Simonstrator: Simulation and Prototyping Platform for Distributed Mobile Applications

Richerzhagen, Björn; Stingl, Dominik; Rückert, Julius; Steinmetz, Ralf

doi:10.4108/eai.24-8-2015.2261064

Cited by 49 publications

(19 citation statements)

References 21 publications

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“…We plan to use the SIMONSTRATOR [61] platform to evaluate the localized, parallel GTbased specification, which results from answering the first two questions, and we plan to use the Contiki operating system [21] as target platform for generating embedded C code (Step 9).…”

Section: Resultsmentioning

confidence: 99%

“…In this section, we present a comparative simulation-based evaluation study that serves as a proof-of-concept for our integration of the GT tool EMOFLON [47] and the network simulator SIMONSTRATOR [61]. In Section 6.1, we explain the research questions of this evaluation.…”

Section: Discussionmentioning

confidence: 99%

“…Following best practices for simulation studies [35,46], we rigorously document the evaluation setup to foster reproducibility of our results. The technical platforms of this evaluation are the GT tool EMOFLON [47] and the network evaluation platform SIMONSTRATOR [61] with its contained network simulator PEERFACTSIM.KOM [73], as shown in Figure 26 and as described in detail in the following. A SHARE virtual machine [29] is available for exploring our tool integration.…”

Section: Evaluation Setupmentioning

confidence: 99%

“…On the one hand, the specification of a TC algorithm often builds on a mathematically well-founded framework (e.g., graph or game theory), which allows the TC developer to prove formal properties. On the other hand, the implementation of a TC algorithm is typically written in a general-purpose programming language (e.g., Java for simulation [61] or C for testbed evaluation [21]). Additionally, the TC algorithm and the runtime environment (often continuously) interact: The TC algorithm activates and inactivates links in the topology, and the runtime environment causes context events (e.g., node removals due to battery depletion).…”

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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: Discussionmentioning

confidence: 99%

Section: Evaluation Setupmentioning

confidence: 99%

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confidence: 99%

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

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“…For this evaluation, we use the GT tool EMOFLON [23] to automatically transform the specification of kTC into executable Java code. For simulating WSNs, we use the network simulation platform SIMONSTRATOR [61].…”

Section: Simulation-based Evaluationmentioning

confidence: 99%

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

Kluge

Stein²,

Varr

et al. 2017

Journal of Visual Languages & Computing

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This document corresponds to the accepted manuscript of the article Kluge, R., Stein, M., Varró, G., Schürr, A., Hollick, M., Mühlhäuser, M.: "A Systematic Approach to Constructing Incremental Topology Control Algorithms using Graph Transformation," in: JVLC 2016. The URL of the formal version is https://dx.Communication networks form the backbone of our society. Topology control algorithms optimize the topology of such communication networks. Due to the importance of communication networks, a topology control algorithm should guarantee certain required consistency properties (e.g., connectivity of the topology), while achieving desired optimization properties (e.g., a bounded number of neighbors). Real-world topologies are dynamic (e.g., because nodes join, leave, or move within the network), which requires topology control algorithms to operate in an incremental way, i.e., based on the recently introduced modifications of a topology. Visual programming and specification languages are a proven means for specifying the structure as well as consistency and optimization properties of topologies. In this paper, we present a novel methodology, based on a visual graph transformation and graph constraint language, for developing incremental topology control algorithms that are guaranteed to fulfill a set of specified consistency and optimization constraints. More specifically, we model the possible modifications of a topology control algorithm and the environment using graph transformation rules, and we describe consistency and optimization properties using graph constraints. On this basis, we apply and extend a well-known constructive approach to derive refined graph transformation rules that preserve these graph constraints. We apply our methodology to re-engineer an established topology control algorithm, kTC, and evaluate it in a network simulation study to show the practical applicability of our approach. Meta-modeling [Sec. 2] WSN meta-model Topology control (TC) Graph constraints [Sec. 3] Structural constraints TC constraints Connectivity Consistency Graph transformation [Sec. 4] TC rules TC algorithm Context event rules Rule refinement [Sec. 5] Rule refinement algorithm Restoring rule applicability Enforcing termination Proving preservation of connectivity Evaluation [Sec. 6] RQ1: Correctness RQ2: Incrementality RQ3: Generalizability RQ4: Performance Figure 1: Overview of the proposed constructive methodology and structure of this paper (i) We characterize the required and desired properties of output topologies of a TC algorithm using graph constraints (as described in [42]).(ii) We use GT rules to describe TC operations, which specify the possible modifications during an execution of a TC algorithm, and context events, which specify the possible modifications by the environment.(iii) We apply and enhance the constructive refinement approach for GT rules presented in [42,43] to TC. Our approach is able to cope with temporarily constraint-violating rules, which contrary to [42,43] may not be restricted.(iv) We ...

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Stigmergic Service Composition and Adaptation in Mobile Environments

Palade

Cabrera

White

et al. 2018

Service-Oriented Computing

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Users within a limited geographic area can form servicesharing communities using the services deployed on their mobile devices. Creating Quality of Service (QoS) optimal service compositions in such decentralised and dynamic environments is challenging because of the service providers' mobility and the inherent dynamism in the available services. Existing proposals for mobile environments either use templatematching composition or require a-priori knowledge about the QoS objectives' weights, which limits the composition's flexibility in such environments. This paper presents a stigmergic-based approach to model the decentralised, flexible and dynamic service interactions of providers in a mobile environment. A nature-inspired optimisation mechanism is used to approximate the set of QoS optimal compositions that result from these interactions. To facilitate adaptation of the composite during execution, we introduce a procedure that encourages the exploration of service composition configurations that emerge as a result of providers' mobility. We evaluate the performance of the proposed approach with a no-adaptation variant, a Dijkstra-based, a Greedy and a Random approach. The results show that the proposed approach can obtain superior solutions compared with current optimisation methods for flexible service composition in mobile environments at the cost of increased overhead.

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Simonstrator: Simulation and Prototyping Platform for Distributed Mobile Applications

Abstract: The documents distributed by this server have been provided by the contributing authors as a means to ensure timely dissemination of scholarly and technical work on a non-commercial basis.

Cited by 49 publications

References 21 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

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

Stigmergic Service Composition and Adaptation in Mobile Environments

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