With the advent of the Internet of Things, the security of the network layer in the Internet of Things is getting more and more attention. Traditional intrusion detection technologies cannot be well adapted in the complex Internet environment of the Internet of Things. Therefore, it is extremely urgent to study the intrusion detection system corresponding to today's Internet of Things security. This paper presents an intrusion detection model based on improved Genetic Algorithm and Deep Belief Network. Facing different types of attacks, through multiple iterations of the GA, the optimal number of hidden layers and number of neurons in each layer are generated adaptively, so that the intrusion detection model based on the DBN achieves a high detection rate. Finally, the NSL-KDD dataset was used to simulate and evaluate the model algorithm. Experimental results show that the improved intrusion detection model combined with DBN can effectively improve the recognition rate of intrusion attacks and reduce the complexity of the network.

Due to the problems of high bit error rate and delay, low bandwidth and limited energy of sensor nodes in underwater acoustic sensor network (UASN), it is particularly important to design a routing protocol with high reliability, strong robustness, low end-to-end delay and high energy efficiency which can flexibly be employed in dynamic network environment. Therefore, a reinforcement learning-based opportunistic routing protocol (RLOR) is proposed in this paper by combining the advantages of opportunistic routing and reinforcement learning algorithm. The RLOR is a kind of distributed routing approach, which comprehensively considers nodes’ peripheral status to select the appropriate relay nodes. Additionally, a recovery mechanism is employed in RLOR to enable the packets to bypass the void area efficiently and continue to forward, which improves the delivery rate of data in some sparse networks. The simulation results show that, compared with other representative underwater routing protocols, the proposed RLOR performs well in end-to-end delay, reliability, energy efficiency and other aspects in underwater dynamic network environments.

Due to the high-energy efficiency and scalability, the clustering routing algorithm has been widely used in wireless sensor networks (WSNs). In order to gather information more efficiently, each sensor node transmits data to its Cluster Head (CH) to which it belongs, by multi-hop communication. However, the multi-hop communication in the cluster brings the problem of excessive energy consumption of the relay nodes which are closer to the CH. These nodes’ energy will be consumed more quickly than the farther nodes, which brings the negative influence on load balance for the whole networks. Therefore, we propose an energy-efficient distributed clustering algorithm based on fuzzy approach with non-uniform distribution (EEDCF). During CHs’ election, we take nodes’ energies, nodes’ degree and neighbor nodes’ residual energies into consideration as the input parameters. In addition, we take advantage of Takagi, Sugeno and Kang (TSK) fuzzy model instead of traditional method as our inference system to guarantee the quantitative analysis more reasonable. In our scheme, each sensor node calculates the probability of being as CH with the help of fuzzy inference system in a distributed way. The experimental results indicate EEDCF algorithm is better than some current representative methods in aspects of data transmission, energy consumption and lifetime of networks.

Due to their special environment, Underwater Wireless Sensor Networks (UWSNs) are usually deployed over a large sea area and the nodes are usually floating. This results in a lower beacon node distribution density, a longer time for localization, and more energy consumption. Currently most of the localization algorithms in this field do not pay enough consideration on the mobility of the nodes. In this paper, by analyzing the mobility patterns of water near the seashore, a localization method for UWSNs based on a Mobility Prediction and a Particle Swarm Optimization algorithm (MP-PSO) is proposed. In this method, the range-based PSO algorithm is used to locate the beacon nodes, and their velocities can be calculated. The velocity of an unknown node is calculated by using the spatial correlation of underwater object’s mobility, and then their locations can be predicted. The range-based PSO algorithm may cause considerable energy consumption and its computation complexity is a little bit high, nevertheless the number of beacon nodes is relatively smaller, so the calculation for the large number of unknown nodes is succinct, and this method can obviously decrease the energy consumption and time cost of localizing these mobile nodes. The simulation results indicate that this method has higher localization accuracy and better localization coverage rate compared with some other widely used localization methods in this field.

Synthetic Aperture Radar (SAR) target detection is a significant research direction in radar information processing. Aiming at the poor robustness and low detection accuracy of traditional detection algorithms, SAR image target detection based on the Convolutional Neural Network (CNN) is reviewed in this paper. Firstly, the traditional SAR image target detection algorithms are briefly discussed, and their limitations are pointed out. Secondly, the CNN’s network principle, basic structure, and development process in computer vision are introduced. Next, the SAR target detection based on CNN is emphatically analyzed, including some common data sets and image processing methods for SAR target detection. The research status of SAR image target detection based on CNN is summarized and compared in detail with traditional algorithms. Afterward, the challenges of SAR image target detection are discussed and future research is proposed. Finally, the whole article is summarized. By summarizing and analyzing prior research work, this paper is helpful for subsequent researchers to quickly recognize the current development status and identify the connections between various detection algorithms. Beyond that, this paper summarizes the problems and challenges confronting researchers in the future, and also points out the specific content of future research, which has certain guiding significance for promoting the progress of SAR image target detection.