Abstract
The Artificial DNA (ADNA) is a powerful tool for designing self-organizing, self-healing and self-configuring distributed embedded systems. However, a large amount of knowledge on the targeted hardware, available sensors, is required, thus limiting the reusability and adaptability of an already composed ADNA. Recently, the abstract ADNA (\({A^{2}DNA}\)) has been proposed as a countermeasure to this problem. In an \({A^{2}DNA}\), sensor elements are replaced by so-called abstract sensors describing properties of the required sensory input. Only when the \({A^{2}DNA}\) is initialized on the target hardware, these abstract sensors are specified by a combination of actual sensors available. In addition, a semantic knowledge base provides knowledge on the hardware’s sensors and their relations. In order to convert an \({A^{2}DNA}\) to a hardware specific ADNA, knowledge about how to calculate a required sensor value that cannot be directly measured by the hardware from other available sensors is required. In this paper, we present and analyze two algorithms that determine this knowledge.
Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - project number 445555232.
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Notes
- 1.
At this stage the knowledge is limited to knowledge on the sensors and actuators.
- 2.
The block’s exact structure must not be known in the equation, just what block or set of blocks will be needed.
- 3.
Since all attribute values in an equation are from the same set, we only have these three cases.
- 4.
For better readability, the iteration over \(\mathcal {E}\) is written as a sequential one over the sets \(\mathcal {E}_\mathcal {Q}\), \(\mathcal {E}_\mathcal {D}\), \(\mathcal {E}_\mathcal {T}\) instead of using a switch case structure.
- 5.
Only in the last iteration, we do not add a new sensor.
- 6.
Thus, we only have to check if any counter has reached 0, instead of checking for different js.
- 7.
Since we have dequeue every enqueued entry the number of operations doubles.
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Koschowoj, A., Brinkschulte, U. (2023). Abstract Artificial DNA’s Improved Time Bounds. In: Goumas, G., Tomforde, S., Brehm, J., Wildermann, S., Pionteck, T. (eds) Architecture of Computing Systems. ARCS 2023. Lecture Notes in Computer Science, vol 13949. Springer, Cham. https://doi.org/10.1007/978-3-031-42785-5_13
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