We investigate the problem of path planning and task offloading for UAV clusters in a UAV-assisted edge computing scenario. UAVs autonomously make decisions regarding path planning, continuous service provision, and task offloading based on collected information. In this setting, terminal equipment (TE) cannot directly connect to servers; thus, UAVs act as both edge servers and communication relays, proactively providing services to TEs. We construct a fine-grained temporal scale model that decomposes UAV actions into atomic time units, transforming decision-making on specific behaviors into state transition decisions. This approach better accommodates the needs of time-varying environments. Regarding path planning, given the time-sensitive nature of TE requests, we focus on how to provide stable and timely computational services to TEs. We propose a multi-agent reinforcement learning algorithm capable of dynamically sensing task priorities to enhance Quality of Service (QoS), with optimizations made in terms of task completion rate, UAV energy consumption, and processing delay. About task offloading decision, we introduce a dual-keyword-based offloading algorithm to optimize the binary offloading process. Finally, we conduct simulation experiments to demonstrate the effectiveness of the proposed algorithms, and comparative experiments confirm their superiority.

Abstract Fires not only cause devastating consequences for human life and property, but also lead to soil erosion in forests. Therefore, it is necessary to design a novel algorithm that can quickly monitor smoke from fires. Most existing smoke segmentation methods do not consider the segmentation accuracy of algorithms under limited computational resources. To address this research gap, this paper proposes a lightweight smoke segmentation algorithm based on DeepLabV3+ that achieves fast inference speed and high accuracy for different sizes smoke. To reduce the number of parameters, the feature extraction network of the DeeplabV3+ algorithm is replaced by MobileNetV2, which enhances the extraction ability of the algorithm in segment smoke images. Then, the Convolutional Block Attention Module (CBAM) is added to the encoder part to enhance the perception of the algorithm for small smoke and effectively alleviates smoke mis‐segmentation. Furthermore, a newly designed loss function is used in the network. The experimental results show that the proposed method has improved by 1.27% in Smoke IoU and 1.21% in mPA compared with other methods. The weight size has been reduced to 10.76% of the original DeepLabV3+, and the inference time is only 33.71ms. Therefore, it is a more suitable early fire detection algorithm for resource‐constrained environments.

Abstract There are a lot of flammable materials in the textile workshop, and once a fire occurs, it will cause property damage and casualties. At present, smoke detection in textile workshops mainly relies on temperature‐sensing smoke sensors with low detection rate and poor real‐time performance, which cannot meet the task of smoke detection in complex environments. Therefore, this paper proposes an improved mixed Gaussian and YOLOv5 smoke detection algorithm for textile workshops. In order to reduce the interference of static background in smoke detection, an improved gaussian mixture algorithm is used to extract suspected smoke areas in video by using the dynamic characteristics of smoke. Then, an adaptive attention module is added to the feature pyramid infrastructure of the YOLOv5 target detection network to improve the multi‐scale target recognition ability. In addition, the focal loss function is used to reduce the impact of background and foreground class imbalances on the detection results. The experimental results show that the detection accuracy of the proposed method is 94.7%, and the average detection speed is 66.7 FPS. By comparing with the existing state‐of‐the‐art algorithms, the detection capability of this method has been significantly improved. At the same time, it has high real‐time performance and detection accuracy in smoke detection in textile workshops.