An adaptive trust model based on recommendation filtering algorithm for the Internet of Things systems

2021 ◽  
Vol 190 ◽  
pp. 107952
Author(s):  
Guozhu Chen ◽  
Fanping Zeng ◽  
Jian Zhang ◽  
Tingting Lu ◽  
Jingfei Shen ◽  
...  
2015 ◽  
Vol 9 (1) ◽  
pp. 256-261 ◽  
Author(s):  
Aiyu Hao ◽  
Ling Wang

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.


2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987400 ◽  
Author(s):  
Waseem Ahmed Abbasi ◽  
Zongrun Wang ◽  
Yanju Zhou ◽  
Shahzad Hassan

This article first expounds the concept of supply chain finance and its credit risk, describes the hierarchical structure of the Internet of Things and its key technologies, and combines the unique functions of the Internet of Things technology and the business process of the inventory pledge financing model to design the supply chain financial model based on the Internet of Things. Then it studies the credit risk assessment under the supply chain financial model based on the Internet of Things, and uses the support vector machine algorithm and Logistic regression method to establish a credit risk measurement model considering the subject rating and debt rating. Finally, an example analysis shows that the credit risk measurement model has a high accuracy rate for determining whether small and medium-sized enterprises in the supply chain financial model based on the Internet of Things are trustworthy. This will facilitate the revision and improvement of the existing credit evaluation system and improve the accuracy of measuring the current financial risk of supply chain. This research adopts the Internet of Things to measure financial credit risk in supply chain and provides a reference for the following researches.


2011 ◽  
Vol 403-408 ◽  
pp. 2457-2460 ◽  
Author(s):  
Run Chen ◽  
Cai Ming Liu ◽  
Lu Xin Xiao

Grasping security situation of the Internet of Things (IoT) is useful to work out a scientific and reasonable strategy to defend the IoT security. In the interest of resolving the problems of the security situation sense technology for IoT, a security situation sense model based on artificial immune system for IoT is proposed in this paper. Security threat sense sub-model, formulation mechanism for security threat intensity and security situation assessment sub-model are established. The security threats in the IoT environment are surveyed effectively. Quantitative and accurate assessment for the Real-Time security situation is realized. Theoretical analysis shows that the proposed model is significative of theory and practice.


2012 ◽  
Vol 35 (5) ◽  
pp. 846-855 ◽  
Author(s):  
Wen-Mao LIU ◽  
Li-Hua YIN ◽  
Bin-Xing FANG ◽  
Hong-Li ZHANG

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1759
Author(s):  
Mabrook S. Al-Rakhami ◽  
Majed Al-Mashari

Accurate data and strategic business processes are crucial to all parties in a supply chain system. However, the absence of mutual trust can create a barrier to implementation. Several studies have shown that supply chains face challenges arising from a lack of trust with respect to the sharing of data. How well each party trusts the data they receive can have a profound influence on management decisions. Blockchain technology has been widely used to process cryptocurrency transactions. Recently, it has also proved to be effective in creating trust in the Internet of things (IoT) domain. Blockchain technology can facilitate mutual trust between parties who would otherwise have been doubtful of each other’s data, allowing for more effective and secure sharing of data. However, if the blockchain is not IoT-optimized, companies can experience significant delays and the need for extensive computational capacity. Moreover, there are still some limitations regarding the consensus between the nodes in the traditional consensus approaches. Here, we propose an alternative approach to creating trust in supply chains with diverse IoT elements. Our streamlined trust model simplifies data sharing and reduces computational, storage, and latency requirements while increasing the security of the IoT-based supply chain management. We evaluate the suggested model using simulations and highlight its viability.


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