A very fast algorithm for RAM compression
ACM SIGOPS Operating Systems Review
Wireless integrated network sensors
Communications of the ACM
Code compression for low power embedded system design
Proceedings of the 37th Annual Design Automation Conference
MANTIS: system support for multimodAl NeTworks of in-situ sensors
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
Analyzing and modeling encryption overhead for sensor network nodes
WSNA '03 Proceedings of the 2nd ACM international conference on Wireless sensor networks and applications
LLVM: A Compilation Framework for Lifelong Program Analysis & Transformation
Proceedings of the international symposium on Code generation and optimization: feedback-directed and runtime optimization
Distributed regression: an efficient framework for modeling sensor network data
Proceedings of the 3rd international symposium on Information processing in sensor networks
Analysis of wireless sensor networks for habitat monitoring
Wireless sensor networks
Memory overflow protection for embedded systems using run-time checks, reuse and compression
Proceedings of the 2004 international conference on Compilers, architecture, and synthesis for embedded systems
Synopsis diffusion for robust aggregation in sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Optimization of in-network data reduction
DMSN '04 Proceeedings of the 1st international workshop on Data management for sensor networks: in conjunction with VLDB 2004
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
Software design patterns for TinyOS
LCTES '05 Proceedings of the 2005 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
CRAMES: compressed RAM for embedded systems
CODES+ISSS '05 Proceedings of the 3rd IEEE/ACM/IFIP international conference on Hardware/software codesign and system synthesis
High-performance operating system controlled memory compression
Proceedings of the 43rd annual Design Automation Conference
The case for compressed caching in virtual memory systems
ATEC '99 Proceedings of the annual conference on USENIX Annual Technical Conference
Query Processing in Sensor Networks
IEEE Pervasive Computing
IBM memory expansion technology (MXT)
IBM Journal of Research and Development
Offline compression for on-chip ram
Proceedings of the 2007 ACM SIGPLAN conference on Programming language design and implementation
Quasistatic shared libraries and XIP for memory footprint reduction in MMU-less embedded systems
ACM Transactions on Embedded Computing Systems (TECS)
Eliminating the call stack to save RAM
Proceedings of the 2009 ACM SIGPLAN/SIGBED conference on Languages, compilers, and tools for embedded systems
Enix: a lightweight dynamic operating system for tightly constrained wireless sensor platforms
Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems
Nucleos: a runtime system for ultra-compact wireless sensor nodes
EMSOFT '10 Proceedings of the tenth ACM international conference on Embedded software
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Random access memory (RAM) is tightly-constrained in many embedded systems. This is especially true for the least expensive, lowest-power embedded systems, such as sensor network nodes and portable consumer electronics. The most widely-used sensor network nodes have only 4-10 KB of RAM and do not contain memory management units (MMUs). It is very difficult to implement increasingly complex applications under such tight memory constraints. Nonetheless, price and power consumption constraints make it unlikely that increases in RAM in these systems will keep pace with the requirements of applications.We propose the use of automated compile-time and run-time techniques to increase the amount of usable memory in MMU-less embedded systems. The proposed techniques do not increase hardware cost, and are designed to require few or no changes to existing applications. We have developed a fast compression algorithm well suited to this application, as well as run-time library routines and compiler transformations to control and optimize the automatic migration of application data between compressed and uncompressed memory regions. These techniques were experimentally evaluated on Crossbow TelosB sensor network nodes running a number of data collection and signal processing applications. The results indicate that available memory can be increased by up to 50% with less than 10% performance degradation for most benchmarks.