Multi Level Integrity Management in LTE/LTE-A Networks

Kasmi, Ouafae; Baïna, Amine; Bellafkih, Mostafa

doi:10.25046/aj020384

Cited by 3 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy Logic is a mathematical discipline used to handle nonlinear uncertainties that exist in physical systems to model human experience and human decision making behavior, it has been introduced by L. A. Zadeh in 1965 [10]. This concept is characterized by fuzzy sets, membership functions, linguistic variable, fuzzy operations, fuzzy If-Then rules, and Fuzzy Inference Systems [11] determines the degree to which, a given input or output belongs to a specific set.…”

Section: B Fuzzy Logicmentioning

confidence: 99%

See 1 more Smart Citation

Priority-Based Event Management using Fuzzy Logic for an IoT-BPM Architecture

Ismaili-Alaoui

Kasmi

Baïna

et al. 2019

2019 IEEE 12th Conference on Service-Oriented Computing and Applications (SOCA)

Self Cite

View full text Add to dashboard Cite

Internet of things (IoT) world is growing at a breathtaking pace. This new paradigm shift affects all the enterprise architecture layers from infrastructure to business. Organizations are nowadays faced with new challenges to keep their quality of service and competitive advantage over other rival organizations. Business Process Management (BPM) is a field among others that will be affected by this new technology. Both IoT and BPM communicate through events, and effective and efficient management of those events ensures a better communication channel between the IoT physical layer and the Business layer. However, the huge amount of those IoT generated events and sometimes the subtle difference between their criticality level, generate uncertainty regarding their priority level determination. In this paper, we propose a fuzzy logic-based event management approach to estimate the criticality level of the incoming IoT events using two fuzzy inference systems (FIS) and to manage the priority of business process instances triggered by those events. A case study is presented and the obtained results from our simulations demonstrate the benefit of our approach and allowed us to confirm the efficiency of our assumptions.

show abstract

Section: B Fuzzy Logicmentioning

confidence: 99%

“…This output is divided into four linguistic values as follows: Low, Average, Serious and Very Serious. The membership functions of all inputs and output are represented by triangular membership functions because this type of functions is the most frequently used and gives better results [11].…”

Section: B Membership Functionsmentioning

confidence: 99%