An official website of the United States government
In the field of autonomous transportation systems, the integration of Unmanned Aerial Vehicles (UAVs) in emergency response scenarios is important for enhancing the operational efficiency and the victims’ positioning. This article presents a novel Positioning, Navigation, and Timing (PNT) framework, namedHEROES, which leverages the UAV and integrated sensing and communication technologies to address the challenges in post-disaster environments. Our approach focuses on a comprehensive post-disaster scenario involving multiple victims, first responders, UAVs, and an emergency control center. HEROES enables UAVs to function as anchor nodes and facilitate the precise positioning of the victims while simultaneously collecting critical data from the disaster area. We further introduce a reinforcement learning model based on the Optimistic Q-learning with Upper Confidence Bound algorithm, enabling the victims and first responders to autonomously select the most advantageous UAV connections based on their channel gain, shadowing probability, and positional characteristics. Furthermore, HEROES is based on a satisfaction game-theoretic model to enhance the sensing, communication, and positioning functionalities. Our analysis reveals the existence of various satisfaction equilibria, including minimum efficient satisfaction equilibrium, ensuring that the UAVs meet their quality of service constraints at minimal operational costs. Extensive experimental results validate the scalability and performance of HEROES, demonstrating significant improvements over existing state-of-the-art methods in delivering PNT services during humanitarian emergencies.
more »
« less
FReCS: A First Responder Classification System
In the digital communication age, using social media data to classify first responders presents a new and promising approach to enhancing emergency response strategies. We introduce the First Responder Classification System (FReCS), a framework that annotates and classifies disaster tweets from 26 crisis events. Our annotations cater for first reponders and their sub-layers. Furthermore, we proposed a classifier called RoBERTa-CAFÉ that integrates pre-trained RoBERTa with Cross-Attention and Focused-Entanglement components, improving the precision and reliability of classification tasks. The model is rigorously tested across publicly available disaster datasets. The RoBERTa-CAFÉ model outperformed state-of-the-art models in identifying relevant emergency communications, displaying its generalization, robustness, and adaptability. Our FReCS approach offers a pioneering technique for classifying first responders and enhances emergency management systems’ operational capabilities, leading to more efficient and effective disaster responses.
more »
« less
- Award ID(s):
- 2219614
- PAR ID:
- 10657495
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- Page Range / eLocation ID:
- 355 to 372
- Subject(s) / Keyword(s):
- Data Annotation, Social Media, Emergency Management, First Responder, and Transformer
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)During disasters, it is critical to deliver emergency information to appropriate first responders. Name-based information delivery provides efficient, timely dissemination of relevant content to first responder teams assigned to different incident response roles. People increasingly depend on social media for communicating vital information, using free-form text. Thus, a method that delivers these social media posts to the right first responders can significantly improve outcomes. In this paper, we propose FLARE, a framework using 'Social Media Engines' (SMEs) to map social media posts (SMPs), such as tweets, to the right names. SMEs perform natural language processing-based classification and exploit several machine learning capabilities, in an online real-time manner. To reduce the manual labeling effort required for learning during the disaster, we leverage active learning, complemented by dispatchers with specific domain-knowledge performing limited labeling. We also leverage federated learning across various public-safety departments with specialized knowledge to handle notifications related to their roles in a cooperative manner. We implement three different classifiers: for incident relevance, organization, and fine-grained role prediction. Each class is associated with a specific subset of the namespace graph. The novelty of our system is the integration of the namespace with federated active learning and inference procedures to identify and deliver vital SMPs to the right first responders in a distributed multi-organization environment, in real-time. Our experiments using real-world data, including tweets generated by citizens during the wildfires in California in 2018, show our approach outperforming both a simple keyword-based classification and several existing NLP-based classification techniques.more » « less
-
null (Ed.)In this paper, an Unmanned Aerial Vehicles (UAVs) - enabled human Internet of Things (IoT) architecture is introduced to enable the rescue operations in public safety systems (PSSs). Initially, the first responders select in an autonomous manner the disaster area that they will support by considering the dynamic socio-physical changes of the surrounding environment and following a set of gradient ascent reinforcement learning algorithms. Then, the victims create coalitions among each other and the first responders at each disaster area based on the expected- maximization approach. Finally, the first responders select the UAVs that communicate with the Emergency Control Center (ECC), to which they will report the collected data from the disaster areas by adopting a set of log-linear reinforcement learning algorithms. The overall distributed UAV-enabled human Internet of Things architecture is evaluated via detailed numerical results that highlight its key operational features and the performance benefits of the proposed framework.more » « less
-
Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption — algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem — a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.more » « less
-
Emergency Response Management (ERM) is a critical problem faced by communities across the globe. Despite this, it is common for ERM systems to follow myopic decision policies in the real world. Principled approaches to aid ERM decision-making under uncertainty have been explored but have failed to be accepted into real systems. We identify a key issue impeding their adoption --- algorithmic approaches to emergency response focus on reactive, post-incident dispatching actions, i.e. optimally dispatching a responder after incidents occur. However, the critical nature of emergency response dictates that when an incident occurs, first responders always dispatch the closest available responder to the incident. We argue that the crucial period of planning for ERM systems is not post-incident, but between incidents. This is not a trivial planning problem --- a major challenge with dynamically balancing the spatial distribution of responders is the complexity of the problem. An orthogonal problem in ERM systems is planning under limited communication, which is particularly important in disaster scenarios that affect communication networks. We address both problems by proposing two partially decentralized multi-agent planning algorithms that utilize heuristics and exploit the structure of the dispatch problem. We evaluate our proposed approach using real-world data, and find that in several contexts, dynamic re-balancing the spatial distribution of emergency responders reduces both the average response time as well as its variance.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Book Chapter:
- https://doi.org/10.1007/978-3-031-78541-2_22
