Adaptive Learning Scheme to Increase Fault Tolerance on Iot
By Joseph Musa
This FISWSN framework being discussed in IoT architecture has the capability to be extended to other architectures. For example, Gateway and Networking which is in the second IoT layer can be applied in Cloud Computing. The main limitation in this study is that the proposed framework is applicable only in the first layer of IoT network architecture and that the implemented WSN in it is cluster based. it indicate that today’s adaptive learning systems have negligible impact on learning outcomes, one aspect of quality. Adaptive learning has been partially driven by a realization that tailored learning cannot be achieved on a large-scale using traditional, non-adaptive approaches. Adaptive learning systems endeavor to transform the learner from passive receptor of information to collaborator in the educational process. Adaptive learning systems' primary application is in education, but another popular application is business training. They have been designed as desktop computer applications, web applications, and are now being introduced into overall curricula. this study focus mainly on how IoT devices can adopt this adaptive method(s). Fuzzy logic plays a big role here
Published: January 22, 2020
Uploaded by: Joseph Musa
Many applications based on Internet of Things (IoT) technology have recently founded in industry monitoring area. Internet of things (IoT) is realized by the idea of free flow of information amongst various low-power embedded devices that use the Internet to communicate with one another. It is predicted that the IoT will be widely deployed and will find applicability in various domains of life. Demands of IoT have lately attracted huge attention, and organizations are excited about the business value of the data that will be generated by deploying such networks. IoT has various security and privacy concerns for the end users that limit its proliferation. The emerging trends in embedded technologies and the Internet have enabled objects surrounding us to be interconnected with each other. We envision a future where IoT devices will be invisibly embedded in the environment around us and would be generating an enormous amount of data. . The Internet of Things (IoT) operates solely on local interactions among its components, which include various devices with communications capabilities. Because the IoT is a fully distributed computing network, it is important to mitigate any negative effects resulting from faults occurring in its components and to provide sustainable services. This paper focuses on an adaptive learning scheme which manages an IoT system fault to be fault tolerant. In particular, it handles a fault management scheme for the self-organizing software platform (SoSp), a platform on which IoT services connected to various IT devices are deployed. The proposed fault management scheme enables SoSp to provide situation aware IoT services without loss of data and state.
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