Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

Abstract

Summary Recently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required. In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.

Optimizing resources allocation in a heterogeneous cloud radio access network using machine learning

“…This function has a computational burden equal to (N + D + F)(M + M 2 ). According to Algorithm 1, the computational complexity of steps 2 to 5 is equivalent to 2 . This method is responsible for calculating the subchannel preference function, ranking each subchannel user, and controlling the constraints associated with each request.…”

“…Wireless communication has been an integral part of human society in recent years and covered many aspects of human life. Today, with the exponentially increasing of the mobile devices, the number of users, device‐to‐device (D2D) communication, 1,2 internet of things (IoT), 3,4 online games with high quality, social media, applications with high traffic consumption, and so on, the network traffic volume is also overgrowing. The main challenges may occur in dense areas with high connection density and very high mobility, such as office towers, subways, highways, railways, department stores, crowded residential areas, etc.…”