Comparison Data Traffic Scheduling Techniques for Classifying QoS over 5G Mobile Networks

Enhancing Quality of Service (QoS) in mobile networks is the key aim for mobile operators. Mobile networks transport several forms of data traffic for real-time applications (i.e., video monitoring). These applications need to get the advantage of QoS adaptation. Numerous scheduling techniques are utilized at the router to assure the QoS of the mobile networks. Upcoming 5G mobile networks will be launched; hence, Human-Type-Communication (HTC) and Machine-to-Machine (M2M) data traffic are expected to increase dramatically over mobile networks, which results in growing the capacity and raising high data rates. These networks are expected to face challenges in cases of Radio Access Network (RAN) overload and congestion due to the massive smart devices data traffic with various QoS requirements. This paper presents a comparison for data traffic scheduling techniques, which are Priority Queuing (PQ), First-In-First-Out (FIFO) and Weighted Fair Queuing (WFQ). We consider to select a suitable data traffic scheduling technique in terms of QoS provisioning and helping 5G network, also we propose models and algorithms for efficiently utilized the smallest unit of a RAN in a relay node by aggregating and slicing the data traffic of several M2M devices

Data Traffic Model in Machine to Machine Communications over 5G Network Slicing

The recent advancements in cellular communication domain have resulted in the emergence of Machine-to-Machine applications, in support of the wide range and coverage provision, low costs, and high mobility. 5G network standards represent a promising technology to support the future of Machine-to-Machine data traffic. In recent years, Human-Type-Communication traffic has seen exponential growth over cellular networks, which resulted in increasing the capacity and higher data rates. These networks are expected to face challenges such as explosion of the data traffic due to the future of smart devices data traffic with various Quality of Service requirements. This paper proposes a novel data traffic aggregation model and algorithm along with a new 5G network slicing based on classification and measuring the data traffic to satisfy Quality of Service for smart systems in a smart city environment. In our proposal, 5G radio resources are efficiently utilized as the smallest unit of a physical resource block in a relay node by aggregating the data traffic of several Machine-to-Machine devices as separate slices based on Quality of Service for each application. OPNET is used to assess the performance of the proposed model. The simulated 5G data traffic classes include file transfer protocol, voice over IP, and video users

The role of big data in smart city

The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the existing communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model that can manage big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data