Investigation of an intelligent personalised service recommendation system in an IMS based cellular mobile network

Success or failure of future information and communication services in general and mobile communications in particular is greatly dependent on the level of personalisations they can offer. While the provision of anytime, anywhere, anyhow services has been the focus of wireless telecommunications in recent years, personalisation however has gained more and more attention as the unique selling point of mobile devices. Smart phones should be intelligent enough to match user’s unique needs and preferences to provide a truly personalised service tailored for the individual user. In the first part of this thesis, the importance and role of personalisation in future mobile networks is studied. This is followed, by an agent based futuristic user scenario that addresses the provision of rich data services independent of location. Scenario analysis identifies the requirements and challenges to be solved for the realisation of a personalised service. An architecture based on IP Multimedia Subsystem is proposed for mobility and to provide service continuity whilst roaming between two different access standards. Another aspect of personalisation, which is user preference modelling, is investigated in the context of service selection in a multi 3rd party service provider environment. A model is proposed for the automatic acquisition of user preferences to assist in service selection decision-making. User preferences are modelled based on a two-level Bayesian Metanetwork. Personal agents incorporating the proposed model provide answers to preference related queries such as cost, QoS and service provider reputation. This allows users to have their preferences considered automatically

Personalised Handoff Decision for Seamless Roaming in Next Generation of Wireless Networks

The past three decades have experienced a phenomenal emergence of several wireless networks and technologies. This next generation of wireless networks (4G) will be integrated into one IP-backbone to offer improved services to the user. The features of 4G include: wide coverage, high data rates, seamless roaming and personalisation. This paper presents a personalised handoff decision method to offer personalisation in seamless roaming for the next generation of wireless networks. This is done by assigning profiles to different users with different preferences and using these profiles to offer personalised handoff. The integration of these two important features of 4G networks will provide the end user the ability to choose their own preferred networks while they roam freely between heterogeneous networks

A survey on intelligent computation offloading and pricing strategy in UAV-Enabled MEC network: Challenges and research directions

The lack of resource constraints for edge servers makes it difficult to simultaneously perform a large number of Mobile Devices’ (MDs) requests. The Mobile Network Operator (MNO) must then select how to delegate MD queries to its Mobile Edge Computing (MEC) server in order to maximize the overall benefit of admitted requests with varying latency needs. Unmanned Aerial Vehicles (UAVs) and Artificial Intelligent (AI) can increase MNO performance because of their flexibility in deployment, high mobility of UAV, and efficiency of AI algorithms. There is a trade-off between the cost incurred by the MD and the profit received by the MNO. Intelligent computing offloading to UAV-enabled MEC, on the other hand, is a promising way to bridge the gap between MDs' limited processing resources, as well as the intelligent algorithms that are utilized for computation offloading in the UAV-MEC network and the high computing demands of upcoming applications. This study looks at some of the research on the benefits of computation offloading process in the UAV-MEC network, as well as the intelligent models that are utilized for computation offloading in the UAV-MEC network. In addition, this article examines several intelligent pricing techniques in different structures in the UAV-MEC network. Finally, this work highlights some important open research issues and future research directions of Artificial Intelligent (AI) in computation offloading and applying intelligent pricing strategies in the UAV-MEC network

A Task Offloading Framework for Energy Saving on Mobile Devices using Cloud Computing

Over the last decade, mobile devices have become popular among people, and their number is ever growing because of the computing functionality they offer beyond primary voice communication. However, mobile devices are unable to accommodate most of the computing demand as long as they suffer the limited energy supply caused by the capacity of their small battery to store only a relatively small amount of energy. The literature describes several specialist techniques proposed in academia and industry that save the mobile device energy and solve this problem to some extent but not satisfactorily. Task offloading from mobile devices to cloud computing is a promising technique for tackling the problem especially with the emergence of high-speed wireless networks and the ubiquitous resources from the cloud computing. Since task offloading is in its nascent age, it lacks evaluation and development in-depth studies. In this dissertation, we proposed an offloading framework to make task offloading possible to save energy for mobile devices. We achieved a great deal of progress toward developing a realistic offloading framework. First, we examined the feasibility of exploiting the offloading technique to save mobile device energy using the cloud as the place to execute the task instead of executing it on the mobile device. Our evaluation study reveals that the offloading does not always save energy; in cases where the energy for the computation is less than the energy for communication no energy is saved. Therefore, the need for the offloading decision is vital to make the offloading beneficial. Second, we developed mathematical models for the energy consumption of a mobile device and its applications. These models were then used to develop mathematical models that estimate the energy consumption on the networking and the computing activities at the application level. We modelled the energy consumption of the networking activity for the Transmission Control Protocol (TCP) over Wireless Local Area Network (WLAN), the Third Generation (3G), and the Fourth Generation (4G) of mobile telecommunication networks. Furthermore, we modelled the energy consumption of the computing activity for the mobile multi-core Central Processing Unit (CPU) and storage unit. Third, we identified and classified the system parameters affecting the offloading decision and built our offloading framework based on them. In addition, we implemented and validated the proposed framework experimentally using a real mobile device, cloud, and application. The experimental results reveal that task offloading is beneficial for mobile devices given that in some cases it saves more than 70% of the energy required to execute a task. Additionally, our energy models accurately estimate the energy consumption for the networking and computing activities. This accuracy allows the offloading framework to make the correct decision as to whether or not offloading a given task saves energy. Our framework is built to be applicable to modern mobile devices and expandable by considering all system parameters that have impact on the offloading decision. In fact, the experimental validation proves that our framework is practical to real life scenarios. This framework gives researchers in the field useful tools to design energy efficient offloading systems for the coming years when the offloading will be common.4 month

A quality of experience approach in smartphone video selection framework for energy efficiency

Online video streaming is getting more common in the smartphone device nowadays. Since the Corona Virus (COVID-19) pandemic hit all human across the globe in 2020, the usage of online streaming among smartphone user are getting more vital. Nevertheless, video streaming can cause the smartphone energy to drain quickly without user to realize it. Also, saving energy alone is not the most significant issues especially if with the lack of attention on the user Quality of Experience (QoE). A smartphones energy management is crucial to overcome both of these issues. Thus, a QoE Mobile Video Selection (QMVS) framework is proposed. The QMVS framework will govern the tradeoff between energy efficiency and user QoE in the smartphone device. In QMVS, video streaming will be using Dynamic Video Attribute Pre-Scheduling (DVAP) algorithm to determine the energy efficiency in smartphone devices. This process manages the video attribute such as brightness, resolution, and frame rate by turning to Video Content Selection (VCS). DVAP is handling a set of rule in the Rule Post-Pruning (RPP) method to remove an unused node in list tree of VCS. Next, QoE subjective method is used to obtain the Mean Opinion Score (MOS) of users from a survey experiment on QoE. After both experiment results (MOS and energy) are established, the linear regression technique is used to find the relationship between energy consumption and user QoE (MOS). The last process is to analyze the relationship of VCS results by comparing the DVAP to other recent video streaming applications available. Summary of experimental results demonstrate the significant reduction of 10% to 20% energy consumption along with considerable acceptance of user QoE. The VCS outcomes are essential to help users and developer deciding which suitable video streaming format that can satisfy energy consumption and user QoE

A Cognitive Routing Framework for Reliable Communication in IoT for Industry 5.0

Industry 5.0 requires intelligent self-organized, self- managed and self-monitoring applications with ability to analyze and predict both the human as well as machine behaviors across interconnected devices. Tackling dynamic network behavior is a unique challenge for IoT applications in industry 5.0. Knowledge- Defined Networks (KDN) bridges this gap by extending SDN architecture with Knowledge Plane (KP) which learns the net- work dynamics to avoid sub-optimal decisions. Cognitive Routing leverages the Sixth-Generation (6G) Self-Organised-Networks with self-learning feature. This paper presents a self-organized cognitive routing frame- work for a KDN which uses link-reliability as a routing metric. It reduces end-to-end latency by choosing the most-reliable path with minimal probability of route-flapping. The proposed framework pre-calculates all possible paths between every pair of nodes and ensures self-healing with a constant-time convergence. An experimental test-bed has been developed to benchmark the proposed framework against the industry stranded Link- state and distance-vector routing algorithms SPF and DUAL respectively

QoS Abstraction Layer in 4G Access Networks

Tese de Mestrado. Redes e Serviços de Comunicação. Faculdade de Engenharia. Universidade do Porto. 200