Wireless sensor networks (WSNs) are widely used in military, traffic, medical and so on. The design of routing protocol for WSNs is limited by the single nature of the local topology information. Meanwhile, the power supply of sensor networks node, communication ability and storage capacity are limited, so how to improve the efficient energy of nodes and extend the networks life cycle is the focus of current research. This study proposes the improved algorithm for the LEACH (Low Energy Adaptive Clustering Hierarchy) clustering algorithm, considering the residual energy of the nodes and the factors of the long distance node, the T(n) is readjusted and the new method is proposed. Then the data fusion rate is introduced to allow the cluster-heads to fuse data before sending the data, and send the data to the base station. Finally, the free-space model and the multi-path fading model are adopted to avoid the excessive consumption of energy caused by the node d 4. The authors' simulation results show that the improved algorithm can reduce the energy consumption of the networks and prolongs life cycle.

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In recent years, artificial intelligence has played an increasingly important role in the field of automated control of drones. After AlphaGo used Intensive Learning to defeat the World Go Championship, intensive learning gained widespread attention. However, most of the existing reinforcement learning is applied in games with only two or three moving directions. This paper proves that deep reinforcement learning can be successfully applied to an ancient puzzle game Nokia Snake after further processing. A game with four directions of movement. Through deep intensive learning and training, the Snake (or self-learning Snake) learns to find the target path autonomously, and the average score on the Snake Game exceeds the average score on human level. This kind of Snake algorithm that can find the target path autonomously has broad prospects in the industrial field, such as: UAV oil and gas field inspection, Use drones to search for and rescue injured people after a complex disaster. As we all know, post-disaster relief requires careful staffing and material dispatch. There are many factors that need to be considered in the artificial planning of disaster relief. Therefore, we want to design a drone that can search and rescue personnel and dispatch materials. Current drones are quite mature in terms of automation control, but current drones require manual control. Therefore, the Snake algorithm proposed here to be able to find the target path autonomously is an attempt and key technology in the design of autonomous search and rescue personnel and material dispatching drones. INDEX TERMS Deep reinforcement learning, Markov decision, Monte Carlo, Q-learning. The associate editor coordinating the review of this manuscript and approving it for publication was Zhanyu Ma.

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Artificial intelligence technology plays an increasingly important role in human life. For example, distinguishing different people is an essential capability of many intelligent systems. To achieve this, one possible technical means is to perceive and recognize people by optical imaging of faces, so-called face recognition technology. After decades of research and development, especially the emergence of deep learning technology in recent years, face recognition has made great progress with more and more applications in the fields of security, finance, education, social security, etc. The field of computer vision has become one of the most successful branch areas. With the wide application of biometrics technology, bio-encryption technology came into being. Aiming at the problems of classical hash algorithm and face hashing algorithm based on Multiscale Block Local Binary Pattern (MB-LBP) feature improvement, this paper proposes a method based on Generative Adversarial Networks (GAN) to encrypt face features. This work uses Wasserstein Generative Adversarial Networks Encryption (WGAN-E) to encrypt facial features. Because the encryption process is an irreversible one-way process, it protects facial features well. Compared with the traditional face hashing algorithm, the experimental results show that the face feature encryption algorithm has better confidentiality.

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