In disaster scenarios, e.g., earthquakes, tsunamis, and wildfires, communication infrastructure often becomes severely damaged. To rapidly restore damaged communication systems, we propose a UAV-based mobile base station equipped with Public Safety LTE (PS-LTE) technology to provide standalone communication capabilities. The proposed system includes PS-LTE functionalities, mission-critical push-to-talk, proximity-based services, and isolated E-UTRAN operation to ensure the reliable and secure communication for emergency services. We provide a simulation result to achieve the radio coverage of mobile base station. By using this radio coverage, we find an appropriate location of the end device for performing the outdoor experiments. We develop a prototype of a proposed mobile base station and test its operation in an outdoor environment. The experimental results provide a sufficient data rate to make an independent mobile base station to restore communication infrastructure in areas that experienced environmental disasters. This prototype and experimental results offer a significant step forward in creating agile and efficient communication solutions for emergency scenarios.

Recently, soft sensors have been widely adopted as alternatives to physical sensors. However, their generalization performance often degrades under distribution shifts, and their prediction errors are not directly observable without ground-truth measurements. As a result, cost-expensive sensors that consume significant energy or resources are still required in practice. To address this challenge, we study cost-efficient sensing scheduling in systems equipped with both soft and cost-expensive sensors, operating under a limited budget for activating the latter. We first develop an optimal sensing scheduling algorithm that minimizes measurement error while satisfying an average cost constraint. To enable its practical deployment, we propose a self-improving soft sensor that estimates predictive uncertainty and enhances its performance using selectively collected ground-truth measurements. We then design a cost-efficient sensing scheduling framework by integrating them. In this framework, predictive uncertainty serves as a proxy for unknown measurement errors, which provides guidance on when to activate cost-expensive sensors. Based on this guidance, the scheduling algorithm selectively acquires informative measurements that facilitate not only error reduction but also soft sensor improvement, given the limited budget. Extensive simulations on real-world datasets demonstrate that the proposed framework effectively quantifies predictive uncertainty, improves soft sensor accuracy, and significantly outperforms baseline algorithms in both error reduction and data efficiency.

This paper studies long-term resource allocation for rate-splitting multiple access (RSMA) in multi-carrier downlink systems. RSMA provides a flexible interference-management mechanism that bridges spatial division multiple access (SDMA) and non-orthogonal multiple access (NOMA), but guaranteeing long-term quality-of-service (QoS) performance under dynamic fading channels remains challenging. To address this limitation, we develop an opportunistic scheduling framework based on Lagrangian duality and stochastic optimization, which maximizes the long-term weighted sum rate (WSR) while satisfying per-user time-average QoS constraints. The proposed method decomposes the long-term problem into per-slot subproblems with adaptive effective weights, and each subproblem is efficiently solved through a two-stage procedure consisting of subcarrier–user pair matching and power allocation. Simulation results show that the proposed RSMA scheduling framework significantly outperforms conventional NOMA while ensuring the QoS requirements of all users. These results demonstrate the practical applicability of RSMA for next-generation wireless networks requiring both high spectral efficiency and long-term reliability.

In this paper, we present a reliability-aware multi-drone network management platform for restoring disrupted mobile communication services after disasters. The scientific contribution is not a new PHY/MAC scheme in isolation, but a management model that couples role assignment, relay placement, telemetry/video synchronization, and energy-aware reconfiguration, together with an explicit mapping from that model to deployable software modules. The implemented integrated control system combines a ground control system, a WebRTC/Kurento-based video transmission platform, and a oneM2M/Mobius-based data transmission platform for mission, relay, and tethered drones. Field validation with one tethered drone, one wireless relay drone, and two wireless mission drones indicates that relay-assisted operation improves both downlink and uplink SINR by 4.17 dB on average relative to direct transmission, which corresponds to a 2.61-fold increase in linear SINR and about a 20% improvement in normalized spectral efficiency. Across the seven evaluated mission points, the relay-assisted topology also expands point-wise service availability from 57.1% (4/7 points) to 100% (7/7 points). These results indicate that the proposed management architecture provides a practical basis for rapid communication restoration while also clarifying current limitations in scalability, operator workload assessment, and explicit fault-injection validation.

Segmenting low-light images, such as images showing cracks on tunnel walls, is challenging due to limited visibility. Hence, we need to combine image brightness enhancement and a segmentation algorithm. We introduce essential preliminaries, specifically highlighting deep learning-based low-light image enhancement methods and the pixel-level image segmentation algorithm. After that, we provide a three-step low-light image segmentation algorithm. The proposed algorithm begins with brightness and contrast enhancement of low-light images, followed by accurate segmentation using a U-Net model. By various experimental results, we show the performance metrics of the proposed low-light image segmentation algorithm and compare the proposed algorithm’s performance against several baseline models. Furthermore, we demonstrate the implementation of the proposed low-light image segmentation pipeline on an edge computing platform. The implementation results show that the proposed algorithm is sufficiently fast for real-time processing.