1. SISN
  2. All Volumes and Issues
  3. Volume 17, 2025
  4. Software of Internet-Accessible Semiconductor Laboratory

Software of Internet-Accessible Semiconductor Laboratory

SISN.
2025;
Volume 17
: pp. 146 - 159
https://doi.org/10.23939/sisn2025.17.146
Authors:
  1. Roman Nazarevych
  2. Ivan Bolesta
1
Ivan Franko National University of Lviv, Electronics and Computer Technologies Department, Lviv, Ukraine
2
Ivan Franko National University of Lviv, Electronics and Computer Technologies Department, Lviv, Ukraine

The paper presents approaches for creating an automated internet-accessible semiconductor laboratory, specifically the control software system that ensures the internet accessibility of the laboratory. The functionality and structure of the software-hardware complex, developed with consideration of known solutions, are described, as well as its implementation options using the internet, cloud, and edge computing. Local implementation options with enhanced resilience to force majeure factors and cyber threats are also considered. Optimal solutions for databases and communication protocols are analyzed and proposed, with solutions selected to meet the requirements of a distributed system with limited resources at edge nodes, specifically limitations in memory and computational power. The proposed relational database SQLite has minimal resource requirements while providing most of the capabilities of relational databases, and the proposed gRPC protocol is one of the fastest and most efficient in utilizing network resources. The main approaches to implementing edge computing (Cloudlet, MEC, Fog Computing) are reviewed, and the optimal option for the internet-accessible laboratory is selected. The overall structure and organization of the basic laboratory, client, and data components of the software-hardware complex are presented. A high-level algorithm for the interaction of the main system nodes and the distribution of computational tasks between them is described. This algorithm, leveraging the advantages of hybrid (cloud-edge) computing, accelerates the processing of experiment results, reduces the load on the internet connection channel, and lowers the computational load on the cloud node. Additionally, this algorithm supports operation in an offline mode, allowing the system to function fully without an internet connection, without limiting its functionality.

internet-accessible laboratory
software-hardware complex
Edge Computing
cloud computing
distributed computing
heterogeneous computing
internet communication protocols
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