Design and analysis of dynamic Huffman codes
Journal of the ACM (JACM)
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
TOSSIM: accurate and scalable simulation of entire TinyOS applications
Proceedings of the 1st international conference on Embedded networked sensor systems
Simulating the power consumption of large-scale sensor network applications
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Hardware design experiences in ZebraNet
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Balancing energy efficiency and quality of aggregate data in sensor networks
The VLDB Journal — The International Journal on Very Large Data Bases
TAG: a Tiny AGgregation service for Ad-Hoc sensor networks
OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Data compression algorithms for energy-constrained devices in delay tolerant networks
Proceedings of the 4th international conference on Embedded networked sensor systems
GAMPS: compressing multi sensor data by grouping and amplitude scaling
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
N-CET: network-centric exploitation and tracking
MILCOM'09 Proceedings of the 28th IEEE conference on Military communications
A universal algorithm for sequential data compression
IEEE Transactions on Information Theory
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Wireless sensor networks possess significant limitations in storage, bandwidth, processing, and energy. Additionally, real-time sensor network applications such as monitoring poisonous gas leaks cannot tolerate high latency. While some good data compression algorithms exist specific to sensor networks, in this paper we present TinyPack, a suite of energy-efficient methods with high-compression ratios that reduce latency, storage, and bandwidth usage further in comparison with some other recently proposed algorithms. Our Huffman style compression schemes exploit temporal locality and delta compression to provide better bandwidth utilization important in the wireless sensor network, thus reducing latency for real time sensor-based monitoring applications. Our performance evaluations over many different real data sets using a simulation platform as well as a hardware implementation show comparable compression ratios and energy savings with a significant decrease in latency compared to some other existing approaches. We have also discussed robust error correction and recovery methods to address packet loss and corruption common in sensor network environments.