MOBILITY SUPPORT ARCHITECTURES FOR NEXT-GENERATION WIRELESS NETWORKS

With the convergence of the wireless networks and the Internet and the booming demand for multimedia applications, the next-generation (beyond the third generation, or B3G) wireless systems are expected to be all IP-based and provide real-time and non-real-time mobile services anywhere and anytime. Powerful and efficient mobility support is thus the key enabler to fulfil such an attractive vision by supporting various mobility scenarios. This thesis contributes to this interesting while challenging topic. After a literature review on mobility support architectures and protocols, the thesis starts presenting our contributions with a generic multi-layer mobility support framework, which provides a general approach to meet the challenges of handling comprehensive mobility issues. The cross-layer design methodology is introduced to coordinate the protocol layers for optimised system design. Particularly, a flexible and efficient cross-layer signalling scheme is proposed for interlayer interactions. The proposed generic framework is then narrowed down with several fundamental building blocks identified to be focused on as follows. As widely adopted, we assume that the IP-based access networks are organised into administrative domains, which are inter-connected through a global IP-based wired core network. For a mobile user who roams from one domain to another, macro (inter-domain) mobility management should be in place for global location tracking and effective handoff support for both real-time and non-real-lime applications. Mobile IP (MIP) and the Session Initiation Protocol (SIP) are being adopted as the two dominant standard-based macro-mobility architectures, each of which has mobility entities and messages in its own right. The work explores the joint optimisations and interactions of MIP and SIP when utilising the complementary power of both protocols. Two distinctive integrated MIP-SIP architectures are designed and evaluated, compared with their hybrid alternatives and other approaches. The overall analytical and simulation results shown significant performance improvements in terms of cost-efficiency, among other metrics. Subsequently, for the micro (intra-domain) mobility scenario where a mobile user moves across IP subnets within a domain, a micro mobility management architecture is needed to support fast handoffs and constrain signalling messaging loads incurred by intra-domain movements within the domain. The Hierarchical MIPv6 (HMIPv6) and the Fast Handovers for MIPv6 (FMIPv6) protocols are selected to fulfil the design requirements. The work proposes enhancements to these protocols and combines them in an optimised way. resulting in notably improved performances in contrast to a number of alternative approaches

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Clustering Arabic Tweets for Sentiment Analysis

The focus of this study is to evaluate the impact of linguistic preprocessing and similarity functions for clustering Arabic Twitter tweets. The experiments apply an optimized version of the standard K-Means algorithm to assign tweets into positive and negative categories. The results show that root-based stemming has a significant advantage over light stemming in all settings. The Averaged Kullback-Leibler Divergence similarity function clearly outperforms the Cosine, Pearson Correlation, Jaccard Coefficient and Euclidean functions. The combination of the Averaged Kullback-Leibler Divergence and root-based stemming achieved the highest purity of 0.764 while the second-best purity was 0.719. These results are of importance as it is contrary to normal-sized documents where, in many information retrieval applications, light stemming performs better than root-based stemming and the Cosine function is commonly used

Adaptive Railway Traffic Control using Approximate Dynamic Programming

Railway networks around the world have become challenging to operate in recent decades, with a mixture of track layouts running several different classes of trains with varying operational speeds. This complexity has come about as a result of the sustained increase in passenger numbers where in many countries railways are now more popular than ever before as means of commuting to cities. To address operational challenges, governments and railway undertakings are encouraging development of intelligent and digital transport systems to regulate and optimise train operations in real-time to increase capacity and customer satisfaction by improved usage of existing railway infrastructure. Accordingly, this thesis presents an adaptive railway traffic control system for realtime operations based on a data-based approximate dynamic programming (ADP) approach with integrated reinforcement learning (RL). By assessing requirements and opportunities, the controller aims to reduce delays resulting from trains that entered a control area behind schedule by re-scheduling control plans in real-time at critical locations in a timely manner. The present data-based approach depends on an approximation to the value function of dynamic programming after optimisation from a specified state, which is estimated dynamically from operational experience using RL techniques. By using this approximation, ADP avoids extensive explicit evaluation of performance and so reduces the computational burden substantially. In this thesis, formulations of the approximation function and variants of the RL learning techniques used to estimate it are explored. Evaluation of this controller shows considerable improvements in delays by comparison with current industry practices

An intelligent vertical handoff decision algorithm in next generation wireless networks

Philosophiae Doctor - PhDSeamless mobility is the missing ingredient needed to address the inefficient communication problems faced by the field workforces of service companies that are using field workforce automation solutions to streamline and optimise the operations of their field workforces in an increasingly competitive market place. The key enabling function for achieving seamless mobility and seamless service continuity is seamless handoffs across heterogeneous wireless access networks. A challenging issue in the multi-service next generation wireless network (NGWN) is to design intelligent and optimal vertical handoff decision algorithms, beyond traditional ones that are based on only signal strength, to determine when to perform a handoff and to provide optimal choice of access network technology among all available access networks for users equipped with multimode mobile terminals. The objective of the thesis research is to design such vertical handoff decision algorithms in order for mobile field workers and other mobile users equipped with contemporary multimode mobile devices to communicate seamlessly in the NGWN. In order to tackle this research objective, we used fuzzy logic and fuzzy inference systems to design a suitable handoff initiation algorithm that can handle imprecision and uncertainties in data and process multiple vertical handoff initiation parameters (criteria); used the fuzzy multiple attributes decision making method and context awareness to design a suitable access network selection function that can handle a tradeoff among many handoff metrics including quality of service requirements (such as network conditions and system performance), mobile terminal conditions, power requirements, application types, user preferences, and a price model; used genetic algorithms and simulated annealing to optimise the access network selection function in order to dynamically select the optimal available access network for handoff; and we focused in particular on an interesting use case: vertical handoff decision between mobile WiMAX and UMTS access networks. The implementation of our handoff decision algorithm will provide a network selection mechanism to help mobile users select the best wireless access network among all available wireless access networks, that is, one that provides always best connected services to user

Mobility Management and Congestion Control in Wireless Mesh Networks

Today, wireless mesh networks are increasingly popular. In order to be better adapted to the increasing number of offered services in telecommunications, many Quality of Service (QoS) problems are being considered. Some of the important issues are: admission control, congestion control, and handoff management of the network. Our research focuses on those issues individually and combining them together in order to find solutions to enhance the quality of service provided to each user as demanded in their SLA. A novel Markov Decision-based Admission Control and Routing (MDACR) algorithm is proposed. The MDACR algorithm finds a sub-optimal solution using the value iteration method. Admission rate increases for both types of user associations (handoff and new user association request), which is addressed by a proposed multi-homing admission and routing algorithm. This algorithm associates the user with two different access points. This is beneficial in a highly congested network, which permits a new routing metric to assure seamless handoff in the network. When a user is moving, MDACR algorithm finds a maximally jointed route with the old route, which decreases the handoff delay. Another aspect is considered in order to improve the QoS in WMN, which is the congestion control, a novel proactive approach is proposed. Where a Variable Order Markov (VOM) prediction model is introduced to predict the congestion status in each link in the network, a new route is established for the traffic based on the output of the VOM model, and the transmission rate is adjusted based on the link congestion status to increase the overall user satisfaction. Sub-optimal model is introduced and solved using Lagrange method. Based on the predicted link congestion, rerouting algorithm is implemented in order to insure load balancing and to mitigate congestion over WMN network. Our ultimate goal is to improve the QoS in WMN by dealing individually with the issues stated above and try to combine them together and provide QoS framework which deals with many types of services

Using information engineering to understand the impact of train positioning uncertainties on railway subsystems

Many studies propose new advanced railway subsystems, such as Driver Advisory System (DAS), Automatic Door Operation (ADO) and Traffic Management System (TMS), designed to improve the overall performance of current railway systems. Real time train positioning information is one of the key pieces of input data for most of these new subsystems. Many studies presenting and examining the effectiveness of such subsystems assume the availability of very accurate train positioning data in real time. However, providing and using high accuracy positioning data may not always be the most cost-effective solution, nor is it always available. The accuracy of train position information is varied, based on the technological complexity of the positioning systems and the methods that are used. In reality, different subsystems, henceforth referred to as ‘applications’, need different minimum resolutions of train positioning data to work effectively, and uncertainty or inaccuracy in this data may reduce the effectiveness of the new applications. However, the trade-off between the accuracy of the positioning data and the required effectiveness of the proposed applications is so far not clear. A framework for assessing the impact of uncertainties in train positions against application performance has been developed. The required performance of the application is assessed based on the characteristics of the railway system, consisting of the infrastructure, rolling stock and operational data. The uncertainty in the train positioning data is considered based on the characteristics of the positioning system. The framework is applied to determine the impact of the positioning uncertainty on the application’s outcome. So, in that way, the desired position resolution associated with acceptable application performance can be characterised. In this thesis, the framework described above is implemented for DAS and TMS applications to understand the influence of positioning uncertainty on their fundamental functions compared to base case with high accuracy (actual position). A DAS system is modelled and implemented with uncertainty characteristic of a Global Navigation Satellite System (GNSS). The train energy consumption and journey time are used as performance measures to evaluate the impact of these uncertainties compared to a base case. A TMS is modelled and implemented with the uncertainties of an on-board low-cost low-accuracy positioning system. The impact of positioning uncertainty on the modelled TMS is evaluated in terms of arrival punctuality for different levels of capacity consumption. The implementation of the framework for DAS and TMS applications determines the following: • which of the application functions are influenced by positioning uncertainty; • how positioning uncertainty influences the application output variables; • how the impact of positioning uncertainties can be identified, through the application output variables, whilst considering the impact of other railway uncertainties; • what is the impact of the underperforming application, due to positioning uncertainty, on the whole railway system in terms of energy, punctuality and capacity

Enhancing Networks via Virtualized Network Functions

University of Minnesota Ph.D. dissertation. May 2019. Major: Computer Science. Advisor: Zhi-Li Zhang. 1 computer file (PDF); xii, 116 pages.In an era of ubiquitous connectivity, various new applications, network protocols, and online services (e.g., cloud services, distributed machine learning, cryptocurrency) have been constantly creating, underpinning many of our daily activities. Emerging demands for networks have led to growing traffic volume and complexity of modern networks, which heavily rely on a wide spectrum of specialized network functions (e.g., Firewall, Load Balancer) for performance, security, etc. Although (virtual) network functions (VNFs) are widely deployed in networks, they are instantiated in an uncoordinated manner failing to meet growing demands of evolving networks. In this dissertation, we argue that networks equipped with VNFs can be designed in a fashion similar to how computer software is today programmed. By following the blueprint of joint design over VNFs, networks can be made more effective and efficient. We begin by presenting Durga, a system fusing wide area network (WAN) virtualization on gateway with local area network (LAN) virtualization technology. It seamlessly aggregates multiple WAN links into a (virtual) big pipe for better utilizing WAN links and also provides fast fail-over thus minimizing application performance degradation under WAN link failures. Without the support from LAN virtualization technology, existing solutions fail to provide high reliability and performance required by today’s enterprise applications. We then study a newly standardized protocol, Multipath TCP (MPTCP), adopted in Durga, showing the challenge of associating MPTCP subflows in network for the purpose of boosting throughput and enhancing security. Instead of designing a customized solution in every VNF to conquer this common challenge (making VNFs aware of MPTCP), we implement an online service named SAMPO to be readily integrated into VNFs. Following the same principle, we make an attempt to take consensus as a service in software-defined networks. We illustrate new network failure scenarios that are not explicitly handled by existing consensus algorithms such as Raft, thereby severely affecting their correct or efficient operations. Finally, we re-consider VNFs deployed in a network from the perspective of network administrators. A global view of deployed VNFs brings new opportunities for performance optimization over the network, and thus we explore parallelism in service function chains composing a sequence of VNFs that are typically traversed in-order by data flows