Towards the automated operations of large distributed satellite systems. Part 1: Review and paradigm shifts

Ben-Larbi, Mohamed Khalil; Pozo, Kattia Flores; Haylok, Tom; Choi, Mirue; Grzesik, Benjamin; Haas, Andreas; Krupke, Dominik; Konstanski, Harald; Schaus, Volker; Fekete, Sándor P.; Schurig, Christian; Stoll, Enrico

doi:10.1016/j.asr.2020.08.009

Cited by 32 publications

(10 citation statements)

References 9 publications

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“…In the 2000s, the proliferation of satellite networks and the increasing complexity of satellite operations led to the emergence of big data in satellite network management. Satellite operators were now dealing with massive volumes of data generated by satellite sensors, equipment, and customer usage patterns (Ben-Larbi et al, 2021). This data, often referred to as big data, presented new challenges and opportunities for satellite operators.…”

Section: Historical Perspectivesmentioning

confidence: 99%

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

Nneka Adaobi Ochuba,

David Olanrewaju Olutimehin,

Olusegun Gbenga Odunaiya

et al. 2024

Magna Sci. Adv. Res. Rev.

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The application of big data analytics in satellite network management has emerged as a transformative approach to optimize performance and enhance reliability in the satellite telecommunications industry. This paper reviews the current state of big data analytics in satellite network management, highlighting its key applications and benefits. By analyzing large volumes of data generated by satellite networks, big data analytics enables satellite telecommunications companies to gain valuable insights into network performance, identify potential issues, and take proactive measures to ensure optimal performance. One of the key applications of big data analytics in satellite network management is predictive maintenance. By analyzing historical data and equipment performance metrics, companies can predict when equipment is likely to fail and take preventive measures to avoid downtime. This not only improves network reliability but also reduces maintenance costs and improves overall operational efficiency. Another important application is network optimization. Big data analytics can analyze network traffic, weather conditions, and other factors to optimize satellite beam coverage, frequency allocation, and routing. This helps companies maximize bandwidth utilization, reduce interference, and improve service quality. The implications of big data analytics for future technology developments in satellite network management are significant. As the volume of data generated by satellite networks continues to grow, there is a need for advanced analytics tools and techniques to process and analyze this data efficiently. Future technology developments in areas such as AI, machine learning, and data visualization are expected to play a key role in enhancing the capabilities of big data analytics in satellite network management. In conclusion, the application of big data analytics in satellite network management offers significant benefits in terms of optimizing performance and enhancing reliability. By leveraging the insights provided by big data analytics, satellite telecommunications companies can improve operational efficiency, reduce costs, and deliver better services to their customers. Future technology developments will further enhance the capabilities of big data analytics, paving the way for more efficient and reliable satellite network management.

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Section: Historical Perspectivesmentioning

confidence: 99%

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

Nneka Adaobi Ochuba,

David Olanrewaju Olutimehin,

Olusegun Gbenga Odunaiya

et al. 2024

Magna Sci. Adv. Res. Rev.

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“…4) Distributed satellite constellations may be more survivable in an attack. The primary goal of DSS is to provide a more responsive and resilient solution to meet the growing demands of the research community as well as the defense sector by assisting in the monitoring and prognostication of Earth Observation (EO) missions [10] and Space Domain Awareness (SDA) missions [11], [12], [13], [14] [16], [17], [18], [19], [20], [21] for EO [22]. There are two types of constellations: 1) polar and near-polar constellations and 2) Walker constellation.…”

Section: Distributed Satellite Systemmentioning

confidence: 99%

Near Real-Time Wildfire Management Using Distributed Satellite System

Thangavel

Spiller

Sabatini

et al. 2023

IEEE Geosci. Remote Sensing Lett.

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Climate action (SDG-13) is an integral part of the Sustainable Development Goals (SDGs) set by the United Nations (UN), and wildfire is one of the catastrophic events related to climate change. Large-scale forest fires have drastically increased in frequency and size in recent years in Australia and other nations. These wildfires endanger the forests and urban areas of the world, demolish vast amounts of property, and frequently result in fatalities. There is a requirement for real-time/near realtime catastrophic event monitoring of fires due to their growing frequency. In order to effectively monitor disaster events, it will be feasible to manage them in real time or near real time due to the advent of the Distributed Satellite System (DSS). This research examines the possible applicability of DSS for wildfire surveillance. For spacecraft to continually monitor the dynamically changing environment, satellite missions must have broad coverage and revisit intervals that DSS can fulfill. A feasibility analysis, as well as a model and scenario prototype for a satellite artificial intelligence (AI) system, is included in this letter to enable prompt action and swiftly provide alerts. In our previous research, it is shown that on-board implementation, i.e., data processing utilizing hardware accelerators, is feasible. To enable Trusted Autonomous Satellite Operation (TASO), the same will be included in the proposed DSS architecture, and the outcomes will be provided. To demonstrate the applicability, the suggested DSS architecture will be tested in several geographic locations to demonstrate the system-wide coverage.

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“…The primary goal of a DSS is to deliver a more responsive and resilient solution to meet the expanding demands of the scientific community and also the defence sector by aiding in the measurement and prediction of Earth Observation (EO) missions [ 12 ] and Space Based Space Surveillance (SBSS) missions [ 13 , 14 , 15 , 16 , 17 , 18 ] in the hope of enhancing space sustainability. Distributed satellite architectures can also be classified as formation-flying missions.…”

Section: Introductionmentioning

confidence: 99%

Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors

Thangavel

Spiller

Sabatini

et al. 2023

Sensors

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Recent developments in Distributed Satellite Systems (DSS) have undoubtedly increased mission value due to the ability to reconfigure the spacecraft cluster/formation and incrementally add new or update older satellites in the formation. These features provide inherent benefits, such as increased mission effectiveness, multi-mission capabilities, design flexibility, and so on. Trusted Autonomous Satellite Operation (TASO) are possible owing to the predictive and reactive integrity features offered by Artificial Intelligence (AI), including both on-board satellites and in the ground control segments. To effectively monitor and manage time-critical events such as disaster relief missions, the DSS must be able to reconfigure autonomously. To achieve TASO, the DSS should have reconfiguration capability within the architecture and spacecraft should communicate with each other through an Inter-Satellite Link (ISL). Recent advances in AI, sensing, and computing technologies have resulted in the development of new promising concepts for the safe and efficient operation of the DSS. The combination of these technologies enables trusted autonomy in intelligent DSS (iDSS) operations, allowing for a more responsive and resilient approach to Space Mission Management (SMM) in terms of data collection and processing, especially when using state-of-the-art optical sensors. This research looks into the potential applications of iDSS by proposing a constellation of satellites in Low Earth Orbit (LEO) for near-real-time wildfire management. For spacecraft to continuously monitor Areas of Interest (AOI) in a dynamically changing environment, satellite missions must have extensive coverage, revisit intervals, and reconfiguration capability that iDSS can offer. Our recent work demonstrated the feasibility of AI-based data processing using state-of-the-art on-board astrionics hardware accelerators. Based on these initial results, AI-based software has been successively developed for wildfire detection on-board iDSS satellites. To demonstrate the applicability of the proposed iDSS architecture, simulation case studies are performed considering different geographic locations.

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Towards the automated operations of large distributed satellite systems. Part 1: Review and paradigm shifts

Cited by 32 publications

References 9 publications

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

Reviewing the application of big data analytics in satellite network management to optimize performance and enhance reliability, with implications for future technology developments

Near Real-Time Wildfire Management Using Distributed Satellite System

Trusted Autonomous Operations of Distributed Satellite Systems Using Optical Sensors

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