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Learning Automata Based Multipath Multicasting in Cognitive Radio Networks
Asad Ali,Junaid Qadir,Adeel Baig 한국통신학회 2015 Journal of communications and networks Vol.17 No.4
Cognitive radio networks (CRNs) have emerged as a promising solution to the problem of spectrum under utilization and artificial radio spectrum scarcity. The paradigm of dynamic spectrum access allows a secondary network comprising of secondary users (SUs) to coexist with a primary network comprising of licensed primary users (PUs) subject to the condition that SUs do not cause any interference to the primary network. Since it is necessary for SUs to avoid any interference to the primary network, PU activity precludes attempts of SUs to access the licensed spectrum and forces frequent channel switching for SUs. This dynamic nature of CRNs, coupled with the possibility that an SU may not share a common channel with all its neighbors, makes the task of multicast routing especially challenging. In this work, we have proposed a novel multipath on-demand multicast routing protocol for CRNs. The approach of multipath routing, although commonly used in unicast routing, has not been explored for multicasting earlier. Motivated by the fact that CRNs have highly dynamic conditions, whose parameters are often unknown, the multicast routing problem is modeled in the reinforcement learning based framework of learning automata. Simulation results demonstrate that the approach of multipath multicasting is feasible, with our proposed protocol showing a superior performance to a baseline stateof- the-art CRN multicasting protocol.
Priority-Based Cloud Computing Architecture for Multimedia-Enabled Heterogeneous Vehicular Users
Ali, Amjad,Liu, Hongwu,Bashir, Ali Kashif,El-Sappagh, Shaker,Ali, Farman,Baig, Adeel,Park, Daeyoung,Kwak, Kyung Sup Hindawi Limited 2018 Journal of advanced transportation Vol.2018 No.-
<P>In recent days, vehicles have been equipped with smart devices that offer various multimedia-related applications and services, such as smart driving assistance, traffic congestions, weather forecasting, road safety alarms, and many entertainment and comfort applications. Thus, these smart vehicles produce a large amount of multimedia-related data that require fast and real-time processing. However, due to constrained computing and storage capacities, such huge amounts of multimedia-related data cannot be processed in on-board standalone devices. Thus, multimedia cloud computing (MCC) has emerged as an economical and scalable computing technology that can process multimedia-related data efficiently while providing improved Quality of Service (QoS) to vehicular users from anywhere, at any time and on any device, at reduced costs. However, there are certain challenges, such as fast service response time and resource cost optimization, that can severely affect the performance of the MCC. Therefore, to tackle these issues, in this paper, we propose a dynamic priority-based architecture for the MCC. In the proposed scheme, we divide multimedia processing into four different subphases, while computing resources to each computing server are assigned dynamically, according to the workload, in order to process multimedia tasks according to the multimedia user Quality of Experience (QoE) requirements. The performance of the proposed scheme is evaluated in terms of service response time and resource cost optimization using the CloudSim simulator.</P>