Cognitive wireless sensor network (CWSN) is a combination of cognitive radio and wireless sensor networks (WSNs), which can effectively solve spectrum resource scarcity problem in WSNs. As one of the key technologies in CWSNs, spectrum sensing enables sensors to detect and opportunistic access underutilized idle licensed bands, which remarkably improves the spectrum utilization and system performance. And cooperative spectrum sensing (CSS) has emerged to enhance detection performance of spectrum sensing. Focusing on CSS schemes in CWSNs, this paper explores the cooperative and noncooperative schemes comprehensively. The characteristics of the existing CSS schemes are analyzed in CWSNs. And then, according to the behavior of cognitive sensors, CSS techniques are classified into noncooperative and cooperative schemes. Concentrating on energy consumption, cooperative CSS schemes are classified into three categories: censoring, clustering, and sensor selection. The performance of three kinds of CSS schemes is compared and analyzed thoroughly. Finally, the difficulties and challenges of CSS scheme are addressed in CWSNs.

Internet of vehicles (IoV) enables a large number of applications for consumers by integrating massive electronics, which aggravates the spectrum scarcity and data security, and degrades the quality of services (QoS) and experiences of consumers obviously. Focusing on enhancing the security and improving spectrum utilization, this article proposes a contract-theory-based secure spectrum sharing framework, which adopts the consortium blockchain to guarantee security of spectrum sharing and utilizes the practical Byzantine fault tolerant consensus algorithm to achieve consensus in IoV. Smart contracts are then formed based on insurance contracts to promote primary users to share their idle spectrum resources. A preference-based spectrum allocation algorithm is designed to maximize the total utility of secondary users (SUs). Simulation results show that, compared with the existing schemes, the proposed scheme achieves better performance in terms of the utility of SUs and the spectrum utilization greatly, which provides more access opportunities to consumers in IoV.

To improve query performance and space efficiency, an efficient random access blocked inverted index (RABI) is proposed. RABI divides an inverted list into blocks and compresses different part of each block with the corresponding encoding method to decrease space consumption. RABI can provide fast addressing and random access functions on the compressed blocked inverted index with the novel hybrid compression method, which can provide both block level and inner block level skipping function and further enhance both space and time efficiencies without inserting any additional auxiliary information. Experimental results show that RABI achieves both high space efficiency and search efficiency, and outperforms the existing approach significantly.

The Agricultural Digital Twin Network (ADTN) represents one of the key enabling technologies of Agriculture 4.0. However, the unstructured nature of agricultural scenarios poses significant challenges to the geometric fidelity and scalability of ADTNs, as the morphological diversity and entity uniqueness of crops further reduce the rendering accuracy of virtual entities and increase modeling costs. Existing research lacks low-cost solutions for digital twin generation in unstructured agricultural environments, as implicit-based models struggle to capture global invariant features critical to crop structure and texture. To address these challenges, a crop-based ADTN (CBDTN) framework is designed, which incorporates low-cost image acquisition terminals, edge devices, and cloud platforms, thereby optimizing resource utilization through implicit modeling and minimizing costs in resource constrained scenarios. Furthermore, to improve the accuracy of virtual entity generation, a texture prior feature-enabled neural radiance field (TPFNeRF) is proposed, inspired by the human visual system. It emulates the processing mechanism of the visual cortex to extract consistent prior features from multi-view images, thereby providing robust rendering guidance that enhances perceptual fidelity in 3D reconstruction tasks. The experimental results show that the CBDTN with TPFNeRF exhibits superior performance in generating objects with complex textures and structures. In synthetic dataset, it significantly improves reconstruction quality, achieving 6.52 dB PSNR and 4.3% SSIM improvements over the baseline, and delivers overall performance superior to recent methods such as GFB-NeRF and DiSRNeRF. In real-world scenarios, TPFNeRF effectively reconstructs object geometry and color from fixed viewpoints using only 5 MB of model weights, significantly reducing CBDTN deployment costs.