Architecture and Modeling of Machine to Machine Communications on Heterogeneous Networks

Abstract

With the rapid growth of the citizen services to exchange the data (voice, text, video…etc.) with anyone, anytime and anyplace with any terminals (devices, sensors, actuators). M2M communications are becoming an intelligent technology to fulfill the people requirements without human being, in addition, to utilize the resources in our daily life. Internet of Things (IoT) technology is coming as an extension and an evolution of the M2M applications. IoT technology coexists with the present cellular communication networks for developing an emerging 5G technology. Billions of IoT big data result from the rapid growth of IoT services upon human beneficiaries, especially in the smart city use case, which exhibits several and different IoT data traffic characteristics and then traffic data complexity. Accordingly, it is difficult to carry and handle IoT data traffic services over Human type communication. The traffic modeling concept of different IoT data characteristics is envisioned in this thesis to fulfill this problem. We propose a novel ON/OFF traffic modeling technique to recover IoT data characteristics of diverse IoT smart city services. In a novel model, the characteristics of IoT smart city use cases are formatted and summarized into five major traffic patterns. To prove the concept, IoT smart city architecture, smart home case study, and realistic smart home networks have been designed and implemented as a case study of massive IoT smart city tracks. The experimental measures present several traffic profiles that are generated from a pilot according to be modeled into theoretical models by Easy-fit statistical tool. A proposed five traffic patterns of the novel ON/OFF Traffic modeling technique are concluded and verified in the experimental results.