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Maestro: an orchestration framework for large scale WSN simulations
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-6248-6067
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-0069-640X
2014 (English)In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 3, p. 5392-5414Article in journal (Refereed) Published
Abstract [en]

Contemporary wireless sensor networks (WSNs) have evolved into large and complex systems and are one of the main technologies used in cyber-physical systems and the Internet of Things. Extensive research on WSNs has led to the development of diverse solutions at all levels of software architecture, including protocol stacks for communications. This multitude of solutions is due to the limited computational power and restrictions on energy consumption that must be accounted for when designing typical WSN systems. It is therefore challenging to develop, test and validate even small WSN applications, and this process can easily consume significant resources. Simulations are inexpensive tools for testing, verifying and generally experimenting with new technologies in a repeatable fashion. Consequently, as the size of the systems to be tested increases, so does the need for large-scale simulations. This article describes a tool called Maestro for the automation of large-scale simulation and investigates the feasibility of using cloud computing facilities for such task. Using tools that are built into Maestro, we demonstrate a feasible approach for benchmarking cloud infrastructure in order to identify cloud Virtual Machine (VM)instances that provide an optimal balance of performance and cost for a given simulation.

Place, publisher, year, edition, pages
2014. Vol. 14, no 3, p. 5392-5414
National Category
Computer Sciences
Research subject
Dependable Communication and Computation Systems
Identifiers
URN: urn:nbn:se:ltu:diva-13128DOI: 10.3390/s140305392ISI: 000336783300081PubMedID: 24647123Scopus ID: 2-s2.0-84896441869Local ID: c4d5afc7-aed5-4840-96ae-05cd0429aca8OAI: oai:DiVA.org:ltu-13128DiVA, id: diva2:986079
Note
Validerad; 2014; 20131127 (lauril)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Publisher's full textPubMedScopushttp://www.mdpi.com/1424-8220/14/3/5392

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Riliskis, LaurynasOsipov, Evgeny

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