/library/oar/handle/123456789/120241 2025-11-04T21:49:55Z /library/oar/handle/123456789/131928 Title: Miniature implementation of an IoT‐based smart city Abstract: Ever since the idea of a Smart City was introduced, the Internet of Things (IoT) has been a key pillar of the technological aspect of Smart City development. Cities should fully grasp the advantages and opportunities of the IoT for Smart Cities since there is so much promise and opportunity in a wide variety of fields, including traffic management, urban mobility, security and healthcare. This project presents a scalable miniature implementation of an IoT‐based smart city model, using three physical nodes that perform air quality monitoring, parking sensing, and fire detection. The nodes communicate with a centralised server using NB‐IoT and CoAP protocols. The data ingested from said nodes is stored and visualised using InfluxDB and Grafana. The project evaluates the system from different perspectives, such as integration, load, and battery consumption. Integration testing indicated that all system components integrate properly with each other. Load testing demonstrated that the system can handle a reasonable number of requests without performance degradation. Battery consumption tests indicate that the physical nodes’ battery life lasts for several days. Description: B.Sc. (Hons)(Melit.) 2023-01-01T00:00:00Z /library/oar/handle/123456789/124754 Title: Toward a standardized way for reporting on energy efficiency in the metro area network Abstract: Energy is converted from one form to another through the activity of physical processes. The study of energy use, as it is converted from one form to another, therefore necessarily requires detailed understanding of the laws of physics that describe the behaviour of the entity responsible for the conversion (component: level 1 complexity). The complexity of the problem grows rapidly when these fundamental laws are not the ordinary means by which the behaviour of the entity is understood. This condition is common in systems: such aggregates encapsulate the behaviour of their components and obtain physical processes that are functions of the internal organization of these components (system of components: level 2 complexity). The complexity of the problem is compounded further when the ordinary means of interaction with the entity are no longer physical and material, but parametric representations of the entity’s function(s). These representations might be summarized as key performance indices; a more granular knowledge of the entity’s energy use may be obtained through study of the behaviour of its functions under a variety of operating conditions (multi-layered system of components: level 3 complexity). A fourth level in the hierarchy of complexity emerges with a localized system of systems; the fifth and final level of complexity is that of the geographically-dispersed system of systems. The complexity of the study of energy use by telecommunications networks falls into this fifth level. Several problems take root in this complexity. Diversity of components; diversity of systems; diversity of architectures; laxity in terminology; diversity of players, each interested in specific roles and layers, and abuse of abstractions are just some of the highly impactful ones. These problems lead to poorly defined studies of energy use, incorrect cross-comparison of studies, weak analytical technique and over-extrapolated prognoses. It must be conceded that, notwithstanding grave limitations, these works have sown interest in the field and spurred research into better methods. Perhaps this is a common trajectory in the development of our scientific knowledge of this wonderful world. I have primarily addressed the spatial aspect of the problem domain. Seeded by the observed laxity in architectural description and terminology, and driven by a documented failure arising out of misunderstanding of architectures, I have modelled the access portion of the metro area network in sufficient detail to support coherent analysis. Study was restricted to the metro area of the telecommunications network, as this was found to be the extent within a globally-spanning telecommunications network where fastest traffic growth was predicted. The market has been surveyed and the input gathered has been applied to validate my understanding, correct it, and to establish a firm foundation for future cycles of architecturally rigorous descriptions in support of the energy analyst. This work develops mutual understanding between industrial and academic practitioners in two disciplines: sustainability in ICT, and telecommunications operations. The two groups have been approaching one another over the past ten – fifteen years, and much effort has been put in by both sides to cooperate. Sustainability researchers want to reduce telecommunications’ Scope 1 (and beyond) greenhouse gas emissions; moreover, telecommunications network operators are keen to minimize the significant impact that energy use has on their operational expenditure. However, sustainability researchers have been hindered by the complexity of the object of their study, by the immaturity of methods, by the lack of methodology, and it is only recently that some consensus has emerged on good practice and the actual size of the problem (which, in the 1 – 2 % range of GHG emissions, is well short of more dire anticipations). On the other hand, while the operators are willing to share judiciously crafted questions, the detail of network architecture is not a matter of the public domain. The desire for rapprochement is there, but the modus operandi is still somewhat elusive. This work offers a contribution towards a solution of this problem. The standardized methodology of the implementational model has been applied to map the access network, and work is in progress to describe aggregation and metro-core. The models can be integrated with the software-defined networking paradigm. Since the implementational model describes functions and locates them relative to reference points, then it can be used within controllers to interact with service functions in the data plane. The prerequisite is standardized application programming interfaces, and standardized data models that incorporate energy and/or power usage. The former role can be fulfilled by NETCONF (RFC 6241); the latter role can be fulfilled by YANG (RFC 6020), but a valid contender for the latter role is the Green Abstraction Layer (ES 203 237, ES 203 682). The Green Abstraction Layer’s potential is investigated and its likelihood of adoption in the current data-plan driven exchange of link-state data is found to be poor. Regardless of whether GAL or YANG fulfil NETCONF’s content and operations layers, the energy-related notification data in the content layer cannot be generated without real-time power use models, as virtualization containers are not amenable to direct measurement of power use. The field of models is surveyed in a novel manner and contentious problems, productive approaches and significant developments are elicited. Description: Ph.D.(Melit.) 2023-01-01T00:00:00Z /library/oar/handle/123456789/120582 Title: Routing in VANETs Based on Q-learning and received signal strength information Abstract: Vehicular Ad hoc Networks (VANETs) present a difficult challenge for both vehicular and infrastructure routing. This study focuses on addressing this challenge, particularly in old Eurocentric urban environments, by introducing a routing decision criterion based on Ad-hoc On-Demand Distance Vector (AODV) protocol and enhanced by Q-learning. The reinforcement learning approach is mainly empowered by the received signal strength indication between each exchange, which aims to aid in optimal route prediction in such environments. Additionally, this research introduces novel Q-learning transitional reward factors that occur during the route request and reply stages, as well as in Hello packet exchanges. The performance of the proposed algorithm is evaluated on OMNeT++ and SUMO. Simulation results demonstrate comparative packet delivery ratios and end-to-end delay efficiencies, with a notable performance edge in scenarios involving common obstructions such as buildings. Description: M.Sc. (Melit.) 2023-01-01T00:00:00Z /library/oar/handle/123456789/120581 Title: Enhancing UAV detection through machine learning utilising LTE radio measurements Abstract: The main aim of the dissertation is to detect UAVs (Unmanned Aerial Vehicles) based upon the implementation of a machine learning method. In order to achieve this ultimate objective, a UAV simulator is implemented on MATLAB which provides a flexible environment whereby UAV flight paths can be simulated at a number of different heights. The simulator is employed to extract a variety of LTE radio measurements from shape files supplied by EPIC, a prominent telecommunications company in Malta. These measurements pertain to a drone simulation conducted at different altitudes, as well as at ground level. A number of features extracted from the shape files are then used to train three different machine learning algorithms which include Support Vector Machines, a Multi-Layer Perceptron model and the Random Forest algorithm. A number of different test cases are designed for different heights, as well as some cases that increase the amount of features used in order to ascertain whether there will be any effects on the results produced. These machine learning algorithms are then evaluated on the basis of a number of KPIs (Key Performance Indicators) to determine how good they are at detecting drones. Some of these KPIs include specificity and sensitivity. Using three features to train the machine learning models, the Multi-Layer Perceptron model produces the most accurate results, with a sensitivity score of up to 95% in certain scenarios. When the number of features increases to five from three, a number of improvements are noted for the Random Forest and Support Vector Machine algorithms, however the Multi-Layer Perceptron model is shown to regress, which warrants further investigation. Description: M.Sc. (Melit.) 2023-01-01T00:00:00Z