Wireless Sensor Networks (WSNs), as a crucial component of the Internet of Things infrastructure, are widely applied in fields such as environmental monitoring, industrial control, and security surveillance. However, due to the limited and irreplaceable energy of sensor nodes, energy efficiency remains a core challenge in WSN research. To address this issue, a Multi-Objective Butterfly Clustering Optimization routing Algorithm (MBCO) is proposed. This algorithm innovatively combines butterfly foraging behavior with dynamic clustering, optimizing cluster head selection by simulating both the dispersive and centralized foraging behaviors of butterflies. An adaptive weight clustering mechanism based on node density and residual energy is designed to achieve network load balancing. A hybrid intra-cluster data fusion strategy is proposed, dynamically adjusting data aggregation methods according to the urgency of events. Additionally, a cross-cluster coordination mechanism is introduced to support inter-cluster load migration and resource sharing. Simulation results show that MBCO, while ensuring quality of service, significantly reduces energy consumption by 6.69 J, extends the useful life of the network by 83.05 rounds, increases packet delivery rate by 5.1%, and decreases communication delay by 67.34 ms compared to the relative average values of FDAM, EOMR-X, and EE-MO. This greatly enhances energy efficiency and provides a new paradigm for large-scale wireless sensor network deployments.

To address the issues of battery dependence, energy consumption, and energy management imbalance in industrial wireless sensor networks (IWSNs), this study proposes a green and energy-saving multiobjective clustering scheme to improve network efficiency and reduce environmental pollution. Given that conventional methods struggle to effectively optimize IWSN clustering, this article specifically designs a novel multiobjective clustering model. During the optimization process, this model comprehensively considers four key performance indicators: 1) total remaining energy, 2) average network delay; 3) average network packet loss rate; and 4) average distance from cluster heads (CHs) to the base station (BS), achieving holistic optimization of network performance. To further enhance clustering efficiency and network stability, this article also introduces a green energy-saving scheme based on the chaotic elite clone elephant herding optimization algorithm (i.e., CECEHO-GCS). This scheme ingeniously incorporates chaos operators in the initialization stage to enrich solution diversity and introduces clone and elite operators in the evolution stage, aiming to retain superior solutions and significantly enhance the algorithm’s search capabilities. Through comparative experiments with four existing advanced clustering schemes: 1) LEACH-C; 2) LEACH-R; 3) ESCVAD; and 4) ARSH-FATI-CHS, the model and the algorithm proposed in this article, CECEHO-GCS, demonstrate significant advantages in improving network energy efficiency and service quality. Specifically, CECEHO-GCS has achieved an improvement of at least 19.27% in network lifespan and at least 16.89% in data throughput, opening up new avenues for green energy conservation and sustainable development in IWSNs.

As Industry 4.0 rapidly advances, optimizing energy and performance in industrial wireless sensor networks (IWSNs) for the Industrial Internet of Things (IIoT) is critical. Addressing the NP-hard challenge of IWSN clustering, this article introduces a novel multiobjective model to balance energy consumption and enhance service quality. Based on this model, a new energy-saving cluster routing protocol called CNCMSA-ECRP was designed. The protocol comprehensively considers four key indicators: 1) total residual energy; 2) average network delay; 3) average network packet loss rate; and 4) average distance from cluster heads (CHs) to the base station (BS), with the goal of achieving overall optimization of network performance. The CNCMSA-ECRP protocol employs our newly proposed chaotic niching clone moth swarm algorithm (CNCMSA). The algorithm introduces chaotic mapping operators in the initialization phase and clonal and niching operators in the evolution phase, enhancing the search capability and diversity of the algorithm, as well as improving clustering efficiency and network stability. Comparative experiments with four existing advanced clustering schemes—LEACH-C, EEHCHR, OptGACHE, and ESCVAD—demonstrate that CNCMSA-ECRP achieves at least a 16.38% increase in network lifespan and significantly reduces average data transmission delay and packet loss rate by at least 1.84% and 17.76%, respectively. This research provides a novel solution to the clustering routing problem in IWSN.

Accurate detection of blooming roses and flower buds is essential for greenhouse phenotyping, cultivation scheduling, harvest planning, and yield management. However, UAV-derived greenhouse imagery presents major challenges because rose targets are often small, densely distributed, partially occluded, and visually similar to complex backgrounds. This study proposes a lightweight rose detection framework that combines multi-source dataset construction with an improved YOLOv12n-based detector, termed BloomRoseNet. A GreenHouse Rose dataset was constructed by integrating self-collected UAV overhead images, screened RoseTracker images, and supplementary multi-view rose images to increase diversity in scale, growth stage, viewpoint, and background complexity. BloomRoseNet introduces task-oriented improvements for fine-grained feature extraction, adaptive feature fusion, and attention-enhanced detection. The supplementary multi-view data improved precision, recall, and mAP@50 from 85.2%, 82.8%, and 89.2% to 86.1%, 85.3%, and 90.5%, respectively. Compared with the baseline YOLOv12n, BloomRoseNet increased precision, recall, mAP@50, and mAP@50:95 by 3.2, 3.3, 3.6, and 1.6 percentage points, respectively, while reducing parameters from 2.55 M to 2.08 M and model size from 5.5 MB to 4.5 MB. The model also maintained real-time inference capability and stronger robustness under blur, occlusion, and illumination disturbances. The proposed framework provides an effective and practical solution for UAV-based greenhouse rose monitoring and supports precision cultivation management.