A Unified Mobility Management Architecture for Interworked Heterogeneous Mobile Networks

The buzzword of this decade has been convergence: the convergence of telecommunications, Internet, entertainment, and information technologies for the seamless provisioning of multimedia services across different network types. Thus the future Next Generation Mobile Network (NGMN) can be envisioned as a group of co-existing heterogeneous mobile data networking technologies sharing a common Internet Protocol (IP) based backbone. In such all-IP based heterogeneous networking environments, ongoing sessions from roaming users are subjected to frequent vertical handoffs across network boundaries. Therefore, ensuring uninterrupted service continuity during session handoffs requires successful mobility and session management mechanisms to be implemented in these participating access networks. Therefore, it is essential for a common interworking framework to be in place for ensuring seamless service continuity over dissimilar networks to enable a potential user to freely roam from one network to another. For the best of our knowledge, the need for a suitable unified mobility and session management framework for the NGMN has not been successfully addressed as yet. This can be seen as the primary motivation of this research. Therefore, the key objectives of this thesis can be stated as: To propose a mobility-aware novel architecture for interworking between heterogeneous mobile data networks To propose a framework for facilitating unified real-time session management (inclusive of session establishment and seamless session handoff) across these different networks. In order to achieve the above goals, an interworking architecture is designed by incorporating the IP Multimedia Subsystem (IMS) as the coupling mediator between dissipate mobile data networking technologies. Subsequently, two different mobility management frameworks are proposed and implemented over the initial interworking architectural design. The first mobility management framework is fully handled by the IMS at the Application Layer. This framework is primarily dependant on the IMS’s default session management protocol, which is the Session Initiation Protocol (SIP). The second framework is a combined method based on SIP and the Mobile IP (MIP) protocols, which is essentially operated at the Network Layer. An analytical model is derived for evaluating the proposed scheme for analyzing the network Quality of Service (QoS) metrics and measures involved in session mobility management for the proposed mobility management frameworks. More precisely, these analyzed QoS metrics include vertical handoff delay, transient packet loss, jitter, and signaling overhead/cost. The results of the QoS analysis indicates that a MIP-SIP based mobility management framework performs better than its predecessor, the Pure-SIP based mobility management method. Also, the analysis results indicate that the QoS performances for the investigated parameters are within acceptable levels for real-time VoIP conversations. An OPNET based simulation platform is also used for modeling the proposed mobility management frameworks. All simulated scenarios prove to be capable of performing successful VoIP session handoffs between dissimilar networks whilst maintaining acceptable QoS levels. Lastly, based on the findings, the contributions made by this thesis can be summarized as: The development of a novel framework for interworked heterogeneous mobile data networks in a NGMN environment. The final design conveniently enables 3G cellular technologies (such as the Universal Mobile Telecommunications Systems (UMTS) or Code Division Multiple Access 2000 (CDMA2000) type systems), Wireless Local Area Networking (WLAN) technologies, and Wireless Metropolitan Area Networking (WMAN) technologies (e.g., Broadband Wireless Access (BWA) systems such as WiMAX) to interwork under a common signaling platform. The introduction of a novel unified/centralized mobility and session management platform by exploiting the IMS as a universal coupling mediator for real-time session negotiation and management. This enables a roaming user to seamlessly handoff sessions between different heterogeneous networks. As secondary outcomes of this thesis, an analytical framework and an OPNET simulation framework are developed for analyzing vertical handoff performance. This OPNET simulation platform is suitable for commercial use

Building Programmable Wireless Networks: An Architectural Survey

In recent times, there have been a lot of efforts for improving the ossified Internet architecture in a bid to sustain unstinted growth and innovation. A major reason for the perceived architectural ossification is the lack of ability to program the network as a system. This situation has resulted partly from historical decisions in the original Internet design which emphasized decentralized network operations through co-located data and control planes on each network device. The situation for wireless networks is no different resulting in a lot of complexity and a plethora of largely incompatible wireless technologies. The emergence of "programmable wireless networks", that allow greater flexibility, ease of management and configurability, is a step in the right direction to overcome the aforementioned shortcomings of the wireless networks. In this paper, we provide a broad overview of the architectures proposed in literature for building programmable wireless networks focusing primarily on three popular techniques, i.e., software defined networks, cognitive radio networks, and virtualized networks. This survey is a self-contained tutorial on these techniques and its applications. We also discuss the opportunities and challenges in building next-generation programmable wireless networks and identify open research issues and future research directions.Comment: 19 page

A business planning framework for WiMAX applications

Mobile networking refers to wireless technologies which provide communications between devices. Applications for mobile networking have a broad scope as they can be applied to many situations in either industrial or commercial sectors. The challenge for firms is to better match market-induced variability to the organizational issues and systems necessary for technological innovation. This chapter develops a business planning framework for mobile networking applications. This framework recognises the fluidity of the situation when trying to anticipate and model emerging wireless applications. The business planning framework outlined in this chapter is a generic model which can be used by companies to assess the business case for applications utilizing mobile networking technologies

Deep Learning in Mobile and Wireless Networking: A Survey

The rapid uptake of mobile devices and the rising popularity of mobile applications and services pose unprecedented demands on mobile and wireless networking infrastructure. Upcoming 5G systems are evolving to support exploding mobile traffic volumes, agile management of network resource to maximize user experience, and extraction of fine-grained real-time analytics. Fulfilling these tasks is challenging, as mobile environments are increasingly complex, heterogeneous, and evolving. One potential solution is to resort to advanced machine learning techniques to help managing the rise in data volumes and algorithm-driven applications. The recent success of deep learning underpins new and powerful tools that tackle problems in this space. In this paper we bridge the gap between deep learning and mobile and wireless networking research, by presenting a comprehensive survey of the crossovers between the two areas. We first briefly introduce essential background and state-of-the-art in deep learning techniques with potential applications to networking. We then discuss several techniques and platforms that facilitate the efficient deployment of deep learning onto mobile systems. Subsequently, we provide an encyclopedic review of mobile and wireless networking research based on deep learning, which we categorize by different domains. Drawing from our experience, we discuss how to tailor deep learning to mobile environments. We complete this survey by pinpointing current challenges and open future directions for research