At present, the existing methods have many limitations in small target detection, such as low accuracy, a high rate of false detection, and missed detection. This paper proposes the KPE-YOLOv5 algorithm aiming to improve the ability of small target detection. The algorithm has three improvements based on the YOLOv5 algorithm. Firstly, it achieves more accurate size of anchor-boxes for small targets by K-means++ clustering technology. Secondly, the scSE (spatial and channel compression and excitation) attention module is integrated into the new algorithm to encourage the backbone network to pay greater attention to the feature information of small targets. Finally, the capability of small target feature extraction is improved by increasing the small target detection layer, which also increases the detection accuracy of small targets. We evaluate KPE-YOLOv5 on the VisDrone-2020 dataset and compare performance with YOLOv5. The results show that KPE-YOLOv5 improves the detection mAP by 5.3% and increases the P by 7%. The KPE-YOLOv5 algorithm has better detection outcome than YOLOv5 for small target detection.

Complex Question Answering over Knowledge Graph (C-KGQA) seeks to solve complex questions using knowledge graphs. Currently, KGQA systems achieve great success in answering simple questions, while complex questions still present challenging issues. As a result, an increasing number of novel methods have been proposed to remedy this challenge. In this survey, we proposed two mainstream categories of methods for C-KGQA, which are divided according to their use for knowledge graph representation and construction, namely, graph metric (GM)-Based Methods and graph neural network (GNN)-based methods. Additionally, we also acknowledge the influence of ChatGPT, which has prompted further research into utilizing knowledge graphs as a knowledge source to assist in answering complex questions. We also introduced methods based on pre-trained models and knowledge graph joint reasoning. Furthermore, we have compiled research achievements from the past three years to make it easier for researchers with similar interests to obtain state-of-the-art research. Finally, we discussed the resources and evaluation methods for tackling C-KGQA tasks and summarized several research prospects in this field.

Wireless sensor networks are expected to automatically monitor the ecological evolution and wildlife habits in forests. Low-power links (transceivers) are often adopted in wireless sensor network applications, in order to save the precious sensor energy and then achieve long-term, unattended monitoring. Recent research has presented some performance characteristics of such low-power wireless links under laboratory or outdoor scenarios with less obstacles, and they have found that low-power wireless links are unreliable and prone to be affected by the target environment. However, there is still less understanding about how well the low-power wireless link performs in real-world forests and to what extent the complex in-forest surrounding environments affect the link performances. In this paper, we empirically evaluate the low-power links of wireless sensors in three typical different forest environments. Our experiment investigates the performance of the link layer compatible with the IEEE 802.15.4 standard and analyzes the variation patterns of the packet reception ratio (PRR), the received signal strength indicator (RSSI) and the link quality indicator (LQI) under diverse experimental settings. Some observations of this study are inconsistent with or even contradict prior results that are achieved in open fields or relatively clean environments and thus, provide new insights both into effectively evaluating the low-power wireless links and into efficiently deploying wireless sensor network systems in forest environments.

Unmanned aerial vehicles (UAVs) are a highly sought-after technology with numerous applications in both military and non-military uses. The identification of targets is a crucial aspect of UAV applications, but there are challenges associated with complex detection models and difficulty in detecting small targets. To address these issues, this study proposes the lightweight L-YOLO algorithm for target detection tasks from a UAV perspective. The L-YOLO algorithm improves on YOLOv5 by improving the model’s detection performance for small targets while reducing the number of parameters and computational effort. The GhostNet module replaces the relevant convolution in the YOLOv5 model to create a lightweight model. The EIoU loss is used as the loss function of the algorithm to accelerate convergence and improve regression accuracy. Furthermore, feature-level extensions based on YOLOv5 are implemented, and a new detection head is proposed to improve the model’s detection accuracy for small targets. The size of the anchor boxes is redesigned to suit the small targets using the K-means++ clustering algorithm. The experiments were conducted on the VisDrone-2022 dataset, and the L-YOLO algorithm demonstrated a reduction in computational effort by 42.42% and number of parameters by 48.6% compared to the original algorithm. Furthermore, recall and mAP@0.5 improved by 2.1% and 1.4%, respectively. These results demonstrate that the L-YOLO algorithm not only has better detection performance for small targets but is also a lighter model, indicating promising prospects for target detection from a UAV perspective.

In emerging energy-harvesting wireless sensor networks (EH-WSN), the sensor nodes can harvest environmental energy to drive their operation, releasing the user’s burden in terms of frequent battery replacement, and even enabling perpetual sensing systems. In EH-WSN applications, usually, the node in energy-harvesting or recharging state has to stop working until it completes the energy replenishment. However, such temporary departures of recharging nodes severely impact the packet routing, and one immediate result is the routing loop problem. Controlling loops in connectivity-intermittent EH-WSN in an efficient way is a big challenge in practice, and so far, users still lack of effective and practicable routing protocols with loop handling. Based on the Collection Tree Protocol (CTP) widely used in traditional wireless sensor networks, this paper proposes a loop-aware routing protocol for real-world EH-WSNs, called La-CTP, which involves a new parent updating metric and a proactive, adaptive beaconing scheme to effectively suppress the occurrence of loops and unlock unavoidable loops, respectively. We constructed a 100-node testbed to evaluate La-CTP, and the experimental results showed its efficacy and efficiency.