A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things
AbstractThe Internet of things (IoT) technology is developing rapidly, and the IoT services are penetrating broadly into every aspect of people’s lives. As the large amount of services grows dramatically, how to discover and select the best services dynamically to satisfy the actual needs of users in the IoT service set, the elements of which have the same function, is an unavoidable issue. Therefore, for the robustness of the IoT system, evaluating the quality level of the IoT service to provide a reference for the users choosing the most appropriate service has become a hot topic. Most of the current methods just use some static data to evaluate the quality of the service and ignore the dynamic changing trend of the service performance. In this paper, an estimation mechanism for the quality level of the IoT service based on fuzzy logic is conducted to grade the quality of the service. Specifically, the comprehensive factors are taken into account according to the defined level changing rules and the effect of the service in the previous execution process, so that it can provide users with an effective reference. Experiments are carried out by using a simulated service set. It is shown that the proposed algorithm can estimate the quality level of the service more comprehensively and reasonably, which is evidently superior to the other two common methods, i.e., the estimating method by a Randomization Test (RT) and the estimating method by a Single Test in Steps (STS). View Full-Text
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Jia, B.; Hao, L.; Zhang, C.; Chen, D. A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things. Sensors 2018, 18, 2190.
Jia B, Hao L, Zhang C, Chen D. A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things. Sensors. 2018; 18(7):2190.Chicago/Turabian Style
Jia, Bing; Hao, Lifei; Zhang, Chuxuan; Chen, Dong. 2018. "A Dynamic Estimation of Service Level Based on Fuzzy Logic for Robustness in the Internet of Things." Sensors 18, no. 7: 2190.
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