A Methodology for Designing Dynamic Topology Control Algorithms via Graph Transformation

Kluge, Roland; Varró, Gergely; Schürr, Andy

doi:10.1007/978-3-319-21155-8_15

Cited by 4 publications

(10 citation statements)

References 13 publications

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“…In [43], we focused on S1 by proposing a systematic approach for developing individual correct-by-construction TC algorithms. In this approach, valid topologies are characterized using graph constraints [33], TC algorithms are specified using programmed GT [23], and an existing constructive approach [33] is applied to enrich GT rules with application conditions derived from the graph constraints.…”

Section: Resultsmentioning

confidence: 99%

“…Correct-by-construction approaches have been extensively studied in the context of hardware design and software development processes (e.g., [6,7,56]). However, to the best of our knowledge, no work (apart from ours [42,43]) exists on constructing TC algorithms using the correct-by-construction paradigm. One technique for verifying correctness properties of software is model checking [60].…”

Section: Correctness Of Algorithmsmentioning

confidence: 99%

“…Previous work on S1 In [42,43], we showed that model-driven principles [10], as applied in many success stories [34,79], are suitable to address S1. We describe topologies as graphbased models and possible operations of topology control algorithms as declarative graph transformation (GT) rules [63].…”

mentioning

confidence: 99%

“…This paper has four major contributions: (i) We model commonalities and differences of TC algorithms by extracting common structural constraints and specifying the individual part of each TC algorithm in terms of individual attribute constraints. We lift all steps described in our original approach [43] to operate on abstract representations of TC algorithm families. (ii) To demonstrate the applicability of our approach, we re-engineer six existing TC algorithms (kTC [69], l-kTC [70,71], XTC [81], RNG [39], GG [80], Yao Graph [84]) and propose e-kTC, a novel, energy-aware variant of kTC [69] that has been inspired by the CTCA algorithm [15].…”

mentioning

confidence: 99%

“…Shortcomings in current development of topology control algorithms: gap between specification and implementation (S1) and missing reuse (S2) [ [42,43])…”

mentioning

confidence: 99%

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A systematic approach to constructing families of incremental topology control algorithms using graph transformation

et al. 2017

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Section: Resultsmentioning

confidence: 99%

Section: Correctness Of Algorithmsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

“…Shortcomings in current development of topology control algorithms: gap between specification and implementation (S1) and missing reuse (S2) [ [42,43])…”

mentioning

confidence: 99%

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A systematic approach to constructing families of incremental topology control algorithms using graph transformation

et al. 2017

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cMoflon: Model-Driven Generation of Embedded C Code for Wireless Sensor Networks

Kluge

Stein

Giessing

et al. 2017

Modelling Foundations and Applications

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A tale of two graph models: a case study in wireless sensor networks

2021

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Designing and reasoning about complex systems such as wireless sensor networks is hard due to highly dynamic environments: sensors are heterogeneous, battery-powered, and mobile. While formal modelling can provide rigorous mechanisms for design/reasoning, they are often viewed as difficult to use. Graph rewrite-based modelling techniques increase usability by providing an intuitive, flexible, and diagrammatic form of modelling in which graph-like structures express relationships between entities while rewriting mechanisms allow model evolution. Two major graph-based formalisms are Graph Transformation Systems (GTS) and Bigraphical Reactive Systems (BRS). While both use similar underlying structures, how they are employed in modelling is quite different. To gain a deeper understanding of GTS and BRS, and to guide future modelling, theory, and tool development, in this experience report we compare the practical modelling abilities and style of GTS and BRS when applied to topology control in WSNs. To show the value of the models, we describe how analysis may be performed in both formalisms. A comparison of the approaches shows that although the two formalisms are different, from both a theoretical and practical modelling standpoint, they are each successful in modelling topology control in WSNs. We found that GTS, while featuring a small set of entities and transformation rules, relied on entity attributes, rule application based on attribute/variable side-conditions, and imperative control flow units. BRS on the other hand, required a larger number of entities in order to both encode attributes directly in the model (via nesting) and provide tagging functionality that, when coupled with rule priorities, implements control flow. There remains promising research mapping techniques between the formalisms to further enable flexible and expressive modelling.

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A Methodology for Designing Dynamic Topology Control Algorithms via Graph Transformation

Cited by 4 publications

References 13 publications

A systematic approach to constructing families of incremental topology control algorithms using graph transformation

A systematic approach to constructing families of incremental topology control algorithms using graph transformation

cMoflon: Model-Driven Generation of Embedded C Code for Wireless Sensor Networks

A tale of two graph models: a case study in wireless sensor networks

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