In response to sudden high-traffic signal demand caused by massive device access in urban Internet of Things environments, Unmanned Aerial Base Stations (UAV-BSs), as dynamic network nodes, can effectively enhance the coverage capacity and quality of communication network services. However, how to efficiently deploy UAV-BSs in complex urban environments while meeting the differentiated communication needs of common and special areas remains an urgent challenge. In this paper, we propose an Average Hamming Distance Modified Particle Swarm Optimization (AHDPSO) algorithm based on the similarity calculation of DNA sequences, which firstly matrices the position and velocity information, and then calculates the average Hamming distance between particles using DNA mapping sequences to identify ’outlier points’. Further, the search guidance coefficientc3is introduced to quantify the guiding effect of ’outlier points’ on the global search, and the values ofc1,c2, andc3are dynamically adjusted by combining with the chaotic mapping, so as to balance the exploratory and developmental capabilities of the algorithm. Compared with the original particle swarm optimization algorithm, matrix particle swarm optimization algorithm, and seven other improved particle swarm optimization algorithms, the experimental results show that AHDPSO can quickly converge to the optimal solution. Compared with the traditional PSO algorithm, the absolute improvement values in the coverage of the entire region and special regions are 7.82% and 7.29%, respectively. It also shows good stability in different scenarios, indicating that the proposed algorithm has significant advantages in convergence speed, coverage, and stability.

Person re-identification (Re-ID) is a critical technique in the video surveillance system, which has achieved significant success in the supervised setting. However, it is difficult to directly apply the supervised model to arbitrary unseen domains due to the domain gap between the available source domains and unseen target domains. In this paper, we propose a novel label distribution learning (LDL) method to address generalizable multi-source person Re-ID task (i.e., there are multiple available source domains, and the testing domain is unseen during training), which aims to explore the relation of different classes and mitigate the domain-shift across different domains so as to improve the discrimination of the model and learn the domain-invariant feature, simultaneously. Specifically, during the training process, we produce the label distribution via the online manner to mine the relation information of different classes, thus it is beneficial for extracting the discriminative feature. Besides, for the label distribution of each class, we further revise it to give more and equal attention to the other domains that the class does not belong to, which can effectively reduce the domain gap across different domains and obtain the domain-invariant feature. Furthermore, we also give the theoretical analysis to demonstrate that the proposed method can effectively deal with the domain-shift issue. Extensive experiments on multiple benchmark datasets validate the effectiveness of the proposed method and show that the proposed method can outperform the state-of-the-art methods. Besides, further analysis also reveals the superiority of the proposed method.