The sixth generation (6G) of mobile networks promises transformative capabilities in terms of, amount others, higher data rates, lower latency and ubiquitous coverage, but achieving these goals sustainably poses significant challenges. A promising solution lies in Cell-Free massive Multiple-Input Multiple-Output (CF-mMIMO) networks, where a dense deployment of geographically distributed Access Points (APs), coordinated by one or more Central Processing Units (CPUs), cooperatively serve User Equipments (UEs). While CF-mMIMO networks offer improved spectral efficiency and more spatially uniform service, their dense deployments can result in high energy consumption. As MNOs typically design their networks such that Quality of Service (QoS) targets are met during peak hours, the network is left significantly over-dimensioned during off-peak hours. This thesis addresses this inefficiency by proposing a low-complexity, heuristic Sleep Mode Management (SMM) algorithm that reduces energy usage by switching off unneeded APs while maintaining acceptable QoS and without compromising coverage.

The proposed SMM algorithm supports multiple AP power states: active, light sleep and deep sleep. It incorporates realistic transition times between these states. Relying solely on practically available information such as long-term Channel State Information (CSI) and previously achieved data rates, the algorithm dynamically decides which APs can be temporarily put into a sleep mode. Importantly, it ensures population coverage is maintained and it preserves UE QoS based on a 10th UE throughput percentile target.

The proposed SMM algorithm is evaluated using a system-level simulator that models a realistic scenario based on the city center of Amsterdam, including lamppost-based AP deployments and a realistic basis for the spatial traffic distribution and daily traffic fluctuations. Simulation results demonstrate that the proposed SMM algorithm reduces the daily energy consumption of a CF-mMIMO network by up to 17.11\% with the best overall configuration. If the parameters of the SMM algorithm are allowed to be adaptively tuned to the traffic load, the daily energy consumption can be reduced by up to 21.54%. This thesis not only contributes a novel SMM algorithm but also provides guidance for AP deployment strategies in 6G CF-mMIMO networks, determining that a higher number of lower-antenna-count APs can yield better QoS than fewer, more higher-antenna-count APs at the cost of energy efficiency.

Rotterdam Werkt! is a network of fourteen organizations in the Rotterdam area in the Netherlands. Their goal is to increase labor mobility between these organizations through sharing vacancies, exchanging employees and partaking in joint projects. Rotterdam Werkt! has tasked us with creating a central platform on which all vacancies are automatically combined from the websites of all the organizations in the network. The two main challenges of the project were to gather the vacancies from all the organizations affiliated with Rotterdam Werkt! and allow their recruiters to search and filter through them. This meant that a significant amount of research needed to be done in order to find a suitable scraping tool as well as a suitable search engine. Whilst gathering the vacancies, we ran into the problem that each website was significantly different in the way it is rendered. Furthermore, we also needed to categorize the data correctly such that it becomes searchable in the search engine. Lastly, the retrieval function needed to be optimized such that the most relevant vacancies would be returned for a given query. In order to assess whether recruiters could use the search engine effectively in practice, an evaluation of the effectiveness of the search engine was done. Three retrieval functions were compared based on a significance test of several effectiveness measures that indicate to what extent a retrieval function is able to retrieve relevant documents, or in this case, vacancies. Out of the three, the retrieval function that scored the highest was chosen to be used in the platform, so that recruiters will have a bigger chance to find the vacancies they will be looking for. In the end, we consider our project to be a success. We managed to scrape all vacancies from all the websites of the organizations in Rotterdam Werkt! and to combine these on a centralized platform. Furthermore, the search engine evaluation allowed us to select the best vacancy retrieval function out of the three evaluated retrieval functions. However, more work can still be put into evaluating the search engine in the future by testing more retrieval functions based on more vacancy data, so that the search functionality can be further improved.