Mobile Information Systems

The technology of ocean monitoring is more advanced while the continuous development of industrial Internet. Unmanned underwater vehicle (UUV) is one of major ways for underwater environment monitoring, which makes high-precision positioning, and tracking of it is one of the key problems and needs to be solved urgently. An underwater acoustic positioning and tracking algorithm based on multiple beacons is proposed to reduce the positioning error of underwater acoustic positioning system caused by uncertain sound speed. The system consists of multiple GPS intelligent buoys floated on the sea surface and acoustic signal generator installed on the UUV. The effective sound speeds between the UUV and different buoys are considered to be unequal and estimated as the state parameters, together with the kinematic parameters of the UUV. Based on the kinematic equations of the UUV, the tracking model is obtained under the framework of the extended Kalman filter. Simulation results show that the proposed algorithm can correct the sound speed and improve the stability and accuracy of underwater acoustic positioning system.

Internet of Things (IoT) technology is one of the more advanced network communication technologies at present, and the IoT-based power dispatching system can improve the operation efficiency of power system. As the number of power users increases dramatically, higher requirements are put forward for the communication and management of IoT-based devices. The authentication method of traditional network structure can no longer adapt to the huge number of clients. For the problem of insufficient reliability of IPoE authentication method at the present stage, this paper proposes a model to enhance the reliability of IPoE authentication method. First, an intelligent scheduling optimization method based on SDH optical transmission technology is designed to complete the optimization of the scheduling network. The time delay of the communication network is reduced, and the reliability of the network is increased. Second, a data online collection module is established to complete the first optimization of the communication network. Finally, SDH optical transmission technology and design communication network terminal are integrated. The second optimization of the communication network is completed. After experimental testing, the proposed model can intelligently optimize the IoT-based power dispatching network. A standardized, manageable, and secure large-scale remote dispatching solution is realized.

In recent few years, flying ad hoc networks are utilized more for interconnectivity. In the topological scenario of FANETs, IoT nodes are available on ground where UAVs collect information. Due to high mobility patterns of UAVs cause disruption where intruders easily deploy cyberattacks like DoS/DDoS. Flying ad hoc networks use to have UAVs, satellite, and base station in the physical structure. IoT-based UAV networks are having many applications which include agriculture, rescue operations, tracking, and surveillance. However, DoS/DDoS attacks disturb the behaviour of entire FANET which lead to unbalance energy, end-to-end delay, and packet loss. This research study is focused about the detail study of machine learning-based IDS. Also, cognitive lightweight-LR approach is modeled using UNSW-NB 15 dataset. IoT-based UAV network is introduced using machine learning to detect possible security attacks. The queuing and data traffic model is utilized to implement DT, RF, XGBoost, AdaBoost, Bagging and logistic regression in the environment of IoT-based UAV network. Logistic regression is the proposed approach which is used to estimate statistical possibility. Overall, experimentation is based on binomial distribution. There exists linear association approach in logistic regression. In comparison with other techniques, logistic regression behaviour is lightweight and low cost. The simulation results presents logistic regression better results in contrast with other techniques. Also, high accuracy is balanced well in optimal way.

With the development of neurological and brain science, human beings have more understanding of the memory mechanism of the brain. Therefore, using the memory mechanism of the brain to store and retrieve images is one of the most popular research fields in the world. Memory is an important part of the human cognitive system, and it is the basis for the realization of higher-level cognitive activities. Human perception and memory are closely related. If people lose the ability to perceive, then people’s memory function will not be able to display. The current storage and extraction of brain information are mostly based on mathematical principles, without considering the memory mechanism in the brain, so the correctness and effectiveness of these methods are not high. Therefore, this study adopts an intelligent algorithm based on PCNN to denoise, segment, identify, and retrieve images. On this basis, a new learning method is adopted. This method can realize online incremental learning and can realize the storage of brain image data without predetermining the size and structure of the network. And when necessary, the information is extracted or not based on the distance between the detected versus the stored data. The test shows that when the number of images is 25, the present technique has an accuracy of 100% and the time required is 2345 s. Compared with the median filtering method, the efficiency of the present technique is greater.

The research on Internet of Things (IoT) network and edge computing has been a research hotspot in both industry and academia in recent years, especially for the ambient intelligence and massive communication. As a typical form of IoT network and edge computing, the intelligent perception vocal music singing learning system has attracted the attention of researchers in education and academia. Piano teaching is an important course for music majors in higher education. Strengthening piano teaching can cultivate outstanding piano talents for the country and promote the development of music art. This paper applies IoT perception technology to piano teaching, constructs an intelligent piano teaching system, and uses edge computing algorithms to accurately deploy sensors into the system by exploiting the ambient intelligence and massive communication. The system includes data acquisition, data perception, data monitoring, and other modules, making piano teaching more humanized and intelligent. Experiments show that the research in this paper provides important guidance for the application of IoT networks and edge computing, especially for the ambient intelligence and massive communication.

The intelligent sensing and communication technology in the airports’ grid information system provides a multidimensional big data set for analyzing flight delays. These data from air traffic control, weather, and multiple determinants will cause initial flight delays. Due to the influence of adjacent flight time correlation, the initial delay causes the delay of subsequent flights, discovered by mining information sensing data, forming the phenomenon of flight delay diffusion. Different determinants will lead to the delay diffusion form of different regions, and more seriously, it will lead to “disaster area” delay in the whole regional grid information structure. To analyze the spatial impact of each factor on flight delay and explore the regional distribution of delay determinants, this paper combined the spatial regression model and determined the key explanatory variables by statistical and processing of the aviation system data. The case study showed the spatial airport delay characteristics in terms of aircraft movements in China. After processing intelligent sensing and communication data, the results show that there is a spatial effect between airports in terms of delay and determinants. The high-delay clusters of delay constraints principally occurred in the Beijing-Tianjin-Hebe and Yangtze River Delta urban agglomerations. Direct flights, weather, new flight routes, take-off, and landing capacity have a more critical impact on spatial airport delays. The use of Internet of Things technology to perceive, analyze, and integrate multiple information of airport delay and combine spatial analysis models can accurately mine delay characteristics and effectively achieve digital and intelligent flight delay management.