A Knowledge-Based Cognitive Architecture Supported by Machine Learning Algorithms for Interpretable Monitoring of Large-Scale Satellite Networks

Abstract

Cyber–physical systems such as satellite telecommunications networks generate vast amounts of data and currently, very crude data processing is used to extract salient information. Only a small subset of data is used reactively by operators for troubleshooting and finding problems. Sometimes, problematic events in the network may go undetected for weeks before they are reported. This becomes even more challenging as the size of the network grows due to the continuous proliferation of Internet of Things type devices. To overcome these challenges, this research proposes a knowledge-based cognitive architecture supported by machine learning algorithms for monitoring satellite network traffic. The architecture is capable of supporting and augmenting infrastructure engineers in finding and understanding the causes of faults in network through the fusion of the results of machine learning models and rules derived from human domain experience. The system is characterised by (1) the flexibility to add new or extend existing machine learning algorithms to meet the user needs, (2) an enhanced pattern recognition and prediction through the support of machine learning algorithms and the expert knowledge on satellite infrastructure, (3) the ability to adapt to changing conditions of the satellite network, and (4) the ability to augment satellite engineers through interpretable results. An industrial real-life satellite case study is provided to demonstrate how the architecture could be used. A single blind experimental methodology was used to validate the results generated by our approach.

Internet of Things-Building Information Modeling Integration: Attacks, Challenges, and Countermeasures

“…Moreover, high-reliability demands, including service availability, reliability, safety, security, portability, maintenance, and scalability, are common in these applications . On the other hand, a few studies [28], [39], [52], [53] focused on solutions for centralized processing and storage of enormous volumes of building site data in the cloud databases. However, real-time applications are more difficult and have remained unaddressed.…”

“…While certain project delivery methods are acceptable for BIM implementations, design and build are required to maximize BIM advantages [53]. Several studies [28], [52], [73] support BIM and integrated project implementation for effective building sector cooperation.…”

“…IoT devices tend to differ significantly from traditional computers and computer devices to address security issues. Many IoT devices, including sensors, are designed to be deployed at a huge scale beyond ordinary Internet-connected devices in terms of their magnitude [53]. As a result, there is no such thing as the potential amount of interconnected devices.…”

“…Additionally, design analysis, safety improvement, productivity, and real-time equipment monitoring are made possible at various project phases [52]. According to Atazadeh et al (2019) and Oyekan et al (2021), IoT-BIM involves creating and managing objective information with a certain property, identification, and connection.…”

“…Furthermore, IoT-BIM technology helps in improving the visualization of building actors before the actual building by virtually placing the building before physical construction [53]. Despite traditional practice, BIM technology and associated processes enable more complicated and sustainable design processes that address growing challenges while reducing building costs and subsequent usage [72].…”