A4WSN: an architecture-driven modelling platform for analysing and developing WSNs

• Published in: Software and systems modeling 2019-08, Vol.18 (4), p.2633-2653
• Main Authors: Malavolta, Ivano, Mostarda, Leonardo, Muccini, Henry, Ever, Enver, Doddapaneni, Krishna, Gemikonakli, Orhan
• Format: Article
• Language: English
• Subjects: Compilers; Computer Science; Configurations; Cooperation; Distributed sensor systems; Hardware; Information Systems Applications (incl.Internet); Interpreters; IT in Business; Life cycle analysis; Life cycle engineering; Modelling; Programming Languages; Programming Techniques; Quality assessment; Quality management; Regular Paper; Remote sensors; Sensors; Software Engineering; Software Engineering/Programming and Operating Systems; Software reuse; Wireless sensor networks
• ISSN: 1619-1366; 1619-1374
• DOI: 10.1007/s10270-018-0687-0

This paper proposes A4WSN, an architecture-driven modelling platform for the development and the analysis of wireless sensor networks (WSNs). A WSN consists of spatially distributed sensor nodes that cooperate in order to accomplish a specific task. Sensor nodes are cheap, small, and battery-powered devices with limited processing capabilities and memory. WSNs are mostly developed directly on the top of the operating system. They are tied to the hardware configuration of the sensor nodes, and their design and implementation can require cooperation with a myriad of system stakeholders with different backgrounds. The peculiarities of WSNs and current development practices bring a number of challenges, ranging from hardware and software coupling, limited reuse, and the late assessment of WSN quality properties. As a way to overcome a number of existing limitations, this study presents a multi-view modelling approach that supports the development and analysis of WSNs. The framework uses different models to describe the software architecture, hardware configuration, and physical deployment of a WSN. A4WSN allows engineers to perform analysis and code generation in earlier stages of the WSN development life cycle. The A4WSN platform can be extended with third-party plug-ins providing additional analysis or code generation engines. We provide evidence of the applicability of the proposed platform by developing PlaceLife, an A4WSN plug-in for estimating the WSN lifetime by taking various physical obstacles in the deployment environment into account. In turn, PlaceLife has been applied to a real-world case study in the health care domain as a running example.