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).

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In recent years, enormous growth has been witnessed in the computational and storage capabilities of mobile devices. However, much of this computational and storage capabilities are not always fully used. On the other hand, popularity of mobile edge computing which aims to replace the traditional centralized powerful cloud with multiple edge servers is rapidly growing. In particular, applications having strict latency requirements can be best served by the mobile edge clouds due to a reduced round-trip delay. In this paper we propose a Multi-Path TCP (MPTCP) enabled mobile device cloud (MDC) as a replacement to the existing TCP based or D2D device cloud techniques, as it effectively makes use of the available bandwidth by providing much higher throughput as well as ensures robust wireless connectivity. We investigate the congestion in mobile-device cloud formation resulting mainly due to the message passing for service providing nodes at the time of discovery, service continuity and formation of cloud composition. We propose a user space agent called congestion handler that enable offloading of packets from one sub-flow to the other under link quality constraints. Further, we discuss the benefits of this design and perform preliminary analysis of the system.

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Today, mobile applications advancements have overcome limited device capabilities by offloading to costly public cloud. As the edge computing paradigm began to take precedence, a mobile device cloud (MDC) formed at the edge based on idle intra-device resources emerged. This is a result of a customized user-centric composition service request for a time-bound application. Herein, devices volunteer their intra-device resources for producing a compute environment in turn satisfying the needs of the consumer. Now, with the growth of device technology and the available interfaces for accessing multiple radio technologies, a new transport layer protocol called Multipath TCP was introduced in literature. This protocol enables multiple sub-flows to join for transmitting data simultaneously. However, in scenarios like formation of device clouds, there are issues pertaining to sub-flows that are involved in a device cloud composition. One such issue is the management of sub-flow buffer. As each of these sub-flows have their own respective buffering and characteristic delays, it leads to sub-optimal performance in term of buffer occupancy. Thereby, degrading the quality of the device cloud composition. To this end, we propose an OS side architecture that plays a crucial role in managing the traffic coming from different flows. We model an agent that works conservatively satisfying Kleinrock's law and show a proof of concept experiment.

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Recently, the edge resource provisioning schemes were defined considering the low-latency Mobile Edge Computing (MEC) paradigm. Most of these models only consider batterypowered devices like smart-phones, thus are agnostic to the
energy harvesting techniques that achieves a green MEC system. Further, most of the studies on MEC assume unlimited edge resources which is not the case as it is with the conventional data-centers (public clouds). Hence, unrestricted use of edge resources is not ideal. This work mainly considers two problems: (1) the offloading of data traffic from the Internet of Things (IoT) devices that rely on energy harvesting to the MEC entities and (2) assignment of the resources at the MEC. The novelty of this paper lies in the energy scavenging based architecture
that is developed over the Contiki OS. Secondly, saving the energy for computations to maximize the lifetime of the sensing nodes by performing the execution of the computationallyintensive tasks at the edge which is a single hop away. The proposed architecture uses the ambient triggers to form the sensor network and establish links with computationally capable resources located at the edge. Further, a mathematical model to manage the resources at the edge is proposed. Finally, we evaluate a threshold-policy for optimizing the resources participating in an edge computation service for an IoT scenario and discuss the
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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.

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