Abstract
The 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.
Highlights
The trend to integrate heterogeneous systems and devices into Cyber Physical Systems (CPS)demands interoperable communications and data models
Results show that Context- and Template-based Compression (CTC) has a very similar compression size performance compared to Efficient XML Interchange (EXI) and an average better performance if we take into account the EXI header
This is a direct result from using the simpler CTC approach and structure of the Context Tables and Template Tables compared to the EXI specification and EXI Processor (EXIP) implementation
Summary
The trend to integrate heterogeneous systems and devices into Cyber Physical Systems (CPS). The efficient management of standard data model representation formats would ease the native use of high level data models and protocols (such as web services) in resource-constrained devices. XML documents too large and too CPU demanding to be efficiently managed by resource-constrained devices. XML parsers need to deal with large amounts of string data and verbose documents, involving too much processing for energy- and processor-constrained devices. Resource-constrained devices usually have small memories of tens of KBytes, which should be able to store the XML document(s), as well as the application, operating system, communication libraries, etc. The purpose of CTC is to reduce the resources needed to transmit, store and process data models compared to using the standard data representation formats (such as XML or JSON). Results show that CTC outperforms other solutions and is a valid candidate for data model management in resource-constrained devices and networks.
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