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Wireless sensor networks (WSN) have problems such as limited power, weak computing power, poor communication ability, and vulnerability to attack. However, the existing encryption methods cannot effectively solve the above problems when applied to WSN. To this end, according to WSN’s characteristics and based on the identity-based encryption idea, an improved identity-based encryption algorithm (IIBE) is proposed, which can effectively simplify the key generation process, reduce the network traffic, and improve the network security. The design idea of this algorithm lies between the traditional public key encryption and identity-based public tweezers’ encryption. Compared with the traditional public key encryption, the algorithm does not need a public key certificate and avoids the management of the certificate. Compared with identity-based public key encryption, the algorithm addresses the key escrow and key revocation problems. The results of the actual network distribution experiments demonstrate that IIBE has low energy consumption and high security, which are suitable for application in WSN with high requirements on security.

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The particle degradation problem of particle filter (PF) algorithm caused by reduction of particle weights significantly influences the positioning accuracy of target nodes in wireless sensor networks. This study presents a predictor to obtain the particle swarm of high quality by calculating non-linear variations of ranging between particles and flags and modifying the reference distribution function. To this end, probability variations of distances between particles and star flags are calculated and the maximum inclusive distance using the maximum probability of high-quality particle swarm is obtained. The quality of particles is valued by the Euclidean distance between the predicted and real observations, and hereafter particles of high quality are contained in spherical coordinate system using the distance as diameter. The simulation results show that the proposed algorithm is robust and the computational complexity is low. The method can effectively improve the positioning accuracy and reduce the positioning error of target nodes.

Forest fire recognition is important to the protection of forest resources. To effectively monitor forest fires, it is necessary to deploy multiple monitors from different angles. However, most of the traditional recognition models can only recognize single-source images. The neglection of multi-view images leads to a high false positive/negative rate. To improve the accuracy of forest fire recognition, this paper proposes a graph neural network (GNN) model based on the feature similarity of multi-view images. Specifically, the correlations (nodes) between multi-view images and library images were established to convert the input features of graph nodes into the correlation features between different images. Based on feature relationships, the image features in the library were updated to estimate the node similarity in the GNN model, improving the image recognition rate of our model. Furthermore, a fire area feature extraction method was designed based on image segmentation, aiming to simplify the complex preprocessing of images, and effectively extract the key features from images. By setting the threshold in the hue-saturation-value (HSV) color space, the fire area was extracted from the images, and the dynamic features were extracted from the continuous frames of the fire area. Experimental results show that our method recognized forest fires more effectively than the baselines, improving the recognition accuracy by 4%. In addition, the multi-source forest fire data experiment also confirms that our method could adapt to different forest fire scenes, and boast a strong generalization ability and anti-interference ability.

Unmanned ship navigates on the water in an autonomous or semiautonomous way, which can be widely used in maritime transportation, intelligence collection, maritime training and testing, reconnaissance, and evidence collection. In this paper, we use deep reinforcement learning to solve the optimization problem in the path planning and management of unmanned ships. Specifically, we take the waiting time (phase and duration) at the corner of the path as the optimization goal to minimize the total travel time of unmanned ships passing through the path. We propose a new reward function, which considers the environment and control delay of unmanned ships at the same time, which can reduce the coordination time between unmanned ships at the same time. In the simulation experiment, through the quantitative and qualitative results of deep reinforcement learning of unmanned ship navigation and path angle waiting, the effectiveness of our solution is verified.

Wireless sensor networks are widely used in smart environments to capture and detect the activities of human beings, and achieving reliable transmission between sensor nodes has become one of the main challenges of practical applications. This paper presents a scheme for path planning that is designed to achieve optimal coverage by using active nodes to periodically fill in the blank areas and to replace the failed nodes. This approach can effectively avoid uneven energy consumption while maintaining complete link states. Meanwhile, the curl field of the nodes is used to model the effects of the residual energy and the distance between nodes, thereby effectively relaxing the requirements on the spatial positions of the nodes. Experiments show that in the case of directional transmission, the proposed method demonstrates better performance than other algorithms in terms of the network lifecycle, coverage, and transmission reliability. This method can effectively address the problem of cross-node failure along the transmission paths in complex and dynamic networks.