A security communication model based on certificateless online/offline signcryption for Internet of Things

At present, hospitals in our country have basically established the HIS system, which manages registration, treatment, and charge, among many others, of patients. During treatment, patients need to use medical devices repeatedly to acquire all sorts of inspection data. Currently, the output data of the medical devices are often manually input into information system, which is easy to get wrong or easy to cause mismatches between inspection reports and patients. For some small hospitals of which information construction is still relatively weak, the information generated by the devices is still presented in the form of paper reports. When doctors or patients want to have access to the data at a given time again, they can only look at the paper files. Data integration between medical devices has long been a difficult problem for the medical information system, because the data from medical devices lack mandatory unified global standards and have outstanding heterogeneity of devices. In order to protect their own interests, manufacturers use special protocols, etc., thus causing medical devices to still be the "lonely island" of hospital information system. Besides, unfocused application of the data will lead to failure to achieve a reasonable distribution of medical resources. With the deepening of IT construction in hospitals, medical information systems will be bound to develop toward mobile applications, intelligent analysis, and interconnection and interworking, on the premise that there is an effective medical device integration (MDI) technology. To this end, this paper presents a MDI model based on the Internet of Things (IoT). Through abstract classification, this model is able to extract the common characteristics of the devices, resolve the heterogeneous differences between them, and employ a unified protocol to integrate data between devices. And by the IoT technology, it realizes interconnection network of devices and conducts associate matching between the data and the inspection with the terminal device in a timely manner.

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Research on Network Communication Model of Intelligent Ship Handling Simulator

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.97-98.787 ◽

2011 ◽

Vol 97-98 ◽

pp. 787-793 ◽

Cited By ~ 1

Author(s):

Shen Hua Yang ◽

Guo Quan Chen ◽

Xing Hua Wang ◽

Yue Bin Yang

Keyword(s):

Mathematical Model ◽

Degrees Of Freedom ◽

Communication Process ◽

Network Communication ◽

Communication Model ◽

Test Results ◽

Six Degrees Of Freedom ◽

Model Based ◽

Hydraulic Servo ◽

Ship Handling

Due to the target ship in the traditional ship handling simulator have not the ability to give way to other ships automatically to avoid collision, this paper put forward a new idea that bringing the hydraulic servo platform, six degrees of freedom ship mathematical model, the actual traffic flow, researching achievement of automatic anti-collision in research of the new pattern ship handling simulator, and successfully develop the Intelligent Ship Handling Simulator(ISHS for short). The paper focuse on the research on the network communication model of ISHS. We took the entire simulator system as three relatively independent networks, proposed a framework of communication network that combined IOCP model based on TCP with blocking model based on UDP, and gave the communication process and protocols of system. Test results indicate that this is an effective way to improve the ownship capacity of ship handling simulator and meet the need of multi-ownship configuration of desktop system of ship handling simulator.

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A Weakly Supervised Academic Search Model Based on Knowledge-Enhanced Feature Representation

Wireless Communications and Mobile Computing ◽

10.1155/2021/4411524 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Mingying Xu ◽

Junping Du ◽

Feifei Kou ◽

Meiyu Liang ◽

Xin Xu ◽

...

Keyword(s):

Big Data ◽

Internet Of Things ◽

Feature Representation ◽

Search Model ◽

Model Based ◽

Proposed Model ◽

Cloud Server ◽

Weakly Supervised ◽

Internet Of Things Technology ◽

Academic Search

Internet of Things search has great potential applications with the rapid development of Internet of Things technology. Combining Internet of Things technology and academic search to build academic search framework based on Internet of Things is an effective solution to realize massive academic resource search. Recently, the academic big data has been characterized by a large number of types and spanning many fields. The traditional web search technology is no longer suitable for the search environment of academic big data. Thus, this paper designs academic search framework based on Internet of Things Technology. In order to alleviate the pressure of the cloud server processing massive academic big data, the edge server is introduced to clean and remove the redundancy of the data to form a clean data for further analysis and processing by the cloud server. Edge computing network effectively makes up for the deficiency of cloud computing in the conditions of distributed and high concurrent access, reduces long-distance data transmission, and improves the quality of network user experience. For Academic Search, this paper proposes a novel weakly supervised academic search model based on knowledge-enhanced feature representation. The proposed model can relieve high cost of acquisition of manually labeled data by obtaining a lot of pseudolabeled data and consider word-level interactive matching and sentence-level semantic matching for more accurate matching in the process of academic search. The experimental result on academic datasets demonstrate that the performance of the proposed model is much better than that of the existing methods.

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AdaGUM: An Adaptive Graph Updating Model-Based Anomaly Detection Method for Edge Computing Environment

Security and Communication Networks ◽

10.1155/2021/9954951 ◽

2021 ◽

Vol 2021 ◽

pp. 1-12

Author(s):

Xiang Yu ◽

Chun Shan ◽

Jilong Bian ◽

Xianfei Yang ◽

Ying Chen ◽

...

Keyword(s):

Internet Of Things ◽

Anomaly Detection ◽

Real Time ◽

Detection Method ◽

Edge Computing ◽

Sensor Data ◽

Computing Environment ◽

The Real ◽

Edge Node ◽

Model Based

With the rapid development of Internet of Things (IoT), massive sensor data are being generated by the sensors deployed everywhere at an unprecedented rate. As the number of Internet of Things devices is estimated to grow to 25 billion by 2021, when facing the explicit or implicit anomalies in the real-time sensor data collected from Internet of Things devices, it is necessary to develop an effective and efficient anomaly detection method for IoT devices. Recent advances in the edge computing have significant impacts on the solution of anomaly detection in IoT. In this study, an adaptive graph updating model is first presented, based on which a novel anomaly detection method for edge computing environment is then proposed. At the cloud center, the unknown patterns are classified by a deep leaning model, based on the classification results, the feature graphs are updated periodically, and the classification results are constantly transmitted to each edge node where a cache is employed to keep the newly emerging anomalies or normal patterns temporarily until the edge node receives a newly updated feature graph. Finally, a series of comparison experiments are conducted to demonstrate the effectiveness of the proposed anomaly detection method for edge computing. And the results show that the proposed method can detect the anomalies in the real-time sensor data efficiently and accurately. More than that, the proposed method performs well when there exist newly emerging patterns, no matter they are anomalous or normal.

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