Real Time Delta Huffman Compression in Sensor Networks

1. Real Time Delta Huffman Compression in Sensor Networks Students: Brett Geren1, Brandon Mikel2 Mentors: Dr. Sanjay Madria2, Tommy Szalapski2 1Hendrix College, 2Missouri University of Science and Technology MATERIALS AND METHODS RESULTS WHAT IS DELTA HUFFMAN ENCODING? • Materials: • A Windows 7 machine using Ubuntu 9 as a virtual machine, equipped with NesC and PowerTossim • Preliminary Procedures: • Modified existing code written by Dylan McDonald (Missouri S&T) to be modular enough to transmit data from senor node to base station using a variety of compression algorithms. • Procedure: • 1)Using a data stream from the movement patterns of 30 burrowing creatures, mimic in a simulation each independent data set as a different sensor mote sensing and sending the data. • 2)Using our simulation system, switch out the compression algorithms for what type of compression is intended. • 3) An automated data analysis is done, giving the amount of power consumption and both simulation time and real-world implementation time. • Using the data acquired in this fashion, we plotted out graphed comparisons between both TinyPack and another algorithm, SLZW. • Delta Huffman Encoding • The process of translating a data packet into values representing change from a predetermined median value, which are then represented as a Huffman diagram, then sent toward the base station. Real Time Compression • Rather than do all the compression at the base station, like many sensor network compression algorithms, with our algorithm, nicknamed TinyPack, compression happens in real time on the senor motes.. • Advantages • Minimizes costly radio transmissions over general processer use • Disadvantages • Works quite well with only certain data sets. Approximation for real-world power consumption PURPOSE The purpose of this project was to determine the advantages of our compression algorithm over similar algorithms in the field of sensor networks. MICA2 MOTES Percent space saved by the various compression algorithms • Specifications: • 2.4 GHz Processor • -16 MIPS • 128 KB Flash • 4 KB SRAM • 4 KB EEPROM • Radio transceiver • -Operates from 868-870 MHz and 902-928 MHz • -Range of up to 30 meters indoors • Energy consumption- Uses two ‘AA’ batteries • -Up to one year of battery life with sleep mode enabled • 3 Diagnostic LEDS • 51 pin connector for an external sensor board CONCLUSIONS • Compared to a simliarly-scoped algoirthm, TinyPack performs very well • Energy consumption is highly minimzed with our algorithm • Data compression is largely increased per payload APPLICATION OF RESULTS • TinyPack could be used for a variety of applications • Any data set that has very minimal variance from timestep to timestep • Any sensor networks where power usage needs to be minimized and there’s far too much data transmission going on Simulation flow