Medical data fusion algorithm based on Internet of things

2018 ◽  
Vol 22 (5-6) ◽  
pp. 895-902 ◽  
Author(s):  
Weiping Zhang ◽  
Jingzhi Yang ◽  
Hang Su ◽  
Mohit Kumar ◽  
Yihua Mao
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Medical Data ◽  
Fusion Algorithm

scholarly journals Security Technology of Wireless Sensor Internet of Things Based on Data Fusion

2017 ◽  
Vol 13 (11) ◽  
pp. 25
Author(s):  
Jie Zhang
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Original Data ◽  
Wireless Sensor ◽  
The Internet ◽  
Fusion Technology ◽  
Security Technology ◽  
Fusion Algorithm ◽  
The Impact ◽  
The Internet Of Things

<span style="font-family: 'Times New Roman',serif; font-size: 10pt; mso-fareast-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: DE; mso-bidi-language: AR-SA;" lang="EN-US">In order to prove the effect of data fusion technology in the Internet of things, a wireless sensor Internet of things security technology based on data fusion is designed, and the impact of data fusion in the field of communication technology is studied. Therefore, two security fusion algorithms are designed on the basis of analyzing and comparing the advantages and disadvantages of various security fusion algorithms, namely, data security fusion algorithm EDCSDA and approximate fusion algorithm PADSA. By analyzing the probability distribution model of the data collected by the nodes, the disturbance data is superimposed on the original data to hide the effect of the original data. A test bed system for perception layer of the Internet of things is designed and implemented. The test results prove the feasibility of the two algorithms. Meanwhile, it shows that the two algorithms can reduce the transmission overhead of the network while guaranteeing the security. Based on the above finding, it is concluded that data fusion technology is very effective for improving network efficiency and prolonging the network life cycle as one of the key technologies in the perception layer of Internet of things.</span>

Application in Data Fusion of Internet of Things Based on the KL Distance of Reputation Model

2013 ◽  
Vol 760-762 ◽  
pp. 587-591 ◽  
Author(s):  
Xu Ping Zhu
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Interconnection Network ◽  
Limited Resource ◽  
The Internet ◽  
User Privacy ◽  
Fusion Algorithm ◽  
Other Information ◽  
Reputation Model ◽  
The Internet Of Things

The Internet of Things is a bearer network based on the Internet, the traditional telecommunications network, wireless self-organizing networks etc, so that all can be individually addressable ordinary physical objects to achieve the interconnection network. The Internet of Things is evolved from the wireless sensor network, which has limited resource in energy, memory, calculation, bandwidth and etc,. In addition, many applications in the Internet of Things are related to user privacy. In the process of data collection and transmission, the data may be forged , tampered, and a variety of other information security threats. In order to extend the network lifetime, to protect the authenticity and reliability of data fusion, this paper presents a reputation model of data fusion algorithm. The algorithm is verified by simulation, the experimental results show that the proposed algorithm is effective and the result of data aggregation is reliable.

Research on data fusion algorithm and anti-collision algorithm based on internet of things

2018 ◽  
Vol 85 ◽  
pp. 107-115 ◽  
Author(s):  
Yongfeng Cui ◽  
Yuankun Ma ◽  
Zhongyuan Zhao ◽  
Ya Li ◽  
Wei Liu ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Fusion Algorithm

scholarly journals Intelligent Building Data Fusion Algorithm by Using the Internet of Things Technology

2021 ◽  
Vol 2143 (1) ◽  
pp. 012030
Author(s):  
Duo Peng ◽  
Jingqiang Zhao ◽  
Tongtong Xu
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Minimum Variance ◽  
The Internet ◽  
Fusion Algorithm ◽  
Intelligent Building ◽  
Variance Matrix ◽  
Optimal Information ◽  
Internet Of Things Technology ◽  
The Internet Of Things

Abstract Analyzed in this paper based on the Internet of things technology for intelligent building data, redundancy of data fusion are pointed out, based on the dynamic Kalman filter algorithm of multi-sensor fusion, first using the theory of fuzzy and covariance matching technique to adjust the noise covariance of traditional algorithm, combined with weighted minimum variance matrix under the optimal information fusion algorithm of data fusion, Finally, the simulation results show that this algorithm can effectively reduce the redundancy of intelligent data and make the estimated value of data fusion more close to the actual value.

scholarly journals Heracles: A Context-Based Multisensor Sensor Data Fusion Algorithm for the Internet of Things

Information ◽  
2020 ◽  
Vol 11 (11) ◽  
pp. 517
Author(s):  
Flávia C. Delicato ◽  
Tayssa Vandelli ◽  
Mario Bonicea ◽  
Claudio M. de Farias
Keyword(s):  
Internet Of Things ◽  
Data Fusion ◽  
Smart Cities ◽  
Sampling Rate ◽  
Value Added ◽  
Sensor Data ◽  
The Internet ◽  
Fusion Algorithm ◽  
The Internet Of Things ◽  
Analyze Data

In the Internet of Things (IoT), extending the average battery duration of devices is of paramount importance, since it promotes uptime without intervention in the environment, which can be undesirable or costly. In the IoT, the system’s functionalities are distributed among devices that (i) collect, (ii) transmit and (iii) apply algorithms to process and analyze data. A widely adopted technique for increasing the lifetime of an IoT system is using data fusion on the devices that process and analyze data. There are already several works proposing data fusion algorithms for the context of wireless sensor networks and IoT. However, most of them consider that application requirements (such as the data sampling rate and the data range of the events of interest) are previously known, and the solutions are tailored for a single target application. In the context of a smart city, we envision that the IoT will provide a sensing and communication infrastructure to be shared by multiple applications, that will make use of this infrastructure in an opportunistic and dynamic way, with no previous knowledge about its requirements. In this work, we present Heracles, a new data fusion algorithm tailored to meet the demands of the IoT for smart cities. Heracles considers the context of the application, adapting to the features of the dataset to perform the data analysis. Heracles aims at minimizing data transmission to save energy while generating value-added information, which will serve as input for decision-making processes. Results of the performed evaluation show that Heracles is feasible, enhances the performance of decision methods and extends the system lifetime.

Application of multi-resolution data fusion algorithm

2010 ◽  
Vol 30 (9) ◽  
pp. 2556-2558 ◽  
Author(s):  
Ming-bo SHI ◽  
Ji-hong CHEN ◽  
Zheng-zheng JIANG
Keyword(s):  
Data Fusion ◽  
Fusion Algorithm ◽  
Resolution Data

scholarly journals Phenological Analysis of Sub-Alpine Forest on Jeju Island, South Korea, Using Data Fusion of Landsat and MODIS Products

Forests ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 286
Author(s):  
Sang-Jin Park ◽  
Seung-Gyu Jeong ◽  
Yong Park ◽  
Sang-hyuk Kim ◽  
Dong-kun Lee ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Data Fusion ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Fusion Model ◽  
Fusion Algorithm ◽  
Study Region ◽  
Modis Imagery ◽  
Increasing Temperature

Climate change poses a disproportionate risk to alpine ecosystems. Effective monitoring of forest phenological responses to climate change is critical for predicting and managing threats to alpine populations. Remote sensing can be used to monitor forest communities in dynamic landscapes for responses to climate change at the species level. Spatiotemporal fusion technology using remote sensing images is an effective way of detecting gradual phenological changes over time and seasonal responses to climate change. The spatial and temporal adaptive reflectance fusion model (STARFM) is a widely used data fusion algorithm for Landsat and MODIS imagery. This study aims to identify forest phenological characteristics and changes at the species–community level by fusing spatiotemporal data from Landsat and MODIS imagery. We fused 18 images from March to November for 2000, 2010, and 2019. (The resulting STARFM-fused images exhibited accuracies of RMSE = 0.0402 and R2 = 0.795. We found that the normalized difference vegetation index (NDVI) value increased with time, which suggests that increasing temperature due to climate change has affected the start of the growth season in the study region. From this study, we found that increasing temperature affects the phenology of these regions, and forest management strategies like monitoring phenology using remote sensing technique should evaluate the effects of climate change.

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