Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support Systems
Abstract
:1. Introduction
- We review technologies and approaches related to real-time semantic data integration and reasoning on IoT-enabled collaborative IoT entities and edge devices in life- and time-critical decision support systems, focusing on SAR missions and disaster management scenarios.
- We identify and discuss open issues and challenges focusing on the specific topic of semantic data integration and reasoning with SAR-related knowledge in life- and time-critical decision support systems.
- We propose a novel approach that goes beyond the state-of-the-art in efficiently recognizing time-critical high-level events, supporting commanders and first responders with meaningful and life-critical insights about the current and predicted states of the environment in which they operate.
2. Preliminaries
2.1. IoT and Edge Computing
2.2. UAVs
2.3. Semantic Interoperability
2.4. KGs
2.5. Semantic Reasoning
2.6. Graph Neural Networks
3. Survey Methodology
- IoT in disaster management;
- IoT in SAR missions;
- Decision support in disaster management;
- Semantic reasoning in SAR missions;
- Semantic modeling in SAR missions;
- Real-time reasoning;
- Edge computing in SAR missions;
- Ontologies in SAR missions;
- Ontologies and IoT in SAR missions;
- UAVs in SAR missions.
- Inclusion Criteria
- IC1. Articles published after 2019.
- IC2. Articles related to SAR missions.
- IC3. Articles proposing/implementing a related system/framework.
- IC4. Articles retrieved from Google Scholar, ResearchGate, IEEE Xplore, ACM digital library, and Springer Link.
- IC5. Articles related to the aforementioned keywords.
- Exclusion Criteria
- EC1. Articles written in a non-English language.
- EC2. Articles published in non-scientific resources.
- EC3. Articles with poor analysis.
4. Related Work
4.1. Sensor-Based IoT Applications
4.2. Semantic Modeling and KGs
4.3. Decision Support and Reasoning
5. Discussing Open Issues and Challenges
- R1.
- Integration of multiple heterogeneous collaborative IoT entities (e.g., UAVs, ground robots, wearables, etc.) equipped with sensors (temperature, etc.).
- R2.
- Semantic integration of heterogeneous stream/dynamic (sensor) data using suitable ontologies.
- R3.
- Analysis of streamed data for recognizing real-time low-level events (e.g., fire at specific lat/long/alt coordinates, image/video analysis for the detection of trapped/injured victims, etc.).
- R4.
- Dynamic construction and use of KGs for the representation of the current state in the field of missions.
- R5.
- Recognition of high-level events with automated reasoning over the constructed KG and KG-based recommendations about actions/decisions to be made.
- R6.
- Integration of actions needed to be performed for a recognized event.
- R7.
- Transformation of the decision makers’ natural language queries to machine-understandable queries, to further assist in the decision-making process.
- R8.
- Integration of ML models for making predictions about the evolution of the state in the next few life-critical seconds/minutes, using historical data.
6. Proposed Framework
- Integration of multiple heterogeneous collaborative IoT entities (UAVs, ground robots, weather stations, wearables, etc.) equipped with sensors (temperature, humidity, air quality, etc.) able to sense and/or move the disaster-affected site, collecting valuable data in real-time.
- Semantic integration of heterogeneous stream/dynamic (sensor) data with static data (e.g., missions plans) using suitable ontologies, in one or more ground base units/controllers on edge devices, operating as an interoperability middleware, e.g., Raspberry Pi, making the proposed framework independent from the environmental status (e.g., destruction of critical infrastructures such as telecommunication base stations).
- Analysis of streamed data for recognizing real-time low-level events (e.g., fire at specific lat/long/alt coordinates, image/video analysis for the detection of trapped/injured victims, etc.) using ML models and techniques such as YOLO.
- Dynamic construction and use of KGs for the representation of the current state in the field of operations.
- Recognition of high-level events with automated reasoning over the constructed KG, and KG-based recommendations about actions/decisions to be made, such as aborting the mission due to a high risk of additional human losses, etc.
- Integration of actions needed to be performed for a recognized event using SWRL, SPIN, or SHACL rules.
- Translation of the decision makers’ natural language queries into machine-understandable queries (in SPARQL or Cypher), for inferring new knowledge and further assisting in the decision-making process.
- Integration of ML models, such as GNN models, for making predictions about the evolution of the state in the next few life-critical seconds/minutes using historical data.
7. Conclusion and Future Work
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Soularidis, A.; Kotis, K.Ι.; Vouros, G.A. Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support Systems. Electronics 2024, 13, 526. https://doi.org/10.3390/electronics13030526
Soularidis A, Kotis KΙ, Vouros GA. Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support Systems. Electronics. 2024; 13(3):526. https://doi.org/10.3390/electronics13030526
Chicago/Turabian StyleSoularidis, Andreas, Konstantinos Ι. Kotis, and George A. Vouros. 2024. "Real-Time Semantic Data Integration and Reasoning in Life- and Time-Critical Decision Support Systems" Electronics 13, no. 3: 526. https://doi.org/10.3390/electronics13030526