SummaryWith the advent of the Internet of Things (IoT), the count of gadgets connected to the Internet has been increased. IoT, as a modern paradigm, has been used to describe the future in which physical things like RFID tags, sensors, actuators, and cellphones can intermingle for achieving shared purposes. Also, we can employ cloud computing for storing the things' information in the IoT. However, this information has been replicated through the network for increasing availability. In this paper, due to the NP‐hard nature of the replica selection problem, an improved version of ant colony optimization (IACO) has been applied. The impact of pheromone on the chosen path is converted by ants to invert the underlying logic of ACO. Due to the existence of different IoT centers, the IACO has been employed for selecting the replicated data in the IoT where the load balancing among IoT centers has been considered. In this method, an ant chooses the ideal point for its movement; then others may not pass the track that the preceding ants have been passed. The obtained outcomes have shown that the method has outperformed the ACO, HQFR, and RTRM approaches regarding the waiting time and load balancing.

Multi-objective optimization and gray association are classical technologies in intelligent system. In this paper, a novel multi-focus image fusion method based on the technology is proposed. First, the input images are decomposed into lowfrequency subband and high-frequency subbands using shift invariant Shearlet transform. Second, low-frequency subbands are fused by the weighted average fusion rule. The weight can be adaptively obtained by vector-evaluated quantumbehaved particle swarm optimization and gray relation analysis. The similarity of a corresponding region-based fusion rule is proposed and used to integrate high-frequency subbands. Last but not least, fusion image is obtained by inverse SIST. Visual and statistical analyses demonstrate that the fusion quality can be significantly improved by the proposed method.

For diagnosis models with one response variable influenced by multiple factors, the paper proposes a method that finds the greatest impact factor, referred to as main factor. The method is based on statistical analysis and uses the principal component transformation to optimize statistics.It includes several steps: sampling, calculating and constructing the correlation matrix between response variables and factors, obtaining the most relevant matrix by principal component transformation, determining main factor by comparing the correlation degree of correlation matrices and the most relevant matrix. The algorithm can meet the need of many engineering problems that hunt for the greatest impact factor in non-linear diagnosis models with multiple factors.

This paper introduces the STUNT technology principles, describes the design and implementation process of the STUNT protocol to make TCP holes between two clients in different Local area Networks to achieve clients' connection, and then solves the communication problems in multiple NAT network.

This paper analyzes the composition of enterprise information system and service architecture. Combined with modern information technology and scientific methodology, the paper also discusses the demand of information organization and structure for enterprise information collaboration, and puts forword the structural organization and component-based collaborative model with structural methodology as a guide ,distributed component technology as the technical support , the dynamic demand of utilization as a direction.