Wireless sensor networks equipped with rechargeable batteries are useful for outdoor environmental monitoring. However, the severe energy constraints of the sensor nodes present major challenges for long-term applications. To achieve sustainability, solar cells can be used to acquire energy from the environment. Unfortunately, the energy supplied by the harvesting system is generally intermittent and considerably influenced by the weather. To improve the energy efficiency and extend the lifetime of the networks, we propose algorithms for harvested energy prediction using environmental shadow detection. Thus, the sensor nodes can adjust their scheduling plans accordingly to best suit their energy production and residual battery levels. Furthermore, we introduce clustering and routing selection methods to optimize the data transmission, and a Bayesian network is used for warning notifications of bottlenecks along the path. The entire system is implemented on a real-time Texas Instruments CC2530 embedded platform, and the experimental results indicate that these mechanisms sustain the networks’ activities in an uninterrupted and efficient manner.

Metal surface defect detection has been a challenge in the industrial field. The current metal surface defect algorithms target only at a few types of defects and fail to perform well on defects with different scales. In this paper, a large number of metal surface defects are studied based on GC10-DET data set. An improved yolov5 detection network is designed targeting defects of various scales, especially of small-scaled objects, using a specific data enhancement method to regularize and an effective loss function to address data imbalance caused by small-scaled object defects. Finally, the comparative experiment on GC10-DET data set proves the major improvements on accuracy superiority of the proposed method.

Longan (Dimocarpus longan Lour.) is an important economic crop widely planted in tropical and subtropical regions, and flower and fruit development play decisive effects on the longan yield and fruit quality formation. MCM1, AGAMOUS, DEFICIENS, Serum Response Factor (MADS)-box transcription factor family plays important roles for the flowering time, floral organ identity, and fruit development in plants. However, there is no systematic information of MADS-box family in longan. In this study, 114 MADS-box genes were identified from the longan genome, phylogenetic analysis divided them into type I (Mα, Mβ, Mγ) and type II (MIKC*, MIKCC) groups, and MIKCC genes were further clustered into 12 subfamilies. Comparative genomic analysis of 12 representative plant species revealed the conservation of type II in Sapindaceae and analysis of cis-elements revealed that Dof transcription factors might directly regulate the MIKCC genes. An ABCDE model was proposed for longan based on the phylogenetic analysis and expression patterns of MADS-box genes. Transcriptome analysis revealed that MIKCC genes showed wide expression spectrums, particularly in reproductive organs. From 35 days after KClO3 treatment, 11 MIKC genes were up-regulated, suggesting a crucial role in off-season flower induction, while DlFLC, DlSOC1, DlSVP, and DlSVP-LIKE may act as the inhibitors. The gene expression patterns of longan fruit development indicated that DlSTK, DlSEP1/2, and DlMADS53 could be involved in fruit growth and ripening. This paper carried out the whole genome identification and analysis of the longan MADS-box family for the first time, which provides new insights for further understanding its function in flowers and fruit.

Indoor localization based on wireless sensor networks (WSNs) is an important field of research with numerous applications, such as elderly care, miner security, and smart buildings. In this paper, we present a localization method based on the received signal strength difference (RSSD) to determine a target on a map with unknown transmission information. To increase the accuracy of localization, we propose a confidence value for each anchor node to indicate its credibility for participating in the estimation. An automatic calibration device is designed to help acquire the values. The acceleration sensor and unscented Kalman filter (UKF) are also introduced to reduce the influence of measuring noise in the application. Energy control is another key point in WSN systems and may prolong the lifetime of the system. Thus, a quadtree structure is constructed to describe the region correlation between neighboring areas, and the unnecessary anchor nodes can be detected and set to sleep to save energy. The localization system is implemented on real-time Texas Instruments CC2430 and CC2431 embedded platforms, and the experimental results indicate that these mechanisms achieve a high accuracy and low energy cost.

Wireless sensor networks are widely used to acquire environmental parameters to support agricultural production. However, data variation and noise caused by actuators often produce complex measurement conditions. These factors can lead to nonconformity in reporting samples from different nodes and cause errors when making a final decision. Data fusion is well suited to reduce the influence of actuator-based noise and improve automation accuracy. A key step is to identify the sensor nodes disturbed by actuator noise and reduce their degree of participation in the data fusion results. A smoothing value is introduced and a searching method based on Prim’s algorithm is designed to help obtain stable sensing data. A voting mechanism with dynamic weights is then proposed to obtain the data fusion result. The dynamic weighting process can sharply reduce the influence of actuator noise in data fusion and gradually condition the data to normal levels over time. To shorten the data fusion time in large networks, an acceleration method with prediction is also presented to reduce the data collection time. A real-time system is implemented on STMicroelectronics STM32F103 and NORDIC nRF24L01 platforms and the experimental results verify the improvement provided by these new algorithms.