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1. SAFE: Secure and Flexible Encryption for Dynamic Team Communications in Disaster Management

   https://doi.org/10.1109/ICNP59255.2023.10355587  

   Yu, Hongmiao; Chen, Jiachen; Ramakrishnan, K K (October 2023, IEEE)

   Name-based publish/subscribe systems using Information-Centric Networking (ICN) principles can provide a flexible and efficient framework for communication in disaster situations. Efficient, secure dissemination of information can play a critical role in disaster management. But, secure and authenticated group communications that maintain confidentiality and integrity remain a challenge. In this paper, we design a flexible and efficient encryption framework SAFE that leverages graph-based naming frameworks for providing role-based communication among first responders. We study the suitability of message-oriented encryption where the sender leverages the name hierarchy, and compare it with a key-oriented encryption scheme that requires the receiver to utilize appropriate keys to decrypt based on the publisher-targeted name for the message. Both encryption schemas can be built with attribute-based encryption (ABE) or public key encryption (PKE) implementations. We find message-oriented encryption provides the needed flexibility for dynamic environments when communicating with members changes frequently. With message-oriented encryption, we further address key revocation and support for infrastructure-less environments in disaster situations and consider the tradeoff between flexibility and optimization for large relatively static communication groups. We evaluate both encryption schemas built on top of ABE and PKE. We examine the key generation time, ciphertext length, encryption, and decryption time, and see that SAFE's design is the most suitable for large and dynamically changing groups. 

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2. FLARE: federated active learning assisted by naming for responding to emergencies

   https://doi.org/10.1145/3460417.3482978  

   Mittal, Viyom; Jahanian, Mohammad; Ramakrishnan, K. K. (September 2021, ICN '21: Proceedings of the 8th ACM Conference on Information-Centric Networking)

   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. 

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3. HEROES: Humanitarian Emergency Response based on UAV-enabled Integrated Sensing and Communication, Positioning, and Satisfaction Games

   https://doi.org/10.1145/3715334  

   Siraj, Md Sadman; Charatsaris, Panagiotis; Diamanti, Maria; Tsiropoulou, Eirini Eleni; Papavassiliou, Symeon (December 2025, ACM Journal on Autonomous Transportation Systems)

   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. 

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4. FReCS: A First Responder Classification System

   https://doi.org/10.1007/978-3-031-78541-2_22  

   Adesokan, Ademola; Madria, Sanjay; Nguyen, Long (September 2024, Springer Nature Switzerland)

   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. 

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5. DEMO: FLARE: Federated Active Learning Assisted by Naming for Responding to Emergencies

   https://doi.org/10.1109/ICNP52444.2021.9651978  

   Mittal, Viyom; Jahanian, Mohammad; Ramakrishnan, K. K. (November 2021, 2021 IEEE 29th International Conference on Network Protocols (ICNP))

   Name-based pub/sub allows for efficient and timely delivery of information to interested subscribers. A challenge is assigning the right name to each piece of content, so that it reaches the most relevant recipients. An example scenario is the dissemination of social media posts to first responders during disasters. We present FLARE, a framework using federated active learning assisted by naming. FLARE integrates machine learning and name-based pub/sub for accurate timely delivery of textual information. In this demo, we show FLARE’s operation. 

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