STP (Smart Tracking Platform) is a research & development project, awarded within the CENTRIC project: Center for knowledge transfer to enterprises in the ICT field with the id number 2014+ 119722.

Planned to be implemented in close collaboration with researchers from the Ștefan cel Mare University of Suceava, the project is in line with the CENTRIC field of interest: researching, designing and developing software modules for an asset tracking software application, based on cloud, artificial intelligence and blockchain / distributed ledger technology, adopted as Software as a Service.  

The project proposes to research and subsequently design Smart Tracking Platform modules meant to track assets, equipment or raw materials, providing users with a fully comprehensive history of the items along the supply chain. The general purpose of the project is to meet the needs of small and medium-sized companies in terms of asset tracking, customizable for multiple types of businesses.

The targeted groups are small and medium companies.

The STP project proposes the following objectives:

• addressing the need to identify and track company assets/raw materials/ equipment along the supply chain.
• researching means of asset management and tracking, security requirements, and intelligent solutions that can be integrated into the STP platform modules.
• designing the architecture of various components, based on research on AI - anomaly detection Machine Learning. 
• performing an experimental model.

ASSIST Software's role in the project includes research and development, UI/UX prototyping, elaboration of software architecture, and software development of multiple components, such as frontend, backend, mobile, ML and DLT. Some of the technologies used to build the STP components are Ruby on Rails, React, BigchainDB, Machine Learning, Salesforce and PostgreSQL. 

Research articles

For more insight, we recommend reading these articles that our colleagues published in peer-approved publications, detailing processes, ideas, and the technology used.  

• A Comparative Study of Unsupervised Anomaly Detection Algorithms used in a Small and Medium-Sized Enterprise 2022, International Journal of Advanced Computer Science and Applications (IJACSA) 

Join us as we delve into a comprehensive evaluation of five unsupervised anomaly detection algorithms. Our focus is on a real-world dataset from a small and medium-sized software enterprise, providing practical insights that you won't want to miss. We understand the importance of cost-effective solutions, so we explore the power of open-source machine learning (ML) algorithms available in libraries like scikit-learn and PyOD.

• Centralized vs. Decentralized: Performance Comparison between BigchainDB and Amazon QLDB  2022, MDPI Applied Sciences

We compare the performance of two prominent ledger technologies: BigchainDB, the decentralized blockchain database, and Amazon QLDB, the centralized ledger database known for its transparency and immutability features. The stage for this showdown is set within our cutting-edge traceability platform, the Smart Tracking Platform (STP). Through a series of meticulously designed experiments, we delve into critical metrics such as CPU and memory usage, examining reading and writing operations.