A novel network paradigm of edge computing, namely TWSNs (Two-tiered Wireless Sensor Networks), has just been proposed by researchers in recent years for its high scalability and robustness. However, in the TWSNs-based edge computing systems, the storage nodes, which are located at the upper layer of the systems, are prone to be attacked by the adversaries because they play a key role in bridging sensor nodes and Sink, which may lead to the disclosure of all the data stored on them as well as some other potentially devastating results. In this paper, we study the integrity-and-privacy preservation problem for spatial-temporal Top-k queries in the TWSNs-based edge computing systems, and propose a sequence-encryption-based lightweight scheme named SLS-STQ (Sequence-encryption-based Lightweight Scheme for Securing Spatial-temporal Top-k Queries) to solve the problem. In SLS-STQ, three algorithms, namely the report preparation algorithm, the query processing algorithm and the integrity verification algorithm, are designed for the sensor nodes, the storage nodes and Sink respectively. Theoretical analysis shows that SLS-STQ is able to achieve both the integrity validation and the privacy preservation with low computation complexity, and simulation results show that SLS-STQ is much more efficient than the related state-of-the-art schemes.

Because traditional correlation pattern recognition algorithm cannot accurately identify low-resolution images, a low-resolution image correlation pattern recognition algorithm based on block-centric symmetric local binary model and weighted principal component analysis algorithm is proposed. Firstly, the feature of low-resolution image is extracted by using block CS-LBP operator. The weighted PCA operator is used to reduce the feature, and the classification feature is obtained. Secondly, the optimal classification class is selected by the nearest neighbor classifier, the recognition rate is calculated, and the whole algorithm is analyzed. Finally, the experiment is carried out. The experimental results showed that the algorithm has good recognition effect on ORL, and it is robust to the change of light and weather.

Tree growth potential is crucial for maintaining forest health and sustainable development. Traditional expert-based assessments of growth potential are inherently subjective. To address this subjectivity and improve accuracy, this study proposed a method of using Backpropagation Neural network (BPNN) to classify tree growth potential. 60 Pinus tabulaeformis (Carr.) and 60 Platycladus orientalis (Linn.) were selected as experimental trees in the Miyun Reservoir Water Conservation Forest Demonstration Zone in Beijing, and 95 Pinus massoniana (Lamb.) and 60 Cunninghamia lanceolate (Linn.) were selected as experimental trees in the Jigongshan Nature Reserve. The average annual ring width of the outermost 2cm xylem of the experimental trees were measured by discs or increment cores, and the wood volume increment of each experimental trees in recent years were calculated. According to wood volume increment, the growth potential of experimental trees was divided into three levels: strong, medium, and weak. Using tree height, breast height diameter, average crown width as input variables, using growth potential level as output variables, four sub models for each tree species were established; Using tree species, tree height, breast height diameter, average crown width as input variables, using growth potential level as output variables, a generalized model was established for these four tree species. The test results showed that the accuracy of the sub models for Pinus tabulaeformis, Platycladus orientalis, Pinus massoniana, and Cunninghamia lanceolate were 68.42%, 77.78%, 86.21%, and 78.95%, respectively, and the accuracy of the generalized model was 71.19%. These findings suggested that employing BPNN is a viable approach for accurately estimating tree growth potential.