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Emergency Caching: Coded Caching-based Reliable Map Transmission in Emergency Networks
Feb 27, 2024
Many rescue missions demand effective perception and real-time decision making, which highly rely on effective data collection and processing. In this study, we propose a three-layer architecture of emergency caching networks focusing on data collection and reliable transmission, by leveraging efficient perception and edge caching technologies. Based on this architecture, we propose a disaster map collection framework that integrates coded caching technologies. Our framework strategically caches coded fragments of maps across unmanned aerial vehicles (UAVs), fostering collaborative uploading for augmented transmission reliability. Additionally, we establish a comprehensive probability model to assess the effective recovery area of disaster maps. Towards the goal of utility maximization, we propose a deep reinforcement learning (DRL) based algorithm that jointly makes decisions about cooperative UAVs selection, bandwidth allocation and coded caching parameter adjustment, accommodating the real-time map updates in a dynamic disaster situation. Our proposed scheme is more effective than the non-coding caching scheme, as validated by simulation.
Emergency Computing: An Adaptive Collaborative Inference Method Based on Hierarchical Reinforcement Learning
Feb 03, 2024In achieving effective emergency response, the timely acquisition of environmental information, seamless command data transmission, and prompt decision-making are crucial. This necessitates the establishment of a resilient emergency communication dedicated network, capable of providing communication and sensing services even in the absence of basic infrastructure. In this paper, we propose an Emergency Network with Sensing, Communication, Computation, Caching, and Intelligence (E-SC3I). The framework incorporates mechanisms for emergency computing, caching, integrated communication and sensing, and intelligence empowerment. E-SC3I ensures rapid access to a large user base, reliable data transmission over unstable links, and dynamic network deployment in a changing environment. However, these advantages come at the cost of significant computation overhead. Therefore, we specifically concentrate on emergency computing and propose an adaptive collaborative inference method (ACIM) based on hierarchical reinforcement learning. Experimental results demonstrate our method's ability to achieve rapid inference of AI models with constrained computational and communication resources.
An Improved Virtual Force Approach for UAV Deployment and Resource Allocation in Emergency Communications
Jan 17, 2024In this paper, we consider an unmanned aerial vehicle (UAV)-enabled emergency communication system, which establishes temporary communication link with users equipment (UEs) in a typical disaster environment with mountainous forest and obstacles. Towards this end, a joint deployment, power allocation, and user association optimization problem is formulated to maximize the total transmission rate, while considering the demand of each UE and the disaster environment characteristics. Then, an alternating optimization algorithm is proposed by integrating coalition game and virtual force approach which captures the impact of the demand priority of UEs and the obstacles to the flight path and consumed power. Simulation results demonstrate that the computation time consumed by our proposed algorithm is only $5.6\%$ of the traditional heuristic algorithms, which validates its effectiveness in disaster scenarios.
UAV-assisted Emergency Integrated Sensing and Communication Networks: A CNN-based Rapid Deployment Approach
Jan 13, 2024UAV-assisted integrated sensing and communication (ISAC) network is crucial for post-disaster emergency rescue. The speed of UAV deployment will directly impact rescue results. However, the ISAC UAV deployment in emergency scenarios is difficult to solve, which contradicts the rapid deployment. In this paper, we propose a two-stage deployment framework to achieve rapid ISAC UAV deployment in emergency scenarios, which consists of an offline stage and an online stage. Specifically, in the offline stage, we first formulate the ISAC UAV deployment problem and define the ISAC utility as the objective function, which integrates communication rate and localization accuracy. Secondly, we develop a dynamic particle swarm optimization (DPSO) algorithm to construct an optimized UAV deployment dataset. Finally, we train a convolutional neural network (CNN) model with this dataset, which replaces the time-consuming DPSO algorithm. In the online stage, the trained CNN model can be used to make quick decisions for the ISAC UAV deployment. The simulation results indicate that the trained CNN model achieves superior ISAC performance compared to the classic particle swarm optimization algorithm. Additionally, it significantly reduces the deployment time by more than 96%.