Browsing by Author "Zaman, Faisal"
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Item A mobility management architecture for seamless delivery of 5G-IoT services(Institute of Electrical and Electronics Engineers Inc., 2019) Balasubramanian, Venkatraman; Zaman, Faisal; Aloqaily, Moayad; Ridhawi, Ismaeel Al; Jararweh, Yaser; Salameh, Haythem BanyMobile Edge Computing (MEC) and Network Slicing techniques have a potential to augment 5G-IoT network services. Telecommunication operators use a diverse set of radio access technologies to provide services for users. Mobility management is one such service that needs attention for new 5G deployments. The QoS requirements in 5G networks are user specific. Network slicing along with MEC has been promoted as a key enabler for such on-demand service schemes. This paper focuses on radio resource access across heterogeneous networks for mobile roaming users. A unified service architecture is proposed enabling seamless handover between a 5G (New Generation Core) service and a 4G (Evolved Packet Core) service via the network slicing paradigm. An identifier-locator (I-L) concept that allows active source-IP sessions is used to handle the seamless hand-over. Signaling costs, service disruptions and other resource reservation requirements are considered in the evaluation to assure that profit for mobile edge operators is achieved. Simulation experiments are considered to provide performance comparisons against the state-of-the-art Distributed Mobility Management Protocol (DMM). © 2019 IEEE.Item Reinforcing the Edge: Autonomous Energy Management for Mobile Device Clouds(Institute of Electrical and Electronics Engineers Inc., 2019) Balasubramanian, Venkatraman; Zaman, Faisal; Aloqaily, Moayad; Alrabaee, Saed; Gorlatova, Maria; Reisslein, MartinThe collaboration among mobile devices to form an edge cloud for sharing computation and data can drastically reduce the tasks that need to be transmitted to the cloud. Moreover, reinforcement learning (RL) research has recently begun to intersect with edge computing to reduce the amount of data (and tasks) that needs to be transmitted over the network. For battery-powered Internet of Things (IoT) devices, the energy consumption in collaborating edge devices emerges as an important problem. To address this problem, we propose an RL-based Droplet framework for autonomous energy management. Droplet learns the power-related statistics of the devices and forms a reliable group of resources for providing a computation environment on-the-fly. We compare the energy reductions achieved by two different state-of-the-art RL algorithms. Further, we model a reward strategy for edge devices that participate in the mobile device cloud service. The proposed strategy effectively achieves a 10% gain in the rewards earned compared to state-of-the-art strategies. © 2019 IEEE.