- Open Access
RETRACTED ARTICLE: Research on intelligent medical big data system based on Hadoop and blockchain
EURASIP Journal on Wireless Communications and Networking volume 2021, Article number: 7 (2021)
In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.
The development of medical information services is an international trend. With the rapid development of information technology, more and more domestic medical institutions are accelerating the implementation of information-based platforms based on overall construction. Such systems have improved the hospital's service level and core competitiveness [1, 2]. Information technology not only improves the efficiency of doctors' work, but also it allows doctors to have more time to see patients, improves patient satisfaction and trust, and establishes an invisible scientific image of medical institutions . In the past few years, the US health care information industry has made great strides. Google works with the US Medical Centre to create an electronic medical record for hundreds of millions of communities that doctors can remotely monitor. Microsoft has also launched a new medical information service platform to help doctors, patients and families understand the latest real-time status of patients. A few years ago, Intel also launched a digital health care platform that helps doctors and patients build interaction through information technology. IBM has a lot of work in this area. In China, the “Twelfth Five-Year Plan” of the Ministry of Health clearly stated that health informationization is an important task for deepening medical reform. The Ministry of Health of China has initially established a health information technology roadmap, referred to as the “3521 Project,” which is to build a national, provincial and municipal health information platform, and strengthen public health, medical services, new rural cooperative medical insurance, and basic drug systems. With the aging of the population and the changes in diseases, the proportion of medical services for chronic diseases is rising, which seriously jeopardizes human life and health. This makes personal health management more and more accepted and valued by people. Relevant guidance departments have clearly identified chronic diseases. Prevention and control information has incorporated into health information technology [4, 5]. How to use advanced information technology, patient-centered medical resources, achieve effective integration of information interconnection and resource sharing, innovate chronic disease health management system, improve chronic disease monitoring and information management, has become a key to chronic disease management and control in China .
At present, China's health care system has more focused on treatment, with a focus on medical plans and means to control personal life. Therefore, if there is a service system that can focus on the quality of life and lifestyle of individuals and meet the diverse medical needs of each individual, changing the allocation of medical resources tends to “just cure,” attach importance to the development of medical care, and despise the health of the population. Often, people need a warm, personal-centered health system. A very important aspect of this process is how to use information identification methods without notice, when early detection and early intervention, not only focus on those who have diagnosed, but also strengthen prevention efforts for high-risk groups. At the right time, we can have a much-targeted measure of intervention. In fact, health management has a trilogy: health management is the foundation and health assessment, and health intervention is the key. The World Health Organization survey found that lifestyles affect health by 5 percent. In this context, through health management, self-management is very valuable, can arouse everyone's health awareness, lifestyle changes, and improve people's health . However, how to manage the real-time control of personal health information, effectively prevent, and control chronic diseases still faces two challenges. On the one hand, how to mobilize participation with the patient has no obvious symptoms. On the other hand, for real prevention, doctors need to spend a lot of time and energy [8, 9]. How can doctors have so much time and how to have appropriate mechanisms to encourage doctors? These two problems have been plaguing the universality of the medical field for a long time.
This paper is a safe medical big data ecosystem for the establishment of personalized health information and services on the Hadoop platform to consider the above aspects of stroke, optimize the local medical resources structure, improve people's health care awareness, and promote the health of China's major social impacts . The system utilizes the massive data storage of the Hadoop big data platform, and the convenient scalability greatly reduces the difficulty of storage and system upgrade. It is based on the above considerations to establish a personalized security-oriented big data ecosystem on the Hadoop platform. The health information system has a significant role and social influence on the prevention and treatment of chronic diseases, optimizing the structure of regional medical resources, raising people's awareness of health care, and promoting the level of medical informationization in China . The system utilizes the massive data storage and scalability of the Hadoop big data platform, which greatly reduces the storage and upgrade maintenance costs of the system. With the current form of its popular mobile applications, patient users and doctors can stay in touch anytime, anywhere, which reduces the communication barriers that are inconvenient for medical communication and increases the level of medical digitalization. As the scale of personal health data continues to expand, the computing power of the Hadoop cluster can solve the problem of large-scale concurrent access during peak traffic hours, ensuring the robustness of the system pair.
The specific contributions of this paper include:
This article designs and implements a secure medical big data ecosystem on the Hadoop big data platform.
The system proposed in the thesis realizes the unification of the personal health data center, and centrally stores and analyzes the original distributed data through the data synchronization module and the independent data acquisition system.
Take advantage of the Hadoop big data platform to create personalized health management services for patients with personalized health information, and facilitate medical staff to manage patients.
The rest of this paper is organized as follows. Section 2 discusses related work, Sect. 3 discusses the research methods of the paper, Sect. 4 discusses the experimental analysis of the paper, Sect. 5 discusses the experimental results of the paper, and finally the full text is summarized in Sect. 6.
2 Related work
Developed countries such as Europe and the United States have begun to use information technology to integrate resources into existing medical systems. The main sign is that many medical institutions have gradually begun to develop and apply regional and even national large-scale integrated medical systems . At present, the regional medical and health information construction in developed countries has developed to a new stage . Some countries in Europe and the United States have entered an aging society in the 1980s. In the face of the common problem of aging, some European countries do not encourage the construction of new old-age care institutions, and the pension models in developed countries are based on home-based care . Support community and family to provide high-quality long-term care for the elderly. In order to provide high-quality services that satisfy the elderly, Switzerland, the Netherlands, Denmark, the United Kingdom and other countries have developed incentive programs to promote the cooperative operation of providers of different types of aged care services such as institutional pension, community pension, and home pension. Nowadays, the model of home-based care for the developed countries and related supporting systems has developed relatively mature . Dr. Rifat et al. mentioned that mobile health care is an integration of mobile computing and medical monitoring. They believe that mobile devices are an integral part of our lives and can be seamless. To integrate health care services into our daily lives . Dr. Tang et al. (2010), in conjunction with medical information systems and mobile communication technologies, established a telemedicine home care management system that enables long-term and sustainable health monitoring by transmitting multimedia information services .
Nanjing Mobile launched a series of “Healthy Patrons” project, which includes a variety of health terminal products with data transmission functions, which are used to measure physiological indicators such as blood pressure, blood sugar, ECG, body temperature, etc., and SIM cards built into each terminal product . The measurement data can have uploaded to the health management platform. The platform can automatically provide reminders and reports to users, and achieve multi-integrated service effects such as prevention, diagnosis, treatment, and rehabilitation. It is convenient for individuals, families, and doctors to dynamically control the health status of the elderly in real time and make corresponding reasonable measures in time. Sichuan Mobile launched the “Platform. Terminal” smart care information platform mainly uses information technology to achieve effective connection between citizens' pension needs and civil affairs agencies and community service organizations . The dedicated terminal sets the SOS emergency call button, which can locate the old man through LBS and GPRS in the emergency of the old man, and inform the 120 and relatives to help the first time. The Health China 2020 Strategy Research Report released by the Health Planning Commission in 2012 clearly pointed out that in the next few years, the major projects of the seven major medical systems involving a total amount of 400 billion yuan will have launched . Among them, the national e-health system project budget of 6.1 billion yuan, including the implementation of the standardization of large-scale integrated hospital information system, the establishment of a national electronic health record and the creation of a regional medical information platform. This is the first time the government has clearly stated the scale of investment in the direction of medical informatization, indicating that the status of informatization in the reform of the medical system is constantly improving .
As the public health sector includes safe production, as well as the management of medical supply forests and modern logistics, the application of any technology is not separate; they must be holistic and systematic. Vaccines, blood products, including food anti-counterfeiting, and drug safety monitoring will all have included in the scope of technical monitoring through RFID technology.
Hadoop is a distributed system infrastructure developed by the Apache Foundation. Users can develop distributed programs without having to understand the underlying details of the architecture, so that they can fully utilize the platform's massive data storage and fast computing capabilities. Hadoop implements a distributed file system, referred to as HDFS . HDFS has good features, high fault tolerance, and the need to use expensive hardware like other platforms. In addition, it provides a high-speed interface for application data access, suitable for applications with very large data sets. HDFS relaxes the requirements of the file system so that it can access the data in the file system in a stream form .
The core design of the Hadoop framework is HDFS and Map Reduce. HDFS mainly provides massive storage of data, and Map Reduce provides distributed computing services for data. The processing of data in Hadoop can be understood in a simple sentence: the data have processed by the Hadoop cluster to get the results. The processing flow is shown in Fig. 1 .
The two core components of Hadoop are HDFS and MapReduce in Fig. 2. The HDFS function is the storage of massive data, and the role of MapReduce is the calculation of massive data. The data storage warehouse tool Hive and the distributed database system Hbase are also two important components of Hadoop. The full HDFS name is Hadoop Distributed File System, which is used to store files in a Hadoop cluster. The HDFS surface looks like a simple hierarchical file system with simple operations like creating, removing, moving, and more . However, the files stored in HDFS are divided into data blocks according to certain needs, and then many and many data blocks are placed in multiple slave nodes. This is where it is very different from traditional storage architectures [26, 27]. The user usually determines the size of the divided data block and the number of data blocks placed. The upper layer of DFS is MapReduce, which consists of JobTrackers and TaskTrackers [28, 29]. On HDFS, by default, large files have divided into equal parts. In the HDFS introduction document, this default value is set to 64 M. In the diagram shown below, the file data1 has first divided into three parts, which are placed in three different machines . Map Reduce is a task for each component of Hadoop input, and then call Map to calculate. In the task, the system will process the input records one by one, and then the map will be key-value after processing . The form of the key-value pair will output the result. Hadoop will then pass the result of the previous step as input to Reduce by key. The output of the Reduce Task is the output of the entire job and is saved on HDFS .
4.1 Hadoop architecture
Hadoop uses a master–slave (Master, Slaver) architecture. In order to get Hadoop running smoothly in a complete cluster, a series of necessary background programs are essential. The master–slave architecture of Hadoop is shown in Fig. 3.
Hadoop is a software framework that can have distributed to massive amounts of data and developed by the Apache Foundation. It is possible to satisfy a user to develop a distributed program without fully understanding the principle of "distributed." It is able to divide large chunks of programs into small work units, making full use of the ability of the cluster to make high-speed storage or calculations.
The NameNode is a daemon for HDFS in Hadoop and usually runs on a separate machine. It is mainly responsible for recording how the stored or calculated files are divided into data blocks required for fast processing, and the locations of the nodes to which the data blocks are divided are recorded. The NameNode decides whether to map the file to a copy block on the DataNode . Although the main function of the NameNode is to manage the memory and I/O modules, the true I/O processing is independent of the NameNode. Only the metadata of the node location where the data block is stored has related to the NameNode. This is to avoid these. Unnecessary information reduces the processing speed of the server. However, the NameNode itself is a single node of the Hadoop cluster, and as long as there is a problem with the NameNode service, the entire system will be in crisis.
Compared to the singularity of the NameNode, the number of DataNodes is very large. The DataNode program runs on every slave server in the cluster. It is responsible for reading and writing the data block divided and allocated to it to the local system, that is, it is the address used to store the data block. When the user needs to use the data block of this block, the NameNode finds the corresponding location of the storage DataNode. The client then communicates directly with the daemon on the DataNode to process the required data blocks accordingly. The DataNode has presented in the form of a rack, and each rack connects all the data for the user through a switch, NameNode.
4.2 Building a secure medical big data ecosystem based on Hadoop
In this system, mainly to demonstrate the feasibility of the system, the deployed Hadoop cluster adopts a single-layer network topology, which will reduce many unnecessary problems in development. Of course, in the actual production environment, this is obviously not a suitable; most appropriate still requires a typical two-layer network topology. The network structure used by this system is shown in Fig. 4
The Hadoop cluster of this system uses a master–slave architecture. From the perspective of scalability and performance, in a large-scale Hadoop cluster configuration, different component roles are assigned to different machines to avoid failure of a single machine and failure of the entire cluster. The cluster of this system is composed of 8 host computers, which uses the Ubuntu12.4STD operating system and uses Gigabit LAN to ensure the data transmission. The specific configuration of each component role is shown in Table 1.
The cluster uses a unified installation directory hadoop:/usr/local/hadoop, Hbase:/usr/local/hbase, Zookeeper:/usr/local/zookeeper. Figure 5 shows the overall design of the distribution Hadoop.
Among them HIT02 and HIT03 are Name Node (can change Name Node, high availability), Hmaster, HIT04 and HIT05 are Resource Manager, (HA mode, high availability mode requires at least 2 RM), HIT06-HIT10 is Data Node, Zookeeper. In addition to HIT04 and HIT05, all Hbase is installed. The basic operation commands are shown in Fig. 6. Overall dataflow of Hadoop is shown in Fig. 7.
4.3 Safety medical big data ecosystem construction process
The security medical big data ecosystem of this paper is improved because of the traditional information retrieval system. It utilizes the concept of knowledge map proposed by Google in 2012 and expands the search keywords. This paper realizes the construction of the stroke knowledge map. The knowledge map of the system is built on the Wikipedia database, which guarantees the quality of the map.
First, we choose the knowledge vocabulary collection related to the security medical big data ecosystem as the most program input. According to this knowledge vocabulary collection, we find the corresponding page collection, and then grab these pages to analyze and judge the link information in the page. Whether the link information is a knowledge term related to the secure medical big data ecosystem, and if so, the knowledge term is added to the term set, and the link relationship between them is saved to the relationship set for the system. Knowledge Graphs limit the path length of nodes and starting nodes to facilitate experiments. Otherwise, the constructed knowledge map will be a very large collection. Finally, an undirected graph is formed according to the set of knowledge terms and the set of relationships, which is the stroke knowledge map. The construction flowchart of the safety medical big data ecosystem knowledge is shown in Fig. 8.
The research and analysis of big data storage under the Hadoop platform needs to complete the loading of virtual machines, the installation of Hadoop and platform construction, the application of some file management instructions of HDFS and the problems of Hadoop storage. The first thing to solve is the installation of Linux. I have consulted a lot of materials and articles. I have experienced many failures, and I have slowly understood many problems that I have not succeeded in. Finally, I have an idea of installing a virtual machine system. CentOS7.0 was successfully installed and its basic configuration and installation of some basic tools were completed. The high efficiency of Hadoop-based secure medical big data ecosystem is determined by its own characteristics—fast information transmission, transparent information and information. Because information reduces transaction costs, coordinated costs, and execution transaction costs, it increases transaction efficiency and increases patient value. Improving the efficiency of medical business is one of the important goals of medical digitalization. With the help of electronic information mobilization means and information mode, such as automatic medicine dispensing machines and logistics lines created by modern information technology, medical services such as automatic medicine dispensing machines and logistics lines can be used. Rapidly improve medical efficiency and make labor-intensive business transition to high efficiency and precision. The increase in transaction efficiency leads to a reduction in costs and thus more value. At the same time, more information or improved information reduces the cost of seeking medical consultations for patients. Through the remote video clinic of the upper hospital and the logistics distribution of the medical platform, third-party payment and other background management, patients can find superiors in the nearest community hospital. Hospital doctors have simplified medical procedures, effectively reduced health care costs, saved time and reduced medical costs. Figure 9 shows the test result between Hadoop and traditional method.
China has not formed an effective technology construction mode and business operation mode in the construction of information platform services. In terms of technology, standardization management of information has high requirements for information transmission standards and exchange standards, and the state has no system. Advance standards, comprehensive evaluation of system platform construction, including supervision and evaluation of institutions, stay in the line and framework stage; in the service operation, there is a lack of completed and mature standardized management system, and no effective service model is formed.. The Safety Medical Big Data Ecosystem is an information platform for all medical and health departments in the society. It involves the special group of the elderly, the health administrative department, the medical service organization, the disease prevention, and control centre, the social medical security and other related departments. Multi-sectoral integration and the management of the elderly in China is still multi-sectoral management, the government functions are not clear, resulting in uneven pension services, unbalanced demand for old-age care, and low resource utilization, which seriously affects the development of services for the elderly. The government is required to form a complete and mature standardized management mechanism and manage it in an integrated manner to integrate health care resources. Figure 10 shows the test results of packet loss rate errors in secure medical big data transmission.
Medical expenses are the largest expenditure in addition to basic living. China's medical insurance is limited to a single business operation, and residents' medical insurance has not yet involved regional collaborative services. The medical care system has not yet established, and the medical insurance costs for inter-patient diagnosis cannot have settled. Many local telemedicine services are not included in the scope of medical insurance reimbursement. Therefore, the elderly are more willing to go to the first-level hospital or the secondary hospital to set up a medical insurance hospital for treatment.
The development of informatization in China has only begun in recent years. There is a serious information bias. In the city and large hospitals, there is still an initial construction of informatization, and the level of informatization of grassroots health institutions is not widely applied, resulting in the inversion of the hierarchical structure. It is because the awareness of informatization construction of grassroots health institutions is weak, and the lack of introduction of information zed talents is lacking. Secondly, the level of informatization application is low. The health Informationization construction of primary medical institutions has not yet covered comprehensively. The treatment of basic diseases has not involved informatization. Informatization applications are still at a lower level, lacking the collection and accuracy of data, leading to the informatization construction in the community. Figure 11 shows the comparison result of safe medical big data transmission performance.
In the development of the safety medical big data ecosystem, the role of government departments is the most critical, and it is the main bearer of medical services. Therefore, the government must scientifically develop the old-age service system, conscientiously implement the multi-level and diversified service needs of the elderly, and adhere to the development of China's elderly services in the direction of intelligence and information. The health management department should avoid the information system project in the construction process, because the standards are inconsistent, which are leading to the original intention of leaving the design, meaningless waste of the sword. At the same time, the government should give full play to the guiding role and break the Informationization of medical resources. In addition to the horizontal management of health services, it pays attention to the vertical management of health services, coordinates the interests of various departments, and cooperates regionally to form a scientific system. Integrate more old-age resources, strengthen overall coordination, study and formulate a unified, specific and standardized elderly care service process, give full play to the government's binding role, and establish a benign interaction mechanism between government, society and market. The design and grassroots advancement have combined at the same time, and the government should form a unified payment system and provide financial support for the aged care service.
Because of the weakening of physical function, the elderly have a high incidence of illness in the elderly, frequent frequency of medical treatment, medical expenses have characterized by “rigid” rise, and the burden of medical expenses is heavy. The government should adjust the medical insurance policy and increase the expenditure on medical insurance for the elderly . The government should also encourage the development of medical commercial insurance, guide residents to make more scientific and rational medical financial planning, broaden the source of medical insurance funds for the whole society, and mobilize social resources.
Whether it is a traditional nursing home that has already started operating in the early days, or a large capital force that is planning to build an industrial platform, they are in urgent need of a comprehensive and efficient information system. Primary medical institutions are the health institutions closest to the elderly. The informationization of grassroots medical institutions directly affects the amount of medical data provided by the health care big data ecosystem. Therefore, it is recommended to increase capital investment in grassroots medical institutions, strengthen information construction, and introduce informationization.
The advantages of the medical information health platform should have popularized for the elderly, and the elderly can experience the convenient service brought by the medical information platform. The elderly can understand their physiological parameters at home and operate easily. Through platform remote consultation, the space distance is greatly shortened, online consultation and diagnosis services for elderly people and doctors have provided, and the self-health management of the elderly, especially elderly patients with chronic diseases, and the transition from public management to personal management are guaranteed to a certain extent. The efficient service quality of the elderly and the improvement of the satisfaction and participation of the elderly in medical treatment can stimulate the enthusiasm of the elderly to use the platform.
In general, with the continuous deepening of China's medical and health system reform, medical and health informationization has become a booster for the development of health care, especially in the face of a traditional service such as old-age services; there is a lot of space for medical and health informationization. Integrating the medical resources of individuals, hospitals, and society through the secure medical big data ecosystem has improved the accessibility of older people to medical services. Reducing medical costs is bound to be the development direction of the health care industry. This paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. The system mainly designs personal health data center, personalized information recommendation subsystem, and other modules. Utilizing the advantages of the Hadoop big data platform, the personalized health information service has designed to provide personalized health management services for patient users, while providing convenience for medical staff to manage patients.
Availability of data and materials
Data sharing not applicable to this article as no datasets are generated or analyzed during the current study.
International Business Machine
I.T. Hashem et al., The rise of “big data” on cloud computing: review and open research issues. Inf. Syst. 2015(47), 98–115 (2015)
L. Jake et al., Big data application in biomedical research and health care: a literature review. Biomed. Inform. Insights 8, S31559 (2016)
Y. Ying, L. Hung, Designing a demand chain management framework under dynamic uncertainty: an exploratory study of the Chinese fashion apparel industry. Asia Pac. J. Mark. Logist. 2018(30), 198–234 (2018)
S. Seref, S. Duygu. Big data: a review, in International Conference on Collaboration Technologies and Systems (CTS) (IEEE, 2013), pp. 42–47
F. Hernandez, D.H. Abubaker, Value creation via supply chain risk management in global fashion organizations outsourcing production to China. J. Glob. Oper. Strateg. Sourc. 2018(11), 250–272 (2018)
E. Themari, J.O. Cilliers, Equipping small business retailers to manage logistical supply chain drivers: a theoretical guideline. J. Transp. Supply Chain Manag. 2017(11), 1–12 (2017)
D. Yuri, D. Cees, M. Peter. Defining architecture components of the Big Data ecosystem, in International Conference on Collaboration Technologies and Systems (CTS). (IEEE, 2014), vol. 2014, no. 12, pp. 104–112
R. Wullianallur, R. Viju, Big data analytics in healthcare: promise and potential. Health Inf. Sci. Syst. 2014(02), 1–3 (2014)
L. Zhihan et al., Next-generation big data analytics: State of the art, challenges, and future research topics. IEEE Trans. Ind. Inform. 2017(13), 1891–1899 (2017)
A.A. Cárdenas, P.K. Manadhata, S.P. Rajan, Big data analytics for security. IEEE Secur. Priv. 2013(11), 74–76 (2013)
D. Yuri, N. Canh, M. Peter, Architecture framework and components for the big data ecosystem. J. Syst. Netw. Eng. 2013(5), 1–31 (2013)
R. Frederick, W. Samuel, Research directions on the adoption, usage, and impact of the internet of things through the use of big data analytics, in 48th Hawaii International Conference on System Sciences (HICSS) (IEEE, 2015), no. 11, pp. 1531–1540
A. Ejaz, The role of big data analytics in Internet of Things. Comput. Netw. 129, 459–471 (2017)
V. Dinusha, Privacy-preserving record linkage for big data: current approaches and research challenges, in Handbook of Big Data Technologies (Springer, Cham, 2017), pp. 851–895
M.M. Rathore et al., Urban planning and building smart cities based on the internet of things using big data analytics. Comput. Netw. 101, 63–80 (2016)
T. Chandu, Centralized fog computing security platform for IoT and cloud in healthcare system, in Exploring the Convergence of Big Data and the Internet of Things (IGI Global, 2018), pp. 141–154
S. Ather, Current security threats and prevention measures relating to cloud services, Hadoop concurrent processing, and big data, in IEEE International Conference on Big Data (Big Data) (IEEE, 2015), pp. 1865–1870
G. Maanak, Multi-layer authorization framework for a representative Hadoop ecosystem deployment, in Proceedings of the 22nd ACM on Symposium on Access Control Models and Technologies (ACM, 2017), pp. 183–190
Y. Ji, Emerging information technologies for enhanced healthcare. Comput. Ind. 69, 3–11 (2015)
G. Maanak, P. Farhan, S. Ravi. An attribute-based access control model for secure big data processing in Hadoop ecosystem, in Proceedings of the Third ACM Workshop on Attribute-Based Access Control (ACM, 2018), pp. 13–24.
A. Ahmad et al., Multilevel data processing using parallel algorithms for analyzing big data in high-performance computing. Int. J. Parallel Program. 2017(11), 1–20 (2017)
A. Ahmad et al., Toward modeling and optimization of features selection in Big Data based social Internet of Things. Future Gener. Comput. Syst. 2018(82), 715–726 (2018)
F. Long, N. Xiong, A.V. Vasilakos, L.T. Yang, F. Sun, A sustainable heuristic QoS routing algorithm for pervasive multi-layered satellite wireless networks. Wirel. Netw. 16(6), 1657–1673 (2010)
C. Lin, N. Xiong, J.H. Park, T. Kim, Dynamic power management in new architecture of wireless sensor networks. Int. J. Commun. Syst. 22(6), 671–693 (2009)
H. Liang, J. Zou, K. Zuo, M.J. Khan, an improved genetic algorithm optimization fuzzy controller applied to the wellhead back pressure control system. Mech. Syst. Signal Process. 142(1), 106–114 (2020)
H. Liang, J. Zou, Z. Li, M.J. Khan, Y. Lu, Dynamic evaluation of drilling leakage risk based on fuzzy theory and PSO-SVR algorithm. Future Gener. Comput. Syst. 95(4), 454–466 (2019)
J. Li, N. Xiong, J.H. Park, C. Liu, M.A. Shihua, S. Cho, Intelligent model design of cluster supply chain with horizontal cooperation. J. Intell. Manuf. 23(4), 917–931 (2012)
W. Guo, N. Xiong, A.V. Vasilakos, G. Chen, C. Yu, Distributed k-connected fault-tolerant topology control algorithms with PSO in future autonomic sensor systems. Int. J. Sens. Netw. 12(1), 53–62 (2012)
Z. Chen, D. Chen, Y. Zhang, X. Cheng, M. Zhang, C. Wu, Deep learning for autonomous ship-oriented small ship detection. Saf. Sci. 130, 104812 (2020)
L. Dong, W. Wu, Q. Guo, Reliability-aware offloading and allocation in multilevel edge computing system. IEEE Trans. 15(4), 145–157 (2015)
Y. Sun,Y. Weng, B. Luo, G. Li, B. Tao, D. Chen, D. Jian, Gesture recognition algorithm based on multi-scale feature fusion in RGB-D images. IET Image Process. 14(3), 45–49 (2020)
W. Wang, Q. Xu, L. Wang, H. Hei, P. Shen, W. Shi, L. Shan, GI/Geom/1 queue based on communication model for mesh networks. Int. J. Commun. Syst. 27(11), 3013–3029 (2014)
Yellow River Conservancy Technical Institute innovation fund, KJ18027
Yellow River Conservancy Technical Institute innovation fund, KJ18027.
The authors declare that they have no competing interests.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1186/s13638-023-02217-8
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Zhang, X., Wang, Y. RETRACTED ARTICLE: Research on intelligent medical big data system based on Hadoop and blockchain. J Wireless Com Network 2021, 7 (2021). https://doi.org/10.1186/s13638-020-01858-3
- Secure medical
- Big data
- Block chain
- Health data