Wireless sensor network (WSN) is a kind of self-organized network that plays a critical role in the rescue and relief work of some unattended areas in recent years. With the application background of unattended areas, a positioning algorithm with high accuracy and coverage in the WSN is presented in this paper. First, in view of the complicated environment in unattended areas and demand for accurate positioning, this paper focuses on the ranging-based positioning technology, introduces double-hop ranging in the Euclidean algorithm into the 3D ranging process at the node, and proposes 3DL positioning algorithm on this basis. This proposed algorithm is able to achieve 3D positioning of network nodes and effectively enlarge the network positioning coverage by upgrading the positioned node with adequate accuracy to proxy anchor. Second, since unattended areas are mostly non-line-of-sight (NLOS) environments and node positioning accuracy is affected by NOLS factors, a 3DL-N positioning algorithm is proposed by introducing the way of making up for node positioning error based on the 3DL algorithm. The algorithm makes use of the relative positioning function of anchors to figure out the ranging correction parameter and node positioning error compensation value and adds it to the positioning process of the common to further improve its positioning accuracy. Finally, the simulation results indicate that the positioning algorithm proposed in this paper enjoys significant strengths in terms of positioning accuracy and coverage on the premise of consuming no higher node energy.

The targets in the aerial view of UAVs are small, scenes are complex, and background noise is strong. Additionally, the low computational capability of UAVs is challenged when trying to meet the requirements of large neural networks. Therefore, a lightweight object detection algorithm tailored for UAV platforms, called RSG-YOLO, is proposed. The algorithm introduces an attention module constructed with receptive field attention and coordinate attention, which helps reduce background noise interference while improving long-range information dependency. It also introduces and refines a fine-grained downsampling structure to minimize the loss of target information during the downsampling process. A general efficient layer aggregation network enhances the base feature extraction module, improving gradient flow information. Additionally, a detection layer rich in small target information is added, while redundant large object detection layers are removed, achieving a lightweight design while enhancing detection accuracy. Experimental results show that, compared to the baseline algorithm, the improved algorithm increases the P, R, mAP@0.5, and mAP@0.5:0.95 by 6.9%, 7.2%, 8.4%, 5.8%, respectively, on the VisDrone 2019 dataset, and by 5.7%, 9%, 9.3%, 3.6%, respectively, on the TinyPerson dataset, while reducing the number of parameters by 23.3%. This significantly enhances the model’s detection performance and robustness, making it highly suitable for object detection tasks on low-computing-power UAV platforms.

Topology control technology is one of the key technologies in wireless sensor network (WSN). To solve the problem of excessive energy consumption of single-hop communication between cluster head and coordinator in WSN after clustering, a cluster head multi-hop topology control algorithm based on geographical location is presented in this paper. This algorithm first divides the whole region into a number of radial fan regions centered on the base station. Each cluster head judges its own fan region by its geographical location and searches the base station in the same sector area to find its upper hop cluster head to form a multi-hop backbone network to communicate with the base station. Thus, the energy consumption pressure of cluster heads far away from base stations can be alleviated and the network lifetime can be prolonged. The simulation results show that the algorithm has obvious advantages in balancing energy consumption of network nodes and prolonging network lifetime.

Single-object tracking in complex scenes faces challenges such as drastic target scale variation and strong background interference. To address these issues, an object tracking algorithm based on multi-scale attention and adaptive fusion is proposed. The method integrates a multi-scale attention module and an adaptive gated fusion module, enabling the adaptive mining of key features and dynamic adjustment of fusion weights across multi-level features. This effectively highlights target regions, suppresses redundant information, and enhances the model’s discriminative capability and robustness under complex backgrounds and occlusion. Experiments are conducted on the OTB100 and UAV123 datasets. Results show that, compared with the baseline model, the proposed algorithm improves the success rate and precision by 1.9% and 3.3%, respectively, on OTB100, and by 2.9% and 3.5%, respectively, on UAV123. Moreover, it achieves superior performance when facing typical challenging attributes such as occlusion, scale variation, and background clutter. In summary, the proposed algorithm enhances both tracking accuracy and robustness, offering a viable approach for object tracking under complex conditions.