XMill: an efficient compressor for XML data
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
An analysis of the Burrows—Wheeler transform
Journal of the ACM (JACM)
XGRIND: A Query-Friendly XML Compressor
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
Enabling Real-Time Querying of Live and Historical Stream Data
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
An analysis of XML compression efficiency
Proceedings of the 2007 workshop on Experimental computer science
Tiny Web Services for Sensor Device Interoperability
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
XML compression techniques: A survey and comparison
Journal of Computer and System Sciences
CGT Code-Based XML Data Compression Method
ISECS '09 Proceedings of the 2009 Second International Symposium on Electronic Commerce and Security - Volume 02
Mercury: a wearable sensor network platform for high-fidelity motion analysis
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
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With the broad applicability of wireless sensor networks across fields, it is desirable to develop self-describing sensor nodes that can operate in a plug-n-play manner. In this paper, we present MoteML, a metadata encoding suitable for storage on memory-constrained devices, designed in support of this goal. MoteML is consistent with Sensor Web Enablement's [23] Sensor Model Language (SensorML). More specifically, while MoteML does not conform to the SensorML schema, it can be translated into SensorML and vice-versa. This paper explores the available solutions for storing self-describing information on memory-constrained sensor nodes and presents the design of MoteML. MoteML is a text-based encoding that captures a subset of SensorML in a template-based structure. This text data is then compressed using available text compression techniques. The resulting file is small enough to be stored on a memory-constrained embedded device.