Runtime variability for dynamic reconfiguration in wireless sensor network product lines

Ortiz, Óscar; García, Ana M. García; Capilla, Rafael; Bosch, Jan; Hinchey, Michael G.

doi:10.1145/2364412.2364436

Cited by 18 publications

(13 citation statements)

References 28 publications

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“…Ortiz et al . consider the runtime variability, a key technique for the success of dynamic software product lines, as certain application demand reconfiguration of system features and execution plans at runtime. In this emerging research work, the author's address the problem of dynamic changes in future models in sensor network product families, where the nodes of the network demand dynamic reconfiguration at post‐deployment time.…”

Section: Related Workmentioning

confidence: 99%

“…The simulator consists of the following parts: network topology model, key establishment protocol and adversary model for node capture, network analysis tools and a graphical user interface to facilitate the rapid simulation, visualization and analysis of WSNs. Ortiz et al [30] consider the runtime variability, a key technique for the success of dynamic software product lines, as certain application demand reconfiguration of system features and execution plans at runtime. In this emerging research work, the author's address the problem of dynamic changes in future models in sensor network product families, where the nodes of the network demand dynamic reconfiguration at postdeployment time.…”

Section: (E) Modelling To Support Overall Issues Of Iotmentioning

confidence: 99%

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Modelling of Internet of Things units for estimating security-energy-performance relationships for quality of service and environment awareness

Venčkauskas

Štuikys

Damaševičius

et al. 2016

Security Comm. Networks

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Complexity of Internet of Things (IoT) applications and difficulty to predict their behaviour in wireless communications make modelling of IoT units an important research topic. The IoT unit is considered here as the two‐node IoT model that supports bi‐directional wireless communications. There are many approaches to model security and energy awareness; however, little is known about the synergistic effect of those factors at the application level under the influence of environmental factors such as noise. The paper introduces a modelling framework to model the security‐energy‐environment issues as main attributes to allow defining quality of service (QoS) for the IoT‐based applications. Among others, the healthcare ones are regarded as predominant now. We model IoT units using the feature‐based modelling methodology adopted from the software engineering domain. The result of modelling is a set of feature models with valid configurations that describe the energy‐security‐environment‐performance relationships and possible constraints to support various IoT applications. Having feature models, we can select a configuration that is best suited for a given IoT application with respect to QoS requirements. As feature models represent abstract relationships of domain factors, we need additionally to provide experiments to determine concrete values of modelled features. We describe the experimental system to measure energy consumption under the influencing factors. Both models (abstract and concrete) allow reasoning about QoS of the IoT applications. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Related Workmentioning

confidence: 99%

Section: (E) Modelling To Support Overall Issues Of Iotmentioning

confidence: 99%

Modelling of Internet of Things units for estimating security-energy-performance relationships for quality of service and environment awareness

Venčkauskas

Štuikys

Damaševičius

et al. 2016

Security Comm. Networks

View full text Add to dashboard Cite

show abstract

“…They propose a solution for the automation of changes in the structural variability (i.e., adding or removing features at run-time). They use the notion of super-types [33], [34] while we use the feature model composition. In addition, they propose some techniques to check the feature model and the configuration model [21], [35], [36] once they are modified.…”

Section: Related Workmentioning

confidence: 99%

Achieving Knowledge Evolution in Dynamic Software Product Lines

Arcega

Font

Haugen

et al. 2016

2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER)

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Dynamic Software Product Lines (DSPLs) offer a strategy to deal with software changes that need to be handled at run-time. In response to context changes, a DSPL capitalize on knowledge about the architecture variability of the software system to shift between configurations. Similar to any other kind of software, a DSPL needs to evolve over time but current approaches require software engineers to manually perform the DSPL evolution. Our work addresses the evolution of the architecture variability that makes up the knowledge of the DSPL. Given a new version of the architecture variability, we calculate its configuration space and propose strategies that allow migration from the current version to the new version. Our strategy solves the collision of the realization layer resulting from the integration of the new version of the variability specification. We evaluate our dynamic evolution strategy using the Goal-Question-Metric method for a Smart Hotel case study with 2 39 possible configurations as starting point. Our experiment indicates that the proposed technique would enable automatic evolution in 9 out of 10 cases. In the rest of the cases, all of the DSPL configurations changed between the old and the new version, which frustrates an automatic evolution.

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“…The simulator has five main components: a network topology model, a key establishment protocol, and an adversary model for node capture, network analysis tools, and a graphical user interface to facilitate the rapid simulation, visualization, and analysis of WSNs. Ortiz et al [20] consider runtime variability, which is a key technique for the success of dynamic software product lines (DSPLs), as certain applications demand reconfiguration of system features and execution plans at runtime. In this emerging research work, the authors address the problem of dynamic changes in feature models in sensor-network product families, where the nodes of the network demand dynamic reconfiguration at post-deployment time.…”

Section: Related Workmentioning

confidence: 99%

A Model-Driven Framework to Develop Personalized Health Monitoring

et al. 2016

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Both distributed healthcare systems and the Internet of Things (IoT) are currently hot topics. The latter is a new computing paradigm to enable advanced capabilities in engineering various applications, including those for healthcare. For such systems, the core social requirement is the privacy/security of the patient information along with the technical requirements (e.g., energy consumption) and capabilities for adaptability and personalization. Typically, the functionality of the systems is predefined by the patient's data collected using sensor networks along with medical instrumentation; then, the data is transferred through the Internet for treatment and decision-making. Therefore, systems creation is indeed challenging. In this paper, we propose a model-driven framework to develop the IoT-based prototype and its reference architecture for personalized health monitoring (PHM) applications. The framework contains a multi-layered structure with feature-based modeling and feature model transformations at the top and the application software generation at the bottom. We have validated the framework using available tools and developed an experimental PHM to test some aspects of the functionality of the reference architecture in real time. The main contribution of the paper is the development of the model-driven computational framework with emphasis on the synergistic effect of security and energy issues.

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Runtime variability for dynamic reconfiguration in wireless sensor network product lines

Cited by 18 publications

References 28 publications

Modelling of Internet of Things units for estimating security-energy-performance relationships for quality of service and environment awareness

Modelling of Internet of Things units for estimating security-energy-performance relationships for quality of service and environment awareness

Achieving Knowledge Evolution in Dynamic Software Product Lines

A Model-Driven Framework to Develop Personalized Health Monitoring

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