CCN interest forwarding strategy as Multi-Armed Bandit model with delays

We consider Content Centric Network (CCN) interest forwarding problem as a Multi-Armed Bandit (MAB) problem with delays. We investigate the transient behaviour of the epsilon-greedy, tuned epsilon-greedy and Upper Confidence Bound (UCB) interest forwarding policies. Surprisingly, for all the three policies very short initial exploratory phase is needed. We demonstrate that the tuned epsilon-greedy algorithm is nearly as good as the UCB algorithm, commonly reported as the best currently available algorithm. We prove the uniform logarithmic bound for the tuned epsilon-greedy algorithm in the presence of delays. In addition to its immediate application to CCN interest forwarding, the new theoretical results for MAB problem with delays represent significant theoretical advances in machine learning discipline

Queuing Modelling and Performance Analysis of Content Transfer in Information Centric Networks

With the rapid development of multimedia services and wireless technology, new generation of network traffic like short-form video and live streaming have put tremendous pressure on the current network infrastructure. To meet the high bandwidth and low latency needs of this new generation of traffic, the focus of Internet architecture has moved from host-centric end-to-end communication to requester-driven content retrieval. This shift has motivated the development of Information-Centric Networking (ICN), a promising new paradigm for the future Internet. ICN aims to improve information retrieval on the Internet by identifying and routing data using unified names. In-network caching and the use of a pending interest table (PIT) are two key features of ICN that are designed to efficiently handle bulk data dissemination and retrieval, as well as reduce bandwidth consumption. Performance analysis has been and continues to be key research interests of ICN. This thesis starts with the evaluation of content delivery delays in ICN. The main component of delay is composed of propagation delay, transmission delay,processing delay and queueing delay. To characterize the main components of content delivery delay, queueing network theory has been exploited to coordinate with cache miss rate in modelling the content delivery time in ICN. Moreover, different topologies and network conditions have been taken into account to evaluate the performance of content transfer in ICN. ICN is intrinsically compatible with wireless networks. To evaluate the performance of content transfer in wireless networks, an analytical model to evaluate the mean service time based on consumer and provider mobility has been proposed. The accuracy of the analytical model is validated through extensive simulation experiments. Finally, the analytical model is used to evaluate the impact of key metrics, such as the cache size, content size and content popularity on the performance of PIT and content transfer in ICN. Pending interest table (PIT) is one of the essential components of the ICN forwarding plane, which is responsible for stateful routing in ICN. It also aggregates the same interests to alleviate request flooding and network congestion. The aggregation feature of PIT improves performance of content delivery in ICN. Thus, having an analytical model to characterize the impact of PIT on content delivery time could allow for a more precise evaluation of content transfer performance. In parallel, if the size of the PIT is not properly determined, the interest drop rate may be too high, resulting in a reduction in quality of service for consumers as their requests have to be retransmitted. Furthermore, PIT is a costly resource as it requires to operate at wirespeed in the forwarding plane. Therefore, in order to ensure that interests drop rate less than the requirement, an analytical model of PIT occupancy has been developed to determine the minimum PIT size. In this thesis, the proposed analytical models are used to efficiently and accurately evaluate the performance of ICN content transfer and investigate the key component of ICN forwarding plane. Leveraging the insights discovered by these analytical models, the minimal PIT size and proper interest timeout can be determined to enhance the performance of ICN. To widen the outcomes achieved in the thesis, several interesting yet challenging research directions are pointed out

Clustering algorithm for D2D communication in next generation cellular networks : thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand

Next generation cellular networks will support many complex services for smartphones, vehicles, and other devices. To accommodate such services, cellular networks need to go beyond the capabilities of their previous generations. Device-to-Device communication (D2D) is a key technology that can help fulfil some of the requirements of future networks. The telecommunication industry expects a significant increase in the density of mobile devices which puts more pressure on centralized schemes and poses risk in terms of outages, poor spectral efficiencies, and low data rates. Recent studies have shown that a large part of the cellular traffic pertains to sharing popular contents. This highlights the need for decentralized and distributive approaches to managing multimedia traffic. Content-sharing via D2D clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. Different studies have established that D2D communication in clusters can improve spectral and energy efficiency, achieve low latency while increasing the capacity of the network. To achieve effective content-sharing among users, appropriate clustering strategies are required. Therefore, the aim is to design and compare clustering approaches for D2D communication targeting content-sharing applications. Currently, most of researched and implemented clustering schemes are centralized or predominantly dependent on Evolved Node B (eNB). This thesis proposes a distributed architecture that supports clustering approaches to incorporate multimedia traffic. A content-sharing network is presented where some D2D User Equipment (DUE) function as content distributors for nearby devices. Two promising techniques are utilized, namely, Content-Centric Networking and Network Virtualization, to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multi-factor clustering algorithm is proposed for grouping the DUEs sharing a common interest. Various performance parameters such as energy consumption, area spectral efficiency, and throughput have been considered for evaluating the proposed algorithm. The effect of number of clusters on the performance parameters is also discussed. The proposed algorithm has been further modified to allow for a trade-off between fairness and other performance parameters. A comprehensive simulation study is presented that demonstrates that the proposed clustering algorithm is more flexible and outperforms several well-known and state-of-the-art algorithms. The clustering process is subsequently evaluated from an individual user’s perspective for further performance improvement. We believe that some users, sharing common interests, are better off with the eNB rather than being in the clusters. We utilize machine learning algorithms namely, Deep Neural Network, Random Forest, and Support Vector Machine, to identify the users that are better served by the eNB and form clusters for the rest of the users. This proposed user segregation scheme can be used in conjunction with most clustering algorithms including the proposed multi-factor scheme. A comprehensive simulation study demonstrates that with such novel user segregation, the performance of individual users, as well as the whole network, can be significantly improved for throughput, energy consumption, and fairness

Optimized and Automated Machine Learning Techniques Towards IoT Data Analytics and Cybersecurity

The Internet-of-Things (IoT) systems have emerged as a prevalent technology in our daily lives. With the wide spread of sensors and smart devices in recent years, the data generation volume and speed of IoT systems have increased dramatically. In most IoT systems, massive volumes of data must be processed, transformed, and analyzed on a frequent basis to enable various IoT services and functionalities. Machine Learning (ML) approaches have shown their capacity for IoT data analytics. However, applying ML models to IoT data analytics tasks still faces many difficulties and challenges. The first challenge is to process large amounts of dynamic IoT data to make accurate and informed decisions. The second challenge is to automate and optimize the data analytics process. The third challenge is to protect IoT devices and systems against various cyber threats and attacks. To address the IoT data analytics challenges, this thesis proposes various ML-based frameworks and data analytics approaches in several applications. Specifically, the first part of the thesis provides a comprehensive review of applying Automated Machine Learning (AutoML) techniques to IoT data analytics tasks. It discusses all procedures of the general ML pipeline. The second part of the thesis proposes several supervised ML-based novel Intrusion Detection Systems (IDSs) to improve the security of the Internet of Vehicles (IoV) systems and connected vehicles. Optimization techniques are used to obtain optimized ML models with high attack detection accuracy. The third part of the thesis developed unsupervised ML algorithms to identify network anomalies and malicious network entities (e.g., attacker IPs, compromised machines, and polluted files/content) to protect Content Delivery Networks (CDNs) from service targeting attacks, including distributed denial of service and cache pollution attacks. The proposed framework is evaluated on real-world CDN access log data to illustrate its effectiveness. The fourth part of the thesis proposes adaptive online learning algorithms for addressing concept drift issues (i.e., data distribution changes) and effectively handling dynamic IoT data streams in order to provide reliable IoT services. The development of drift adaptive learning methods can effectively adapt to data distribution changes and avoid data analytics model performance degradation

Maritime expressions:a corpus based exploration of maritime metaphors

This study uses a purpose-built corpus to explore the linguistic legacy of Britain’s maritime history found in the form of hundreds of specialised ‘Maritime Expressions’ (MEs), such as TAKEN ABACK, ANCHOR and ALOOF, that permeate modern English. Selecting just those expressions commencing with ’A’, it analyses 61 MEs in detail and describes the processes by which these technical expressions, from a highly specialised occupational discourse community, have made their way into modern English. The Maritime Text Corpus (MTC) comprises 8.8 million words, encompassing a range of text types and registers, selected to provide a cross-section of ‘maritime’ writing. It is analysed using WordSmith analytical software (Scott, 2010), with the 100 million-word British National Corpus (BNC) as a reference corpus. Using the MTC, a list of keywords of specific salience within the maritime discourse has been compiled and, using frequency data, concordances and collocations, these MEs are described in detail and their use and form in the MTC and the BNC is compared. The study examines the transformation from ME to figurative use in the general discourse, in terms of form and metaphoricity. MEs are classified according to their metaphorical strength and their transference from maritime usage into new registers and domains such as those of business, politics, sports and reportage etc. A revised model of metaphoricity is developed and a new category of figurative expression, the ‘resonator’, is proposed. Additionally, developing the work of Lakov and Johnson, Kovesces and others on Conceptual Metaphor Theory (CMT), a number of Maritime Conceptual Metaphors are identified and their cultural significance is discussed