Context- and Template-Based Compression for Efficient Management of Data Models in Resource-Constrained Systems
AbstractThe Cyber Physical Systems (CPS) paradigm is based on the deployment of interconnected heterogeneous devices and systems, so interoperability is at the heart of any CPS architecture design. In this sense, the adoption of standard and generic data formats for data representation and communication, e.g., XML or JSON, effectively addresses the interoperability problem among heterogeneous systems. Nevertheless, the verbosity of those standard data formats usually demands system resources that might suppose an overload for the resource-constrained devices that are typically deployed in CPS. In this work we present Context- and Template-based Compression (CTC), a data compression approach targeted to resource-constrained devices, which allows reducing the resources needed to transmit, store and process data models. Additionally, we provide a benchmark evaluation and comparison with current implementations of the Efficient XML Interchange (EXI) processor, which is promoted by the World Wide Web Consortium (W3C), and it is the most prominent XML compression mechanism nowadays. Interestingly, the results from the evaluation show that CTC outperforms EXI implementations in terms of memory usage and speed, keeping similar compression rates. As a conclusion, CTC is shown to be a good candidate for managing standard data model representation formats in CPS composed of resource-constrained devices. View Full-Text
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Macho, J.B.; Montón, L.G.; Rodriguez, R.C. Context- and Template-Based Compression for Efficient Management of Data Models in Resource-Constrained Systems. Sensors 2017, 17, 1755.
Macho JB, Montón LG, Rodriguez RC. Context- and Template-Based Compression for Efficient Management of Data Models in Resource-Constrained Systems. Sensors. 2017; 17(8):1755.Chicago/Turabian Style
Macho, Jorge B.; Montón, Luis G.; Rodriguez, Roberto C. 2017. "Context- and Template-Based Compression for Efficient Management of Data Models in Resource-Constrained Systems." Sensors 17, no. 8: 1755.
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