving energy is the use of sleep mode where significant parts of the sensor s transceiver is switched off. In most cases, the radio transceiver on board sensor nodes is the main cause of energy consumption: hence, it is important to keep the transceiver in switched off mode most of the time to save energy. Nevertheless, using the sleep mode reduces data transmission/reception rate and thus communication in the network. The question is how to keep the same data rate sent to the base station by COMPRESSION TECHNIQUE: A new data compression technique that is the K- RLE. In general the run length encoding algorithm is data compression algorithm and basic idea is, if a data item d occurs n consecutive times in the input stream, we replace the n occurrences with the single pair nd. In my proposed system, K-RLE algorithm is very efficient compression technique which decreases the ratio of compression when compared to traditional RLE compression technique. Where as in the proposed algorithm 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. reducing the number of transmission Published in 1

2 1.2 COMPRESSION USING LOW COST ARM 7 EMBEDDED PROCESSOR: In our system we worked on the temperature as a data. For that temperature sensor LM35 is used which is interfaced to ADC channel AD 0.1 of LPC2148. The LCD is interfaced to P0 port of the LPC2148 (P0.16 to P0.23) in 8 bit mode in which 8bit DATA will be sent to the LCD.To receive the data entered from the PC the UART communication is used. To establish that communication RS-232 cable has connected through MAX 232 IC to the P0.0, P0.1, P0.8, P0.9 pins of the LPC2148 controller. The MAX IC placed in this board is used to match the RS-232 voltage levels to TTL logic levels to establish the proper communication between PC and processor. The active RESET circuit has been designed to this processor is used to reset the total circuit whenever we want. The working of total circuit is as fallows when we switch on the power supply the 3.3v goes to processor internal operation & also for zigbee.the 5v for external peripherals like LCD, other devices. First In LCD the messages will be displayed like project name and after it displayed the locally generated temperature continuously. The data the compression will start in very next step and displays the compressed data at the receiver side through the Zig-bee module. At the receiver the data is displayed in Visual Basics 6.0.In Visual Basics 6.0 the compressed temperature is recorded and database is generated. After completion of all these operation observed on the hardware modules we can conclude that the compression ratio is go on increases as precision factor increases. Fig.1 Transmitter Fig.2 Receiver 2.RELATED WORK: For data compression a low cost ARM7 embedded processor is used. The development board having the LCD pin interfacing and all port pins. 2.1 ARM BOARD AND FEATURE OF LPC2148: 16-bit/32-bit ARM7TDMI-S microcontroller with real time emulation and embedded trace support. Tiny size and low power consumption. On chip oscillator operates with an external crystal from 1MHZ TO 25 MHZ. 8 kb to 40 kb of on-chip static RAM and 32 kb to 512 kb of on-chip flash memory. In System Programming/In Application programming (ISP/IAP) via on-chip boot loader software. Single flash sector or full Published in 2

3 chip erase in 400ms and programming of 256bytes in 1ms. USB 2.0 Full-speed compliant device controller with 2kB of endpoint RAM. One or two 10-bit ADCs provides a total of6/14 analog inputs with conversion time 2.44us And Single 10-bit DAC provides variable analog output. Two 32-bit timers/external event counters. Fig.3 ARM development board 2.2 XBEE /XBEE-PRO OEM RF MODULES: The XBee and XBee-PRO OEM RF Modules were engineered to meet IEEE standards and support the unique needs of low-cost, low-power wireless sensor networks. The modules require minimal power and provide reliable delivery of data between devices. Key feature: Long Range Data Integrity: Indoor/Urban: up to 100 (30 m) Outdoor line-of-sight: up to 300 (100 m) Transmit Power: 1 mw (0 dbm) Receiver Sensitivity: -92 dbm Advanced Networking & Security: Retries and Acknowledgements DSSS (Direct Sequence Spread Spectrum) Each direct sequence channels has over 65,000 unique network addresses available Source/Destination Addressing Unicast & Broadcast Communications Point-to-point, point to-multipoint and peerto-peer topologies supported Coordinator/End Device operations Easy-to-Use. ADC and I/O line support: Analog-to-digital conversion, Digital I/O Line Passing ISM (Industrial, Scientific & Medical) 2.4 GHz frequency band. Low power: Tx Current:45 ma Rx Current:50mA Power Down Current:<10uA Published in Fig.4 Zig-bee module 3. 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. 3.1 LOSSLESS COMPRESSION TECHNIQUE: 3

4 In lossless compression techniques, the original image/data can be perfectly recovered from the compressed (encoded) image/data. These are also called noiseless since they do not add noise to the signal (image/data).it is also known as entropy coding since it use statistics or decomposition techniques to eliminate or minimize redundancy. Lossless compression is used only for a few applications with stringent requirements such as medical imaging and sensor data processing, In our system we consider the basic lossless compression technique named as Run Length Encoding (RLE), RLE compression algorithm implemented the on low cost, low power tinny embedded systems (based on 8bit/16bit microcontrollers).in our research work we analyze and evaluate the performance of RLE compression algorithm for temperature data applications based on Visual Basics RUN LENGTH ENCODING: Run-Length Encoding (RLE) is a very simple form of data compression in which runs of data (that is, sequences in which the same data value occurs in many consecutive data elements) are stored as a single data value and count, rather than as the original run. 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: WWWWWWWWWWWWBWWWWWWWWWW WWBBBWWWWWWWWWWWWWWWWWW WWWWWBWWWWWWWWWWWWWW Apply the run-length encoding (RLE) data compression algorithm to the above hypothetical scan line, the encoded text is as follows: 12W1B12W3B24W1B14WInterpret this as twelve W's, one B, twelve W's, three B's, etc. The Idea behind this algorithm is, If a data item d occurs n consecutive times in the input data we replace the n occurrences with the single pair nd. Run-Length Encoding (RLE) is a basic compression algorithm. It is very useful in case of repetitive and slowly varying data items. This is most useful basic compression algorithm on data that contains many such runs: for example, relatively simple graphic images such as icons, line drawings, and grayscale images. Which is a lossless data compression algorithm used for slowly varying sensor and image data. It is not useful with files that don't have many runs as it could double the file size. Published in Fig.5 Flow Chart for Run Length Encoding 4

5 3.2 LOSSY COMPRESSION TECHNIQUE Lossy schemes provide much higher compression ratios than lossless schemes. Lossy schemes are widely used since the quality of the reconstructed images is adequate for most applications.by this scheme, the decompressed image is not identical to the original image, but reasonably close to it RUN LENGTH ENCODING WITH K- PRECISION: The idea behind this new proposed algorithm is this: let K be a number, a data item d or data between d+k and d-k occur n consecutive times in the input stream, replace the n occurrences with the single pair nd. We introduce a parameter K which is a precision. If K = 0, K-RLE is RLE. K has the same unit as the dataset values, in this case degree. Fig.6 Flow Chart for Run Length Encoding with K - Precision 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. The typical parameters, which are used to measure performance of the lossy image compression techniques / algorithms. Published in Compression Ratio is the ratio between the size of the compressed file and the size of the source file. Compression Factor is the inverse of the compression ratio. That is the ratio between the size of the source file and the size of the compressed file. Saving Percentage calculates the shrinkage of the source file as a percentage. SP = (size before compression size after compression) / size before compression. Fig.7 Schematic of ARM7 Processor with wireless sensor networks. 5

6 Fig.9 Photo shot of locally generated temperature. Fig.8 Development Board of ARM7 Processor with Wireless Sensor Networks 4. EXPERIMENTAL RESULTS: Fig.10 Visual Basic 6.0 form results Published in 6

7 Fig.11 Data compression Report in Visual Basic 6.0 work of this project is modifying algorithm for lossless compression with equal compression efficiency and low power consumption Performance Evaluation of Basic Compression Technique for Wireless Text Data, Special Issue of ICACSE Held on 7-8 January, 2013 in Lords Institute of Engineering and Technology, Hyderabad [4] Christopher M. Sadler and Margaret Martonosi, Data Compression Algorithms for Energy-Constrained Devices in Delay Tolerant Networks. Fig.12 Database results generated through VisualBasics6.0 in Microsoft Office Access. 5. CONCLUSION: The project presented a new compression algorithm for data compression. 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. 6. FUTURE WORK: This project implemented an efficient compression process. Since RLE doesn t have great compression ratio K-RLE uses less energy and high compression efficiency compared to RLE. Future 7. REFERENCES: [1] Eug`ene Pamba Capo-Chichi, Herv e Guyenne. K-RLE: A new Data Compression Algorithm for Wireless Sensor: 2009 Third International Conference on Sensor Technologies and Applications: June 2009, IEEE. [2] V.Krishnan, Mr. R.Trinadh, A Low Power New Data Compression Algorithm For Wire/Wireless Sensor Networks Using K-RLE. International Journal of Electronics Signals and Systems (IJESS), ISSN [3] V. Bhagya Raju, Dr. K. JayaSankar, Dr. C.D. Naidu. Published in 7