Software-defined networking (SDN) separates the control plane from the data forwarding plane, realizes the flexible management of the network resources. With the explosive growth of network traffic and scale, multi-controllers need to be deployed to improve the scalability and reliability of the control plane. However, unreasonable subdomain partitioning of SDN controllers may cause the unbalanced distribution of controller loads, and reduces the communication performance of the network. Therefore, in this paper, a dynamic multi-controller deployment scheme based on load balancing is proposed. We transform the flow requests into a queuing model and consider traffic propagation delay and the capacity of controllers as two main factors affecting the deployment of the multi-controllers. In the initial static network, a modified affinity propagation algorithm (PSOAP) based on particle swarm optimization is proposed to solve the problem of clustering performance being affected by the initial values of the bias parameters and convergence coefficients, get the reasonable network planning. With the dynamic traffic network, switches in different sub-domain are reassigned by Breadth First Search (BFS) algorithm to achieve controller load balancing. Extensive evaluations demonstrate that the scheme can provide better stable, accurate and load balancing multi-controller deployment when compared with AP and genetic algorithms.

Remote sensing images have been widely used in the military and other areas because of their rich perceptional data. This makes their visual security critical for practical usage. To address this challenge, an enhanced image encryption scheme is proposed. In the scrambling phase, n bands of remote sensing images undergo Arnold double-bit-level permutation. This reduces not only the pixel correlation in each image plane but also between each frequency band.To enhance security, an RNA crossover rule (RNACMO) is introduced. The RNA image is divided into RNA single strands of different lengths using chaotic sequences, and different crossover methods, including single-point and uniform, are adaptively selected according to the number of RNA single strands. RNACMO significantly improves the security level of the scheme. An improved immune algorithm (IIA) is exploited to optimize chaotic function sequences, which improves the chaotic property of the scheme. In experiments, the proposed algorithm achieves average values of 99.6094% for NPCR, 33.4635% for UACI, and 26.7712% for BACI in encrypted remote sensing images, indicating stronger security and better resilience against attacks compared with other encryption algorithms for remote sensing images.

Time series models have been used to extract damage features in the measured structural response. In order to better extract the sensitive features in the signal and detect structural damage, this paper proposes a damage identification method that combines empirical mode decomposition (EMD) and Autoregressive Integrated Moving Average (ARIMA) models. EMD decomposes nonlinear and non-stationary signals into different intrinsic mode functions (IMFs) according to frequency. IMF reduces the complexity of the signal and makes it easier to extract damage-sensitive features (DSF). The ARIMA model is used to extract damage sensitive features in IMF signals. The damage sensitive characteristic value of each node is used to analyze the location and damage degree of the damaged structure of the bridge. Considering that there are usually multiple failures in the actual engineering structure, this paper focuses on analysing the location and damage degree of multi-damaged bridge structures. A 6-meter-long multi-destructive steel-whole vibration experiment proved the state of the method. Meanwhile, the other two damage identification methods are compared. The results demonstrate that the DSF can effectively identify the damage location of the structure, and the accuracy rate has increased by 22.98% and 18.4% on average respectively.