Internet of Things (IoT) is a huge network and establishes ubiquitous connections between smart devices and objects. The flourishing of IoT leads to an unprecedented data explosion, traditional data storing or processing techniques have the problem of low efficiency, and if the data is used maliciously, the security loss may be further caused. Multi-cloud is a highperformance secure computing platform, which combines multiple cloud providers for data processing, and the distributed multicloud platform ensures the security of data to some extent. Based on multi-cloud and task scheduling in IoT, this paper constructs a many-objective distributed scheduling model, which includes six objectives of total time, cost, cloud throughput, energy consumption, resource utilization, and balancing load. Further, this paper presents a many-objective intelligent algorithm with sine function to implement the model, which considers the variation tendency of diversity strategy in the population is similar to the sine function. The experimental results demonstrate excellent scheduling efficiency and hence enhancing the security. This work provides a new idea for addressing the difficult problem of data processing in IoT.

Analyzing the fast search and find of density peaks clustering (DPC) algorithm, we find that the cluster centers cannot be determined automatically and that the selected cluster centers may fall into a local optimum and the random selection of the parameter cut-off distance dc value. To overcome these problems, a novel clustering algorithm based on DPC & PSO (PDPC) is proposed. Particle swarm optimization (PSO) is introduced because of its simple concept and strong global search ability, which can find the optimal solution in relatively few iterations. First, to solve the effect of the selection of the parameter dc on the calculation density and the clustering results, this paper proposes a method to calculate that parameter. Second, a new fitness criterion function is proposed that iteratively searches K global optimal solutions through the PSO algorithm, that is, the initial cluster centers. Third, each sample is assigned to K initial center points according to the minimum distance principle. Finally, we update the cluster centers and redistribute the remaining objects to the clusters closest to the cluster centers. Furthermore, the effectiveness of the proposed algorithm is verified on nine typical benchmark data sets. The experimental results show that the PDPC can effectively solve the problem of cluster center selection in the DPC algorithm, avoiding the subjectivity of the manual selection process and overcoming the influence of the parameter dc. Compared with the other six algorithms, the PDPC algorithm has a stronger global search ability, higher stability and a better clustering effect.