Mobile 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).

Parking lots in densely crowded environments such as stadiums, theaters and hospitals provide great opportunities for vehicular cloud services. A cloud environment formed by individual vehicles, where each vehicle offers its resources as a service has shown feasible practices in 5G network scenarios. Moreover, resource management in 5G must be achieved in accordance with user-centric QoS requirements. In alignment with this, a key enabler of the user-centric service scheme is Network Slicing. The formation of multiple slices in such a dense environment, the congestion between sender and receiver, and resource management and allocation are topics of current research. This paper has the following contribution: First, a framework of Vehicular Clouds being restricted to individual slices in 5G cellular networks is proposed. Second, a queuing strategy for congestion control in a densely crowded environment such as parking lots is designed. Finally, a resource allocation algorithm that enables maximum matching between the tasks to be executed and the candidate slices is developed. The novelty of this approach comes from the fact that congestion control is performed at the Access Points (AP). We do this by introducing a control module that makes queuing decisions at the time of request arrival. By incorporating control module in AP, our aim is to provide AP resources in terms of transmission period to different slices, thereby, allowing WiFi resources to be shared along with the 5G radio resources. The performance benefits of the proposed solution has been investigated through simulation tests.