Mobility modeling and management for next generation wireless networks

Mobility modeling and management in wireless networks are the set of tasks performed in order to model motion patterns, predict trajectories, get information on mobiles\u27 whereabouts and to make use of this information in handoff, routing, location management, resource allocation and other functions. In the literature, the speed of mobile is often and misleadingly referred to as the level of mobility, such as high or low mobility. This dissertation presents an information theoretic approach to mobility modeling and management, in which mobility is considered as a measure of uncertainty in mobile\u27s trajectory, that is, the mobility is low if the trajectory of a mobile is highly predictable even if the mobile is moving with high speed. On the other hand, the mobility is high if the trajectory of the mobile is highly erratic. Based on this mobility modeling concept, we classify mobiles into predictable and non-predictable mobility classes and optimize network operations for each mobility class. The dynamic mobility classification technique is applied to various mobility related issues of the next generation wireless networks such as location management, location-based services, and energy efficient routing in multihop cellular networks

Mobility Management, Quality of Service, and Security in the Design of Next Generation Wireless Network

The next generation wireless network needs to provide seamless roaming among various access technologies in a heterogeneous environment. In allowing users to access any system at anytime and anywhere, the performance of mobility-enabled protocols is important. While Mobile IPv6 is generally used to support macro-mobility, integrating Mobile IPv6 with Session Initiation Protocol (SIP) to support IP traffic will lead to improved mobility performance. Advanced resource management techniques will ensure Quality of Service (QoS) during real-time mobility within the Next Generation Network (NGN) platform. The techniques may use a QoS Manager to allow end-to-end coordination and adaptation of Quality of Service. The function of the QoS Manager also includes dynamic allocation of resources during handover. Heterogeneous networks raise many challenges in security. A security entity can be configured within the QoS Manager to allow authentication and to maintain trust relationships in order to minimize threats during system handover. The next generation network needs to meet the above requirements of mobility, QoS, and security

Mobility support across hybrid IP-based wireless environment : review of concepts, solutions and related issues.

The 4G or Beyond 3G wireless networks is consist of IP-based heterogeneous access networks from 3G cellular, WiFi, WiMAX to other emerging access technologies such as mesh networks. The key objective of designing the next generation wireless networks is to support of mobile subscribers. To support the mobile host in the hybrid wireless access technologies, many solutions based on network protocol stack have been proposed in the literature. In this article, after review of mobility concepts, a special attention is given to some of the mobility management methods as well as handover techniques across various wireless access networks. We have also compared the major mobility protocols in each layer for their features. Finally, some of the open issues that needed to be addressed in mobility management protocol in the next generation wireless networks are outlined

IP-Based Mobility Management and Handover Latency Measurement in heterogeneous environments

One serious concern in the ubiquitous networks is the seamless vertical handover management between different wireless technologies. To meet this challenge, many standardization organizations proposed different protocols at different layers of the protocol stack. The Internet Engineering Task Force (IETF) has different groups working on mobility at IP level in order to enhance mobile IPv4 and mobile IPv6 with different variants: HMIPv6 (Hierarchical Mobile IPv6), FMIPv6 (Fast Mobile IPv6) and PMIPv6 (Proxy Mobile IPv6) for seamless handover. Moreover, the IEEE 802.21 standard provides another framework for seamless handover. The 3GPP standard provides the Access Network and Selection Function (ANDSF) to support seamless handover between 3GPP – non 3GPP networks like Wi-Fi, considered as untrusted, and WIMAX considered as trusted networks. In this paper, we present an in-depth analysis of seamless vertical handover protocols and a handover latency comparison of the main mobility management approaches in the literature. The comparison shows the advantages and drawbacks of every mechanism in order to facilitate the adoption of the convenient one for vertical handover within Next Generation Network (NGN) environments. Keywords: Seamless vertical handover, mobility management protocols, IEEE 802.21 MIH, handover latenc

Mobility management: deployment and adaptability aspects through mobile data traffic analysis

The expected boost in mobile data traffic and the evolution towards the next generation of networks are making cellular operators reconsider whether current approaches for handling mobility could be improved, according to the characteristics of the mobile traffic that actually flows through real networks. In this work, we make use of extensive analysis of real network traces to infer the main characteristics of mobile data traffic for a particular operator. Our analysis focuses on the features related to mobility, i.e., location information, number of handovers, or duration of the data traffic exchange. New techniques to gather the mobility characteristics of the user based on data and control packets correlation are designed and applied to compare the gains of deploying different mobility management approaches.The research leading to these results has received funding from the EU Seventh Framework Programme (FP7/2007-2013) under grant agreement 318115 (Connectivity management for eneRgy Op- timised Wireless Dense networks, CROWD). The work of Antonio de la Oliva has also been funded by the EU H2020 5G-Crosshaul Project (grant no. 671598)

Distributed All-IP Mobility Management Architecture Supported by the NDN Overlay

Two of the most promising candidate solutions for realizing the next-generation all-IP mobile networks are Mobile IPv6 (MIPv6), which is the host-based and global mobility supporting protocol, and Proxy MIPv6 (PMIPv6), which is the network-based and localized mobility supporting protocol. However, the unprecedented growth of mobile Internet traffic has resulted in the development of distributed mobility management (DMM) architecture by the Internet engineering task force DMM working group. The extension of the basic MIPv6 and PMIPv6 to support their distributed and scalable deployment in the future is one of the major goals of the DMM working group. We propose an all-IP-based mobility management architecture that leverages the concept of Named Data Networking (NDN), which is a distributed content management and addressing architecture. In the proposed solution, mobility support services are distributed among multiple anchor points at the edge of the network, thereby enabling a flat architecture that exploits name-based routing in NDN. Our approach overcomes some of the major limitations of centralized IP mobility management solutions, by extending existing routing protocol and mobility management architecture, to distribute the mobility management function of anchor points in the IP network and optimize the transmission path of mobile traffic

ERMO2 algorithm: an energy efficient mobility management in mobile cloud computing system for 5G heterogeneous networks

Recently, mobile devices are becoming the primary platforms for every user who always roam around and access the cloud computing applications. Mobile cloud computing (MCC) combines the both mobile and cloud computing, which provides optimal services to the mobile users. In next-generation mobile environments, mainly due to the huge number of mobile users in conjunction with the small cell size and their portable information‟s, the influence of mobility on the network performance is strengthened. In this paper, we propose an energy efficient mobility management in mobile cloud computing (E2M2MC2) system for 5G heterogeneous networks. The proposed E2M2MC2 system use elective repeat multi-objective optimization (ERMO2) algorithm to determine the best clouds based on the selection metrics are delay, jitter, bit error rate (BER), packet loss, communication cost, response time, and network load. ERMO2 algorithm provides energy efficient management of user mobility as well as network resources. The simulation results shows that the proposed E2M2MC2 system helps in minimizing delay, packet loss rate and energy consumption in a heterogeneous network

User mobility prediction and management using machine learning

The next generation mobile networks (NGMNs) are envisioned to overcome current user mobility limitations while improving the network performance. Some of the limitations envisioned for mobility management in the future mobile networks are: addressing the massive traffic growth bottlenecks; providing better quality and experience to end users; supporting ultra high data rates; ensuring ultra low latency, seamless handover (HOs) from one base station (BS) to another, etc. Thus, in order for future networks to manage users mobility through all of the stringent limitations mentioned, artificial intelligence (AI) is deemed to play a key role automating end-to-end process through machine learning (ML). The objectives of this thesis are to explore user mobility predictions and management use-cases using ML. First, background and literature review is presented which covers, current mobile networks overview, and ML-driven applications to enable user’s mobility and management. Followed by the use-cases of mobility prediction in dense mobile networks are analysed and optimised with the use of ML algorithms. The overall framework test accuracy of 91.17% was obtained in comparison to all other mobility prediction algorithms through artificial neural network (ANN). Furthermore, a concept of mobility prediction-based energy consumption is discussed to automate and classify user’s mobility and reduce carbon emissions under smart city transportation achieving 98.82% with k-nearest neighbour (KNN) classifier as an optimal result along with 31.83% energy savings gain. Finally, context-aware handover (HO) skipping scenario is analysed in order to improve over all quality of service (QoS) as a framework of mobility management in next generation networks (NGNs). The framework relies on passenger mobility, trains trajectory, travelling time and frequency, network load and signal ratio data in cardinal directions i.e, North, East, West, and South (NEWS) achieving optimum result of 94.51% through support vector machine (SVM) classifier. These results were fed into HO skipping techniques to analyse, coverage probability, throughput, and HO cost. This work is extended by blockchain-enabled privacy preservation mechanism to provide end-to-end secure platform throughout train passengers mobility

A survey of cellular IP in the next generation of network protocol

The main objective of mobility management in wireless data communication is to allows networks to search and locate (location management) mobile users while maintaining it’s connections (handoff management) whenever users move into a new network. Management of location and handoff divided into macromobility (for managing inter-domain network) and micromobility (for managing intra-domain network).For macromobility management, IETF has adopted Mobile IP and had perform very well for managing inter-domain mobility.However Mobile IP suffer from handover performance in intradomain network which is inefficient for mobile user with frequent handoff. For this defect,cellular IP protocol has been considered for managing intra-domain network for it’s fast handoff and interoperability with Mobile IP. This paper we present a review of different micro-mobility management protocols available to date. We also discuss various issues and challenges regarding mobility management for the next generation network protocol