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Open research questions in Fire Detection and Safety Systems

48 unresolved questions extracted from the limitations and future-work sections of 154 Fire Detection and Safety Systems papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • Electromagnetic interference generated by the pump motor disrupted GSM communication. The system's performance is evaluated through a limited number of controlled trials.

    A Research on Fire GSM Service Robot · 2026 · DOI

  • The integration of AI and Deep Learning to enhance the system's understanding of its environment. The development of more advanced sensor-driven navigation and alert transmission systems.

    A Research on Fire GSM Service Robot · 2026 · DOI

  • Traditional fire detection methods often fail to provide timely alerts, putting lives and property at risk. There is a need for a system that can detect fire and alert authorities in real-time.

    Design and implementation of an IoT-based fire detection and alert system for real-time hazard monitoring · 2026 · DOI

  • Manual observation of video feeds is time-consuming, labor intensive, and prone to human error. The need for a low-cost, portable, and intelligent surveillance framework. The requirement for real-time threat detection and alert generation.

    DEEP LEARNING ENABLED FIREARM DETECTION AND AUTOMATED THREAT ALERT SYSTEM USING YOLO AND RASPBERRY PI. · 2026 · DOI

  • Human error and delayed emergency responses in traditional kitchens. Limited functionality of traditional kitchen safety devices. Lack of integration with modern communication networks in traditional kitchen safety systems.

    IOT-BASED SMART KITCHEN WITH ENHANCED AND AUTOMATED SAFETY MEASURES · 2026 · DOI

  • The absence of maritime-specific validation is a critical challenge in the development of fire detection systems. The lack of a standardized FHI framework is a significant challenge in the development of fire detection systems. Real-time adaptive learning mechanisms remain computationally constrained for onboard deployment.

    A Systematic Review of Adaptive Thresholding and Fire Hazard Index for Early Fire Detection in Ship Cargo Holds · 2026 · DOI

  • The fuzzy logic inference system uses fixed distance and velocity ranges (3-1008 cm for distance, 10-50 cm/s for velocity); optimization of these parameters for different fire environments and scaling to larger operational areas is not addressed.

    Design and Development of Fire Fighting Robot · 2026 · DOI

  • Conventional sensor-based systems have limitations in dynamic real-world IoT environments. There is a need for a comprehensive deep learning-based framework for intelligent smoke detection.

    An optimized deep learning framework for IoT-based smoke detection with enhanced performance and computational efficiency · 2026 · DOI

  • 1. Install strategically. Place units in hallways, near bedrooms, and close to gas appliances. For LPG (heavier than air), mount 6–12 inches above the floor; for methane (lighter than air), mount near the ceiling. Keep at least 3 meters from cooking surfaces to reduce false alarms. 2. Test monthly. Press and hold the test button for three seconds each month to verify buzzer, indicator lights, and emergency LEDs. This takes less than 30 seconds. 3. Follow emergency protocols. For smoke alarms: evacuate immediately and call emergency services from outside. For gas alarms: do not operate any electrical switches, evacuate immediately, and call the gas company from outside. Never re-enter until cleared. 4. Integrate IoT capabilities. Add WiFi or LoRa connectivity for remote smartphone notifications, battery status monitoring, and maintenance reminders.

    Design And Development of Dual Source Emergency Light with Integrated Smoke and Gas Detection · 2026 · DOI

  • Improving the accuracy and robustness of the fire detection model. Integrating FireGuard AI with other safety systems and devices. Deploying FireGuard AI in various environments and evaluating its performance.

    FireGuard AI: An Intelligent Real-Time Fire Detection and Emergency Response System Using Computer Vision and Artificial Intelligence · 2026 · DOI

  • The need for an intelligent and integrated approach to fire safety. The lack of cost-effective and scalable fire detection systems. The limitation of traditional fire detection systems in providing real-time response and emergency guidance.

    FireGuard AI: An Intelligent Real-Time Fire Detection and Emergency Response System Using Computer Vision and Artificial Intelligence · 2026 · DOI

  • There is a lack of scholarly attention to forest fires in Turkey despite their significance. Previous studies have limitations in terms of centralized processing or standalone algorithms. There is a need for a novel hybrid model that combines the strengths of different algorithms for wildfire detection and prediction.

    An intelligent IoT–machine learning framework for wildfire detection and prediction using a hybrid RF–XGB model · 2026 · DOI

  • Our future work will consider incorporating additional sensors, including photoelectric smoke detectors, flame sensors, particulate matter monitors to improve detection accuracy across diverse fire‑precursor signatures, integrate a GSM module as a fallback for mobile notifications through SMS when Wi-Fi is unavailable.

    Design and implementation of an IoT-based fire detection and alert system for real-time hazard monitoring · 2026 · DOI

  • The system requires access to visual data from the disaster scene. The system may not perform well in scenarios with limited lighting or visibility.

    Multimodal Computer Vision for Rapid Disaster Damage Analysis and Victim Detection using Deep Learning · 2026 · DOI

  • To improve the performance of the system in scenarios with limited lighting or visibility. To develop more advanced image processing routines for fire and smoke detection.

    Multimodal Computer Vision for Rapid Disaster Damage Analysis and Victim Detection using Deep Learning · 2026 · DOI

  • The need for a smart fire extinguisher switch mechanism with an integrated safety monitoring system. The lack of a globally recognizable visual architecture in traditional fire extinguishing technology.

    Smart Fire Extinguisher Switch Mechanism with Integrated Safety Monitoring System · 2026 · DOI

  • The system requires a large dataset for training. The system may not perform well in scenarios with limited visibility (e.g. fog, sunset). The system may have communication overhead when using multiple nodes.

    A scalable and cost-effective forest fire detection approach using deep transfer learning on a Raspberry Pi cluster · 2026 · DOI

  • Investigating the use of other machine learning models for forest fire detection. Improving the system's performance in scenarios with limited visibility. Deploying the system in real-world scenarios.

    A scalable and cost-effective forest fire detection approach using deep transfer learning on a Raspberry Pi cluster · 2026 · DOI

  • Incorporating acoustic gunshot detection and advanced multi-sensor threat monitoring applications. Extending the system to detect other types of threats.

    DEEP LEARNING ENABLED FIREARM DETECTION AND AUTOMATED THREAT ALERT SYSTEM USING YOLO AND RASPBERRY PI. · 2026 · DOI

  • The lack of quantitative energy profiling is a research gap. Limited cross-architecture benchmarking is another research gap. The underexplored intersection of spectral pattern learning and hardware-efficient deployment is a research gap.

    A COMPREHENSIVE REVIEW OF DEEP LEARNING-BASED FOREST FIRE AND SMOKE DETECTION: MODEL ARCHITECTURES, HARDWARE BACKENDS, AND CROSS-PLATFORM BENCHMARKING · 2026 · DOI

  • The intersection of spectral pattern learning and hardware-efficient deployment has not been studied. (2024) represents a promising approach to embedding domain knowledge into learnable preprocessing, but its computational overhead on different hardware backends — particularly MPS versus CUDA — has not been characterised. Im Cho, "An efficient deep learning algorithm for fire and smoke detection with limited data," Advances in Electrical and Computer Engineering, vol.

    A COMPREHENSIVE REVIEW OF DEEP LEARNING-BASED FOREST FIRE AND SMOKE DETECTION: MODEL ARCHITECTURES, HARDWARE BACKENDS, AND CROSS-PLATFORM BENCHMARKING · 2026 · DOI

  • Traditional kitchens rely on manual monitoring of appliances and gas usage, which is prone to human error and delayed emergency responses. Traditional kitchen safety devices provide essential alerts but often fail to intervene during emergencies or inform users remotely.

    IOT-BASED SMART KITCHEN WITH ENHANCED AND AUTOMATED SAFETY MEASURES · 2026 · DOI

  • Extending the model architecture to account for tunnel gradient and curvature. Employing more advanced architectures like Graph Neural Networks (GNNs) to generalize across non-Euclidean spatial structures.

    Spatio-Temporal Prediction of Critical Smoke Properties in Tunnel Fires Using a Hybrid CNN-LSTM Network · 2026 · DOI

  • Testing the Fireguard Approach in different environments and conditions. Improving the autonomous flights of UAS without backup of a drone-pilot. Developing new applications for the use of 5G mobile networks in Wildfire Management.

    Fireguard: A Real-Time Wildfire Monitoring and Risk Assessment System Using Unmanned Aerial Systems and Multi-Sensor Fusion · 2026 · DOI

  • The lack of innovative techniques for Wildfire Management. The limited use of 5G mobile networks for wireless data handling in this field. The need for a novel system for real-time wildfire monitoring and risk assessment.

    Fireguard: A Real-Time Wildfire Monitoring and Risk Assessment System Using Unmanned Aerial Systems and Multi-Sensor Fusion · 2026 · DOI

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48 open questions have been extracted from the limitations and future-work passages of 154 Fire Detection and Safety Systems papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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