Now showing 1 - 4 of 4
  • Publication
    Energy saving potential of adaptive, networked, embedded systems
    ( 2016)
    Heinrich, Patrick
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    Knorr, Rudi
    This paper presents and evaluates the energy saving potential of adaptive, networked, embedded systems. The aim is to demonstrate the benefits of modeling the energy demand during the development of such systems. For this purpose, the previous developed energy model is applied within a case study and different allocations of software components are compared. The estimated energy demands of these allocations are presented and discussed. The analyzed system of the case study represents an automotive system which executes two advanced driver assistance applications. The system is adaptive, which means that temporally unnecessary applications will be deactivated. Within the evaluated system this deactivation depends on the vehicle speed, which is derived by the New European Driving Cycle. Two different allocations of software components are evaluated.
  • Publication
    Early energy estimation of heterogeneous embedded networks within adaptive systems
    ( 2015)
    Heinrich, Patrick
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    Gossen, Dietrich
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    Knorr, Rudi
    This paper presents and evaluates a new approach of modeling energy consumption of communication within adaptive networked embedded systems. The objective is to enable energy estimation within early phases of system development, which allows system designers to compare different allocations of software components. As networked embedded systems consist of multiple specialized networks (with different protocols and topologies) and are characterized by a high degree of interaction, existing network-centric approaches have significant disadvantages describing entire systems. To overcome this problem a model was created which is based on individual communication connections between software components. This enables technology-transparent mapping to network topologies (across borders of networks) which significantly simplifies the evaluation of different software placements.
  • Publication
    Early energy estimation of networked embedded systems executing concurrent software components
    ( 2015)
    Heinrich, Patrick
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    Bergler, Hannes
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    This paper presents and evaluates a new approach of modeling energy consumption of embedded systems resulted by concurrent software components. The objective is to enable energy estimation within early phases of system development, which allows system designers to compare different allocations of software components within networked systems. The individual elements of the presented model are: energy consumption of software components themselves, energy consumption resulted by any software component, and energy consumption resulted by specific software components. The The developed model was applied within an automotive case study which shows a theoretical energy saving potential of 36.2 %. This demonstrates the potential and relevance of modeling energy estimation within early development phases.
  • Publication
    A self-learning approach for validation of communication in embedded systems
    ( 2014)
    Langer, Falk
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    This paper demonstrates a new approach that addresses the problem of evaluating the communication behavior of embedded systems by applying algorithms from the area of artificial intelligence. An important problem for the validation for the interaction in the distributed system is missing, wrong or incomplete specification. This paper demonstrates the application of a new self-learning approach for assessing the communication behavior based on reference traces. The benefit of the approach is that it works automatically, with low additional effort and without using any specification. The investigated methodology uses algorithms from the field of machine learning and data mining to extract behavior models out of a reference trace. For showing the application, this paper provides a use case and the basic setup for the proposed method. The applicability of this self-learning methodology is evaluated based on real vehicle network data.