SIMORA: SIMulating Open Routing protocols for Application interoperability on edge devices

Warnke, Benjamin; Sehgelmeble, Yuri Cotrado; Mantler, Johann; Groppe, Sven; Fischer, Stefan

doi:10.1109/icfec54809.2022.00013

Cited by 4 publications

(5 citation statements)

References 19 publications

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“…SIMORA [14] (source code available at https://github.com/luposdate3000/SIMORA (accessed on 20 August 2022) ) is used to simulate the DBMS in a reproducible randomized topology. This topology was chosen because it is the most realistic one.…”

Section: Discussionmentioning

confidence: 99%

“…However, the application needs to know the multicast routes beforehand. That is why we introduced our simulator SIMORA (SIMulating Open Routing protocols for Application interoperability on edge devices) [14] (source code available at https://github.com/luposdate3000/SIMORA (accessed on 20 August 2022)) in our previous work.…”

Section: Motivationmentioning

confidence: 99%

“…It is entirely written in Kotlin and can therefore be compiled to different targets, allowing it to be executed on various platforms. The database works with the simulator SIMORA [14] to gain further insight into semantic web databases. Luposdate3000 uses column iterators wherever possible when evaluating queries.…”

Section: Luposdate3000mentioning

confidence: 99%

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Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

Warnke,

Fischer,

Groppe

2023

Computers

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Due to increasing digitization, the amount of data in the Internet of Things (IoT) is constantly increasing. In order to be able to process queries efficiently, strategies must, therefore, be found to reduce the transmitted data as much as possible. SPARQL is particularly well-suited to the IoT environment because it can handle various data structures. Due to the flexibility of data structures, however, more data have to be joined again during processing. Therefore, a good join order is crucial as it significantly impacts the number of intermediate results. However, computing the best linking order is an NP-hard problem because the total number of possible linking orders increases exponentially with the number of inputs to be combined. In addition, there are different definitions of optimal join orders. Machine learning uses stochastic methods to achieve good results even with complex problems quickly. Other DBMSs also consider reducing network traffic but neglect the network topology. Network topology is crucial in IoT as devices are not evenly distributed. Therefore, we present new techniques for collaboration between routing, application, and machine learning. Our approach, which pushes the operators as close as possible to the data source, minimizes the produced network traffic by 10%. Additionally, the model can reduce the number of intermediate results by a factor of 100 in comparison to other state-of-the-art approaches.

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Section: Discussionmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

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Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

Warnke,

Fischer,

Groppe

2023

Computers

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“…Furthermore, scenarios such as those described with the IoT simulator of Warnke et al (cf. Github repository (https://github.com/luposdate3000/SIMORA, accessed on 14 September 2023) and [28]) can be tested. Among other things, the authors describe a smart parking scenario.…”

Section: Smart Parking Scenariomentioning

confidence: 99%

A Finite State Automaton for Green Data Validation in a Real-World Smart Manufacturing Environment with Special Regard to Time-Outs and Overtaking

Paasche,

Groppe

2023

Future Internet

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Since data are the gold of modern business, companies put a huge effort into collecting internal and external information, such as process, supply chain, or customer data. To leverage the full potential of gathered information, data have to be free of errors and corruptions. Thus, the impacts of data quality and data validation approaches become more and more relevant. At the same time, the impact of information and communication technologies has been increasing for several years. This leads to increasing energy consumption and the associated emission of climate-damaging gases such as carbon dioxide (CO2). Since these gases cause serious problems (e.g., climate change) and lead to climate targets not being met, it is a major goal for companies to become climate neutral. Our work focuses on quality aspects in smart manufacturing lines and presents a finite automaton to validate an incoming stream of manufacturing data. Through this process, we aim to achieve a sustainable use of manufacturing resources. In the course of this work, we aim to investigate possibilities to implement data validation in resource-saving ways. Our automaton enables the detection of errors in a continuous data stream and reports discrepancies directly. By making inconsistencies visible and annotating affected data sets, we are able to increase the overall data quality. Further, we build up a fast feedback loop, allowing us to quickly intervene and remove sources of interference. Through this fast feedback, we expect a lower consumption of material resources on the one hand because we can intervene in case of error and optimize our processes. On the other hand, our automaton decreases the immaterial resources needed, such as the required energy consumption for data validation, due to more efficient validation steps. We achieve the more efficient validation steps by the already-mentioned automaton structure. Furthermore, we reduce the response time through additional recognition of overtaking data records. In addition, we implement an improved check for complex inconsistencies. Our experimental results show that we are able to significantly reduce memory usage and thus decrease the energy consumption for our data validation task.

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“…In our previous paper [27] we introduced SIMORA. SIMORA is a network simulator with focus on the application layer, and how the application performs in a low power network.…”

Section: Related Workmentioning

confidence: 99%

A SPARQL benchmark for distributed databases in IoT environments

Warnke

Mantler

Groppe

et al. 2022

Proceedings of the International Workshop on Big Data in Emergent Distributed Environments

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Data transmission is one of the biggest energy consumers in low power networks. So, any application running in such environment must reduce the volume of communication caused by its presence. We want to deploy our database on a wireless sensor network. Therefore, we want to gain insights into the interoperability between routing and application in order to reduce communication costs. To do this, we propose our new benchmark framework in this contribution. Using this benchmark framework, we compare state-of-the-art database indexes and how much traffic they cause in a fully distributed environment. We believe that computing speed shouldn't be the only metric to compare databases with each other.

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SIMORA: SIMulating Open Routing protocols for Application interoperability on edge devices

Cited by 4 publications

References 19 publications

Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

Using Machine Learning and Routing Protocols for Optimizing Distributed SPARQL Queries in Collaboration

A Finite State Automaton for Green Data Validation in a Real-World Smart Manufacturing Environment with Special Regard to Time-Outs and Overtaking

A SPARQL benchmark for distributed databases in IoT environments

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