Energy Efficient Data Management in Sensor Networks Sanjay K Madria Web and Wireless Computing Lab (W2C) Department of Computer Science, Missouri University of Science and Technology, Rolla, MO

Introduction Energy challenges in WSN motivates to devise algorithms which require least energy to make the sensors network last longer. Power consumption due to excessive wireless communication and computation, therefore, need to minimize both. Example: Data Aggregation helps in minimizing the number of wireless transmissions in a multi hop communication scenario. Counter Example: Secure data aggregation can drain energy saved by data aggregation and introduce delays and therefore, need better energy efficient solutions.

OBJECTIVE Adaptive watermarking-like techniques to provide confidentiality and integrity verification of high speed sensor data streams. Tailored towards energy efficiency to enhance lifetime, minimal computational overhead to enhance availability, while simultaneously being adaptive in order to meet application demands on desired security and compression levels. Homomorphic encryption and additive digital signature schemes (using public key cryptography) for providing confidentiality of sensor data during aggregation in WSNs Algorithm to allow aggregate encrypted data in Wireless Sensor Networks and a digital signature scheme to preserve data integrity.

Implementation on Two Platforms MICA2TELOSB OperationTime Taken (ms) Energy Consumed (mJ) Time Taken (ms) Energy Consumed (mJ) Encryption Sign Addition of ciphertext Addition of signatures Addition of Public keys

Energy Efficeint Compression for WSN: TinyPack Numeric - Temporal Locality

TinyPack Numeric Methods TP-Initial Static delta codes Fast, efficient, good compression ratio TP-Dynamic Frequencies Huffman style delta codes over window More processing and RAM, better compression TP-Running Statistics Approximate frequencies by data statistics Similar compression, less RAM

TinyPack Numeric Results (compared with two existing methods)

TinyPack XML Results (compared with three existing methods) PAQ requires prohibitive amounts of memory and time and is included as a benchmark

Decentralizing Compression Implementation for Real Time Sensor Networks Spatio-temporal correlation Data can be approximated to increase correlation Group sensors with similar data – Distinct grops Choose a base signal Transmit ratio signals (scalar multiple of base signal) Ratio signals have high compressibility Compressed Sensing

Decentralizing Compression Implementation for Real Time Sensor Networks Decentralization Define groups at sinks and cluster heads Choose base signal with leader selection Compression Lossless Compress ratio signals using TinyPack Lossy Set maximum tolerated error Compress with TinyPack and run length encoding