Interactive Middleware Services for Heterogeneous Systems

Raghupathy, Vasanthi; Khalaf, Osamah Ibrahim; Romero, Carlos Andrés Tavera; Sengan, Sudhakar; Sharma, Dilip Kumar

doi:10.32604/csse.2022.021997

Cited by 19 publications

(6 citation statements)

References 33 publications

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“…Today, big data technology is often used as a means of investigating the current situation [13]. Different from traditional verification, extensive research is carried out through object data as a whole [14]. erefore, currently available research is limited to personnel, materials, and temporary personnel and is limited to selectivity, and complete information about the complete plan cannot be obtained.…”

Section: Theories and Methods Related To Urbanmentioning

confidence: 99%

Application of Big Data Technology in Urban Greenway Design

Liu

2022

Security and Communication Networks

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With the continuous advancement of big data technology, there are many drawbacks in the past urban greenway design. The environment, population distribution, geographic location, spatial distribution, and other factors affect the greenway design. At the same time, a large amount of historical data is mixed. The systematic arrangement has indirectly led to the fact that urban greenway design does not know how to analyze and use past data. There is always a waste of various resources. While greenways bring many positive impacts, they also have negative impacts: it is difficult for the wild animals in the greenways to move along the predetermined passages, and most animals spread in the greenways in a disorderly manner, so that the greenways accelerate the invasion of alien species and diseases to a certain extent. Using big data technology can enable us to allocate resources more reasonably and accurately. The purpose of this article is to study the use of big data technology to rationalize our urban greenway design. This paper adopts a calculation method that closely combines quantitative and qualitative features to study its features. The local feature analysis algorithm can extract and analyze urban spatial data. Spatial remote sensing image technology can deepen our knowledge and understanding of urban spatial characteristics, conduct data mining, provide all the information of urban space, analyze the content and type, and classify it; then filter and integrate the big data in the greenway planning and design information. The information about greenway planning and design in the big data of Dao Design is digitized. This information can be composed of traditional data and dynamic data. Finally, after scientific analysis, the data is subjected to scenario assumptions, modeling, and output, we applied various analysis results to actual operations, and we obtain the final inspection. In fact, this is also going out from actual work, summing up the experience we have done, and again starting from the perspective of specific practice. Compared with our traditional field investigation before, with the guidance of big data analysis, the experimental results of this article show that under the use of big data technology, the wrong resolution of the SegNet semantic classification algorithm for buildings is 9.6%, and the others are 10.9%, 16%, 20%, etc., which are all greater than 9.6%, so the segNet model has a higher image acquisition accuracy. It can solve the problems caused by the lag of the past questionnaires and the small sample size, improve the accuracy of data extraction through algorithms, find some features and problems of greenway design, and complete the urban greening rate through computer algorithms. Calculation and analysis can better provide the most reasonable method for greenway design site selection and route selection.

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Section: Theories and Methods Related To Urbanmentioning

confidence: 99%

Application of Big Data Technology in Urban Greenway Design

Liu

2022

Security and Communication Networks

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“…Another option is to go beyond performance engineering to address issues like security and trust in IoT and edge cloud environments [30][31].…”

Section: Discussionmentioning

confidence: 99%

Clustered Single-Board Devices with Docker Container Big Stream Processing燗rchitecture

Penchalaiah¹,

Al-Humaimeedy²,

Maashi³

et al. 2022

Computers, Materials &Amp; Continua

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The expanding amounts of information created by Internet of Things (IoT) devices places a strain on cloud computing, which is often used for data analysis and storage. This paper investigates a different approach based on edge cloud applications, which involves data filtering and processing before being delivered to a backup cloud environment. This Paper suggest designing and implementing a low cost, low power cluster of Single Board Computers (SBC) for this purpose, reducing the amount of data that must be transmitted elsewhere, using Big Data ideas and technology. An Apache Hadoop and Spark Cluster that was used to run a test application was containerized and deployed using a Raspberry Pi cluster and Docker.To obtain system data and analyze the setup's performance a Prometheusbased stack monitoring and alerting solution in the cloud based market is employed. This Paper assesses the system's complexity and demonstrates how containerization can improve fault tolerance and maintenance ease, allowing the suggested solution to be used in industry. An evaluation of the overall performance is presented to highlight the capabilities and limitations of the suggested architecture, taking into consideration the suggested solution's resource use in respect to device restrictions.

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“…For optimally tuning the key generation process of the MKHE technique, the ISSOA is applied to it. The presented approach is a metaheuristic method that simulates the social spider behavior to live together, searching for food and transferring the required data among them [21]. Generally, the population of social spiders is separated into females and males, in which the female represents 60%-90% of the overall spider number, and this group creates a web (searching domain) and searches for prey in this web.…”

Section: Optimal Key Generation Using Issoamentioning

confidence: 99%

Encryption with User Authentication Model for Internet of Medical Things Environment

Riya¹,

Surendran²,

Romero³

et al. 2023

Intelligent Automation &Amp; Soft Computing

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Internet of Medical Things (IoMT) enabled e-healthcare has the potential to greately improve conventional healthcare services significantly. However, security and privacy become major issues of IoMT because of the restricted processing abilities, storage, and energy constraints of the sensors. Therefore, it leads to infeasibility of developing traditional cryptographic solutions to the IoMT sensors. In order to ensure security on sensitive medical data, effective encryption and authentication techniques need to be designed to assure security of the patients and healthcare service providers. In this view, this study designs an effective metaheuristic optimization based encryption with user authentication (EMOE-UA) technique for IoMT environment. This work proposes an EMOE-UA technique aims to accomplish mutual authentication for addressing the security issues and reducing the computational complexity. Moreover, the EMOE-UA technique employs optimal multikey homomorphic encryption (OMKHE) technique to encrypt the IoMT data. Furthermore, the improved social spider optimization algorithm (ISSOA) was employed for the optimal multikey generation of the MKHE technique. The experimental result analysis of the EMOE-UA technique takes place using benchmark data and the results are examined under various aspects. The simulation results reported the considerably better performance of the EMOE-UA technique over the existing techniques.

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Interactive Middleware Services for Heterogeneous Systems

Cited by 19 publications

References 33 publications

Application of Big Data Technology in Urban Greenway Design

Application of Big Data Technology in Urban Greenway Design

Clustered Single-Board Devices with Docker Container Big Stream Processing燗rchitecture

Encryption with User Authentication Model for Internet of Medical Things Environment

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