The NAS research section invites MSc students to join our group, and do exciting research on the forefront of Network Science. If you are interested in one of these projects, contact us by e-mailing Maksim Kitsak at** ****.**

If you already have **your own project** in mind, **propose it to us**! We will advise you on **feasibility**, and judge whether there’s a **match**.

**See the instructions for the information on how to apply for a MSc thesis at the NAS Group.**

**Do NOT email Prof. Van Mieghem directly.**

# Available MSc topics

## Non-consensus Opinion Models with Unreliable Links

**Introduction**

Opinion dynamics in social networks have been extensively studied, often under the assumption that individuals eventually reach consensus. However, in many real-world scenarios, opinions do not necessarily converge to a single view, especially when the connections between individuals are unreliable. This leads to non-consensus models, where interactions between individuals, influenced by random or faulty communication links, result in the persistence of diverse opinions. Understanding these dynamics is crucial for applications ranging from political polarization to the spread of misinformation and social influence.

This project explores the dynamics of **non-consensus opinion models** in networks with unreliable links. By modeling these links as probabilistic or subject to failure over time, we aim to study how unreliable communication affects opinion formation, stability, and the overall structure of belief systems in a network.

**Project**

The aim of this project is to investigate the behavior of opinion dynamics in non-consensus models under the influence of unreliable or failing communication links. Specifically, the project will focus on:

· Developing or adapting opinion models where network links are subject to failure or random communication errors.

· Studying the long-term dynamics of opinions and how they stabilize (or remain divergent) in networks with unreliable connections.

· Comparing the impact of different types of unreliable links (e.g., random failure, time-varying reliability) on the persistence of opinion diversity.

· Analyzing how network structure influences the sensitivity of the opinion model to unreliable links and determining whether certain network topologies are more robust to communication failures.

Additionally, the project will involve **developing a tailored training plan** based on your interests and future career aspirations. This plan will help you gain specific skills in network modeling, dynamical systems, and simulation techniques that align with your professional goals.

Key tasks include:

· Developing mathematical and simulation models for opinion dynamics with unreliable links.

· Investigating how the introduction of unreliable communication links alters the convergence or divergence of opinions.

· Studying the role of network topology in mitigating the effects of unreliable communication.

· Verifying the models and hypotheses through simulations and data analysis.

**Requirements**

This project is ideal for final-stage students in Artificial Intelligence, Computer Science, Mathematics, Electrical Engineering, or related fields. The ideal candidate should have strong programming skills, a background in network science or dynamical systems, and an interest in opinion dynamics or social network analysis. Independence, communication skills, and the ability to work remotely are also important.

**To Apply **

Contact Xinhan Liu,

**References**

1. Liu, X., Achterberg, M.A. & Kooij, R. Mean-field dynamics of the non-consensus opinion model. Appl Netw Sci 9, 47 (2024). https://doi.org/10.1007/s41109-024-00656-w

2. Shao J, Havlin S, Stanley HE (2009) Dynamic opinion model and invasion percolation. Phys Rev Lett 103(1):018701

3. Li Q, Braunstein LA, Wang H, Shao J, Stanley HE, Havlin S (2013) Non-consensus opinion models on complex networks. J Stat Phys 151:92–112

## Network Topology Optimization Based on Network Reliability

**Introduction**

Network reliability plays a critical role in a wide range of fields, from communication networks ensuring efficient data transmission to power grids maintaining energy distribution, and even in social networks facilitating information flow. The topology of a network directly impacts its performance, robustness, and functionality. By optimizing the network topology, we can enhance its reliability, making it more resilient to node or link failures, and more efficient in information or resource distribution. Although various methods and metrics have been developed to assess and improve network reliability, understanding the relationships between these metrics remains an open question.

**Project**

The aim of this project is to analyze and optimize the topology of complex networks with a focus on maximizing their reliability. Specifically, this project will explore the relationship between the **network reliability polynomial** and other key reliability metrics such as **effective graph resistance, number of spanning trees, and algebraic connectivity**. By studying these connections, we aim to better understand how different aspects of network topology contribute to overall reliability.

Key tasks include:

· Investigating the mathematical relationships between the network reliability polynomial and other graph reliability measures.

· Systematically comparing the performance of these reliability metrics on real-world networks.

· Analyzing how these metrics influence each other and can be used to optimize network topology for enhanced reliability.

· Developing optimization strategies for network structures based on the insights gained from the study of these metrics.

· Verifying the effectiveness of the proposed optimization strategies through simulations and empirical analysis.

Additionally, the project will involve** developing a tailored training plan** based on your interests and future career aspirations. This plan will help you grow specific technical and analytical skills that align with your aspirations, ensuring that the experience gained during the project support your professional goals.

**Requirements**

This project is suitable for final-stage students in Artificial Intelligence, Computer Science, Mathematics, Electrical Engineering, or a related field. The successful candidate should have strong programming skills, a background in Network Science, and experience with graph theory. A high level of independence, communication skills, and the ability to work remotely are essential.

**To apply**

Contact Xinhan Liu,

## Spreading processes on networks: who is the best spreader?

**Introduction**

Spreading is a ubiquitous process that describes many essential activities in society. Understanding how spreading occurs through social interaction networks is crucial for developing strategies to either prevent the spread of diseases or cascading failures and facilitate the rapid distribution of information. Identifying the best spreaders in a network is essential because these nodes (spreaders) are the most effective at amplifying the spread and maximizing the overall reach of the process. Over the years, scientists constantly introduce new metrics in order to measure specific features of specific graphs. This research will focus on the comparison of existing metrics and provide new insights into the influential node identification problem.

**Project**

The aim of this project is to analyze the differences between various node influence metrics specifically designed to identify the 'best spreader nodes'. You will study spreading processes and existing graph metrics for complex networks and conduct a systematic comparative analysis of their performance on real data.

**Requirements**

You are in the final stages of your degree in artificial intelligence, computer science, mathematics, electrical engineering or a similar degree. The successful student is expected to have strong programming skills, and have some background in Network Science. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

**To apply**

Contact Sergey Shvydun,

**References**

[1] Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature physics, 6(11), 888-893.

[2] Van Mieghem, P., Devriendt, K., & Cetinay, H. (2017), "Pseudoinverse of the Laplacian and best spreader node in a network", Physical Review E, vol. 96, No. 3, p 032311.

[3] Hernández, J. M., & Van Mieghem, P. (2011). Classification of graph metrics. Delft University of Technology: Mekelweg, The Netherlands.

## Human mobility: Can you quantify group behaviour through temporal graph models?

**Introduction**

In the past years, human mobility has received a surge of interest from the research community, mainly in the context of COVID-19. Posed as a dynamic temporal graph modelling problem, the main body of literature on human mobility bases itself on link-based approaches. Most of these models are not able to accurately reproduce the typical dynamics of contacts between individuals as they fail to learn a governing graph generative process. Our research proposes to model dynamic human contacts from first principles and develop a framework for quantifying the class of processes which generate temporal graphs.

The Random Walker Induced temporal Graph (RWIG) model is conceptually intuitive: a collection of random walkers traverse a Markov graph in discrete time steps, with co-location of any pair of walkers in a Markov state resulting in a link in their contact graph. We have developed a comprehensive theory around the probability distribution of the RWIG model, and we wish to further understand how the process behaves in the steady-state.

**Project**

The student is expected to:

- Develop a good understanding of the RWIG model
- Perform large-scale simulations of RWIG using various input parameters
- Conduct both theoretical and empirical analysis of the probability distribution of RWIG graphs when the random walkers are in the steady-state.

**Requirements**

You are in the final stage of your degree in computer science, mathematics, electrical engineering or a similar degree. You are expected to have strong programming skills and experience in Markov theory and network science. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

**To apply**

Contact David Almasan,

## Modeling Temporal Networks

**Introduction**

Modelling time-evolving networks (such as social, biological and infrastructural) is an open problem that has attracted researchers from a diverse range of fields [1]. The ability to model real networks facilitates our understanding of the nature and the timing of observed evolution, but it may also provide some useful intuition about the future behavior of the network, thereby making valuable predictions. Currently, most existing modelling solutions of temporal networks mimic the real-world networks in terms of certain topological features (number of links, clustering coefficient, degree distribution, connected components, motifs, etc.) but they do not allow us to produce an accurate graph topology [2]. Other approaches focus on temporal link prediction using graph statistics or deep learning methods that fail to provide knowledge about the dynamical process itself [3]. Recently, we have proposed the LG-gen model for modelling temporal networks from a system identification perspective [4]. Our experiments show that many real systems can be well approximated by the LG-gen model.

**Project**

This project is aimed as one step forward towards a deeper understanding of network evolution processes. Typical questions to be answered include:

*Modelling*: how well the LG-gen model can approximate real unweighted and weighted networks?*Link Prediction*: what is the performance of LG-gen in comparison to the state-of-art methods such as graph neural networks (GNN)?

In this project, you will study existing graph modelling and link prediction techniques for complex networks and conduct a systematic comparative analysis of their performance on real data.

**Requirements**

You are in the final stages of your degree in artificial intelligence, computer science, mathematics, electrical engineering or a similar degree. The successful student is expected to have strong programming skills, and have some background in Network Science You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

**To apply**

Contact Sergey Shvydun,

## Extending quantum device benchmarks to photonic quantum devices

**Introduction**

In 2021 ATOS proposed a protocol for benchmarking current quantum co-processors, dubbed Q-Score, which is application-centric, hardware-agnostic and scalable to quantum advantage processor sizes and beyond [1]. Computing the Q-Score entails solving the Max Cut problem for increasingly large graphs, however, this can be naturally extended to solving a different hard optimization problem. ATOS applied the Q-Score by using simulators of gate-based quantum hardware. Recently, TNO has presented the first computation of the Q-Score on an actual quantum device, namely the D-WAVE, a commercially available device based upon the concept of quantum annealing. While [1] used a Quantum Approximate Optimization Algorithm (QAOA) to solve the Max Cut optimization, [2] used a Quadratic Unconstrained Binary Optimization (QUBO) formulation.

The aim of this project is to extend the benchmarked quantum technologies (i.e. gate-based and quantum annealing) to another promising near-term technology, namely photonic quantum computers, which use Gaussian boson sampling (GBS) [3].

**Project**

The goal of this project is three-fold::

- Determine the Q-Score for a photonic quantum device
- Determine the Q-Score using a real gate-based quantum device
- Benchmark three different quantum devices (gate-based, quantum annealers and photonic) on another hard optimization problem, such as finding the maximum clique in a graph or densest subgraph of size
*k*

You will perform this assignment at TNO (Netherlands Organization for Applied Scientific Research), where you will be supervised by an expert from the department of Applied Crypto & Quantum Algorithms.

**Requirements**

You are in the final stage of your degree in computer science, mathematics, electrical engineering or a similar degree and have some background in the field of quantum. You have experience in programming, networks, etc. and optimisation. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

**To apply**

Part of the application is an interview at TNO. However, as a first step you need to contact the supervisor from NAS: Rob Kooij (

**References**

[1] S. Martiel et al., "Benchmarking Quantum Coprocessors in an Application-Centric, Hardware-Agnostic, and Scalable Way," in *IEEE Trans. on Quant. Eng**.*, vol. 2, pp. 1-11, 2021

[2] W. van der Schoot et al., "Evaluating the Q-score of Quantum Annealers", *IEEE International Conference on Quantum Software (QSW)*, Barcelona, Spain, 2022 pp. 9-16.

[3] Thomas R. Bromley et al., “Applications of near-term photonic quantum computers: software and algorithms”, May 2020, IOP Publ., *Quantum Sci. and Technol.*, Vol. 5, No. 3

## Quantum approach for the optimal arbitrage problem

**Introduction**

Arbitrage strategies represent a wide range of ways to make a profit from differing prices for the same asset in different markets. A particular example hereof is cross-currency arbitrage: one might make a small profit when converting a sum of money from EUR to USD, then to GPB then back to EUR, because of the favorable transaction costs. This problem can be modeled in terms of a directed graph, where each node represents a currency and each weight marks the conversion cost. Finding the optimal arbitrage on such a graph is equivalent to finding the most profitable cycle (of to finding the negative cycle with the most negative weight). Checking if at least one arbitration opportunity exists can be solved in polynomial time with algorithms such as Bellman-Ford. However, finding the best arbitrage opportunity (the most negative cycle of arbitrary length) is NP-hard. This problem can be formulated as a Quadratic Unconstrained Binary Problem (QUBO) which can then be implemented on a quantum annealer such as D-Wave. An example can be found in [1].

**Project**

The goal of this project is to study whether quantum annealing can be a suitable method for solving the optimal arbitrage problem in comparison to classical algorithms. Typical questions to be answered include:

- can we expect better or faster solutions using quantum annealing?
- what is the scale of the problems that can be solved with quantum annealing?
- can the problem formulation be extended to non-fixed weights?
- in which other applications is this problem relevant?

You will perform this assignment at TNO (Netherlands Organization for Applied Scientific Research), where you will be supervised by an expert from the department of Cybersecurity and Robustness.

**Requirements**

You are in the final stages of your degree in artificial intelligence, computer science, mathematics, electrical engineering or a similar degree and have some background in the field of quantum. You have experience in programming, networks, etc. and an interest in artificial intelligence and optimisation. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

**To apply**

Part of the application is an interview at TNO. However, as a first step you need to contact the supervisor from NAS: Rob Kooij

**References**

[1] 1Qbit white-paper: Finding Optimal Arbitrage Opportunities Using a Quantum Annealer (2016)

## Quantum algorithms for the sensor placement problem

**Introduction**

The sensor placement problem, i.e., how many and where to place, in an optimal sense, a limited number of sensors in a network, occurs in several application domains, for instance in telecom networks [1] and in water distribution networks [2] Also in signal reconstruction, a sparse sensor placement problem arises. Here, the greedy method based on matrix QR factorization with column pivoting, see [3], [4], provides a particularly simple and effective sensor optimization method. However, for bigger problems and a high number of sensors to be placed, this can be computationally expensive. An alternative method is to use the quadratic programming feature selection (QPFS) approach of [5] and run this on the D-Wave quantum annealer.

**Project**

The goal of this project is to implement both the classical method and the quantum method and compare their performance for a variety of scenarios. Scenarios will include use cases from signal reconstruction, but it is also interesting whether the cases in the telecom and the water domain as well as analysis of synthetic networks can use the same methods.

You will perform this assignment at TNO (Netherlands Organization for Applied Scientific Research), where you will be supervised by an expert from the department of Cybersecurity and Robustness.

**Requirements**

You are in the final stages of your degree in artificial intelligence, computer science, mathematics, electrical engineering or a similar degree and have some background in the field of quantum. You have experience in programming, networks, etc. and an interest in artificial intelligence and optimisation. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

**To apply**

Part of the application is an interview at TNO. However, as a first step you need to contact the supervisor from NAS: Rob Kooij

**References**

[1] Brandon Heller, Rob Sherwood, Nick McKeown, The Controller Placement Problem, ACM SIGCOMM Computer Communication Review 42(4), 2012.

[2] Venkata Reddy Palleti, Shankar Narasimhan, R. Rengaswamy, Ravi Teja, S. Murty Bhallamudi, Sensor network design for contaminant detection and identification in water distribution networks, Computers & Chemical Engineering, Volume 87, 2016, pp. 246-256,

[3] Drmac, Zlatko, and Serkan Gugercin. "A new selection operator for the discrete empirical interpolation method---improved a priori error bound and extensions." SIAM Journal on Scientific Computing 38.2 (2016): A631-A648.

[4] Brunton, Steven L., and J. Nathan Kutz. Data-driven science and engineering: Machine learning, dynamical systems, and control. Cambridge University Press, 2019.

[5] Nguyen, Xuan Vinh, et al. "Effective global approaches for mutual information based feature selection." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014.

## Semantic Networks: Properties, Representations, and Applications

**Introduction**

Large tech companies (Facebook, Google) and telecom providers (KPN, Ziggo) each collect and process tons of textual data on what their users say online. For these companies, the automated analysis of the text is an important tool with applications in internet search, product recommendation, and sentiment analysis. A systematic analysis of unstructured textual data is, however, extremely non-trivial.

Examples of text analysis tasks that have been considered in the literature are analogy finding, sentiment analysis, and sentence completion. Unfortunately, existing algorithms do not perform optimally, and understanding text computationally remains a difficult task.

One way to analyze text is through the lens of a semantic network: Semantic network is, in most general terms, a network of words or phrases that represent nodes that are connected in case the nodes are semantically related (e.g., co-occur in text, are synonyms or antonyms, etc.). Semantic networks represent knowledge or ‘meaning’—and they could allow computers to reason like a real person would [1]. In general, semantic networks are expected to play an important role in creating better artificially intelligent systems.

**Track 1: Topological Properties of Semantic Networks**

While semantic networks are extensively used in text analysis, their general topological properties are poorly studied. Early results demonstrate that semantic networks are sparse (number of links is comparable to the number of nodes), heterogeneous (number of links per node varies widely), and possess hierarchical structure [2]. A better understanding of the structural properties of semantic networks will be instrumental in understanding their design principles and will ultimately lead to better performance of text analysis algorithms.

__Goal:__ In this project, you will learn to extract semantic network data from various sources and/or build semantic networks from raw text. You will catalog the extracted networks and analyze their structural properties.

__Prerequisites:__ The successful student is expected to have good programming and data extraction skills, background in *Probability and Statistics*, and *Network Science* (EE4C06, CS4195 (or analogous) courses are recommended).

**Track 2: Network Embedding Techniques**

Text analysis can be performed through network embedding: network nodes can be mapped to points in certain (latent) space, such that related words are mapped close to each other in the space.

Then, new relations between words can be uncovered through the identification of unconnected word pairs in the geometric vicinity of one another. Another network embedding application is analogy solving, e.g., find X, such that X to Germany is like Paris to France. A basic geometric idea to the analogy problem is to find a vector connecting France to Paris in the latent space, then draw the same vector from Germany. The resulting vector should point to X, which is expected to be Berlin.

Although in the past decade, researchers developed a lot of network embedding techniques, most of these techniques are ad-hoc and are not systematically evaluated.

__Goal:__ In this project, you will study existing network embedding techniques for semantic data analysis, learn basic principles of statistical inference, and conduct a systematic comparative analysis of the performance of network embedding techniques on different semantic tasks.

__Prerequisites:__ The successful student is expected to have strong programming skills, have a background in *Probability and Statistics* and *Network Science* (EE4C06, CS4195, or analogous courses are recommended).

**To apply: **

Contact Maksim Kitsak,

**References**

*[1] Sowa, J. F. (1987). Semantic networks. Wiley.*

*[2] Van Mieghem, P. (2014). Performance analysis of complex networks and systems. Cambridge University Press.*

## Attacks against AI/ML-based systems in communication networks

**Introduction**

AI/ML-based systems are considered to be an important component of the (future) fixed and mobile networks [1], [2]. They can help in detecting previously unknown security threats or make decisions about resource allocation, thus being a powerful tool in the network security, management and orchestration. At the same time, AI/ML-based systems can become a target on their own, potentially giving an attacker an enormous advantage due to a large span of control these systems are expected to have. Examples of attacks include inferring the model of the AI/ML system and fooling the threat detection system so that malicious traffic is classified as benign [3]; sending a series of (legitimate) requests to the resource management system resulting in resources exhaustion, and ultimately to a denial of service situation; poisoning the data used in the training of the AI/ML system so to be able to influence its behavior [4].

**Project**

The goal of this assignment is to investigate the weak spots of the AI/ML-based systems used in communication networks and possible mitigation means.

You will perform this assignment by TNO, as part of the cross-domain AINET/PROTECT project. The team is composed of researchers from the departments of Cybersecurity and Robustness, Networks as well as Data Science.

**Requirements**

You are in the final stages of your degree in artificial intelligence, computer science, mathematics, electrical engineering or a similar degree and have some track record in the field of cybersecurity. You have experience in programming, networks, Linux system etc. and an interest in machine learning and artificial intelligence. You are pragmatic and focused on making things work. Next to technical expertise we value high level of independence and ability to work remotely, communication skills and a results-driven attitude.

**How to apply**

Applications have to be submitted via TNO webpage, by attaching curriculum vitae and studiorum, and a motivational letter.

**References**

[2] F. Hussain, R. Hussain, S. A. Hassan and E. Hossain, "Machine Learning in IoT Security: Current Solutions and Future Challenges," in *IEEE Communications Surveys & Tutorials*, vol. 22, no. 3, pp. 1686-1721, thirdquarter 2020, doi: 10.1109/COMST.2020.2986444.

[3] Sagduyu, Yalin E., Yi Shi, and Tugba Erpek. "IoT network security from the perspective of adversarial deep learning." *2019 16th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)*. IEEE, 2019.

[4] N. Baracaldo, B. Chen, H. Ludwig, A. Safavi and R. Zhang, "Detecting Poisoning Attacks on Machine Learning in IoT Environments," *2018 IEEE International Congress on Internet of Things (ICIOT)*, San Francisco, CA, 2018, pp. 57-64, doi: 10.1109/ICIOT.2018.00015.

## Hyperbolic Representations of Complex Networks

**Introduction**

Network embeddings or mappings of networks to latent geometric spaces are standard tools in the arsenal of data analysis, and are routinely used in machine learning, visualization, network science, and graph theory. In general, a procedure of network embedding is mapping network nodes to points in a suitable latent metric space, such that latent distances between connected node pairs are smaller than those between disconnected node pairs. Latent-geometric distances are often interpreted as generalized measures of similarities: similar nodes are separated by small distances.

Applications of network embeddings include prediction of missing links, network reconstruction, as well as the analysis of dynamic processes taking place on the network, e.g., routing or epidemic spreading.

Real networks are often characterized by heterogeneous distributions of number of connections per node (node degrees): while the majority of nodes in a network have only a handful connections to other nodes, there are also hubs, nodes with abnormally large number of connections to other nodes [1]. Networks with heterogeneous degree distributions are often referred to as scale-free.

It has recently been shown that scale-free networks are best represented/embedded in spaces with non-Euclidean geometries. One example, to this end, is embedding of networks into spaces with constant negative curvature, i.e., hyperbolic spaces [2, 3].

The biggest challenge/bottleneck of hyperbolic network representations is the lack of scalable embedding methods. Existing hyperbolic embedding methods are either (i) accurate and non-scalable or (ii) scalable but not very accurate.

**Project**

Rely on latest developments in Statistical Inference and Machine Learning to develop a SCALABLE and ACCURATE hyperbolic embedding algorithm.

**Deliverables**

Hyperbolic embedding algorithm.

**To apply: **

Contact Maksim Kitsak,

**Requirements**

This is a very challenging project at the intersection of Machine Learning, Network Science and non-Euclidean geometry. Successful student will have excellent coding skills, prior experience with Euclidean network embeddings, and some knowledge of Statistical Machine Learning.

**References**

*[1] A-L. Barabási, Network Science, Cambridge University Press, (2016).**[2] D. Krioukov, F. Papadopoulos, M. Kitsak, A. Vahdat, and M. Boguná, Hyperbolic Geometry of Complex Networks, Physical Review E, 82 3 (2010).**[3] M. Boguná, F. Papadopoulos, D. Krioukov, Sustaining the internet with hyperbolic mapping, Nature communications, 1 1 (2010).*

# Available Internship Projects

## xApps for Open RAN systems

**Project Description**

The Radio Access Networks (RAN) architecture for cellular communication networks is moving towards open and modular concepts, for example the O-RAN architecture as in Figure 1. The O-RAN architecture defines clearly separated modules for the base station e.g. the Central Unit (CU), Distributed Unit (DU) and Radio Unit (RU) as well as modules responsible for the controlling of the base station resources and functions such as Near-Real Time RAN Intelligent Controler (RIC) and Non-Real Time RIC. All the modules from the O-RAN architecture are communicating via either 3GPP defined interfaces (e.g. E1, F1, X2, Xn, NG) or O-RAN specific interfaces. The ‘openness’ principle is that the O-RAN components are decoupled from the underlying hardware (i.e. virtualized and running in the cloud) and that modules from different vendors can be easily combined and deployed.

O-RAN is increasingly gaining momentum in the industry and it is surely a technology development with high potential. TNO as an independent research laboratory is first to address this technology development in The Netherlands. TNO has O-RAN based laboratory kit embedded in its 5G test/trial facilities from the vendor Accelleran, that includes the Near-Real Time RIC, see dRAX | Accelleran. Conceptually, the Near-Real Time RIC (dRAX) is hosting xApps that are responsible for dynamic configuration and optimisation of base station resources/functions e.g. related to (massive) MIMO beamforming, network slicing, differentiated scheduling and/or mobility management, etc. The project goal is to learn, document and build an example xAPP with the use of TNO’s O-RAN lab facilities.

The intern will have the opportunity to work with the O-RAN facilities at TNO and gain knowledge of this new architectural concept for the base station. More concretely, the project contains the following tasks:

- Learn from the available Accelleran documentation/tutorials about the working of xApps and how an xApp should be build. As a result a short summary report should be written (i.e. a kind of instruction manual).
- Setting up a development environment for the xApps build up on the Accelleran product.
- Search for available open source xApps (e.g. Rimedo Labs to Integrate and Open Source TS xApp with ONF's SD-RAN), select one example xApp and implement it in TNO’s O-RAN lab facility. If time allows, small improvements/adaptations can be also implemented and tested.
- Define and recommend improvements/adaptations to the selected xApp considering also AI/ML concepts. These recommendations are also input for the follow-up MSc thesis project with duration of nine months.

The final result of the internship work is:

- Written short manual about how to build-up an xApp with the O-RAN lab-kit at TNO.
- Working version of an example xApp.

**To apply**

Part of the application is an interview at TNO. However, as a first step, you need to contact the supervisor: Remco Litjens (

## Development & tuning of indoor propagation model for 3.5 GHz band

**Field of study**: Electrical engineering/ Telecommunications

**Background**:

KPN is currently rolling out the 5G network to achieve national coverage. For this KPN uses 700 MHz spectrum to offer 5G services to its customers.

For specific indoor solutions, KPN currently uses 2100 MHz spectrum for offering 5G services. As this spectrum is dynamically shared with 4G, there is limited capacity and bandwidth available for 5G to achieve higher data rates.

With the availability of the new 3.5 GHz spectrum this year, which offers large capacity and high communication speeds, opportunities arise for KPN to enable new 5G services at indoor locations. Not only in office environments, but also at production locations, such as the operation of automated guided vehicles (AGVs) for smart production logistics.

Ensuring good 5G indoor radio coverage and capacity for indoor users will therefore be an important topic for KPN in the near future.

**Activities**:

To be able to dimension indoor networks more accurately, a good propagation model is indispensable. With an accurate prediction model, KPN is able to properly estimate in advance how many indoor antennas are needed to meet a certain coverage requirement.

KPN indoor radio planners use the iBWave simulation tool to design such indoor installations. Whether this simulation tool is accurate enough for the planning of indoor installations at 3.5 GHz still needs to be verified in practice.

The assignment concerns the following activities to be carried out:

- Literature study of what is currently available for indoor propagation models for the 3.5 GHz band.
- Based on the results of literature research, develop the theoretical propagation model for 3.5 GHz band on which indoor installations can be accurately RF planned and calculated.
- Build a test set-up in KPN's RF lab for pathloss measurements for 3.5 GHz band (KPN has measurement equipment such as 3.5 GHz transmitter, Scanner, etc..).
- Test the theoretically developed propagation model in practice by means of performing measurements in both test and live environment.
- Compare the results obtained with the developed propagation model with simulation results from the iBWave tool.
- Document the research results in an easily readable report.

# MSc Thesis Topics Currently Being Researched

## Controller placement with optimal availability

**Introduction **

The facility location problem [1] is a well-known optimization problem and it appears in many contexts, such as minimizing firefighting response times, locating warehouses near factories, and optimizing the locations of content distribution nodes and proxy servers. We will look at the problem in terms of Software-Defined Networks (SDNs), which move the control logic off packet processing devices and onto external controllers. The resulting optimization problem, looks for the best placement of *k* controllers, such that a certain performance metric is optimized.

This problem has been studied by Heller at al. [2]. In particular they tackled the problem for two performance metrics, namely average delay and worst-case delay. These delay metrics are computed over all nodes in the network that do not have a controller in place. By using exhaustive search the impact controller placement on delay was determined for over 200 real-world telecom networks, that are available at the so-called Internet Topology Zoo [3].

**Project **

The aim of this project is to study optimal controller placement with respect to the performance metric *availability*. To this end, we will consider a (finite, undirected) graph *G* where nodes are always operational and edges are independently operational with probability *p* ∈ [0, 1]. For every sensor placement we want to compute, for every node, the probability that it can reach a controller. Although it is known that in general determining such probabilities is a NP-hard problem [4], methods based upon Binary Decision Diagrams or path-width are available, which can determine network availability for networks of moderate sizes [5]. Similar as for the delay metric, we will consider both the average and minimum availability over all nodes in the network that do not have a controller in place.

The supervisor of the project will be prof. dr. Robert Kooij. Duration of the project is nine months.

**Requirements **

The successful student is expected to have strong programming skills, and have some background in Network Science. The courses EE4C06 (Networking) and IN4341(Performance Analysis) are recommended.

**References**

[1] https://en.wikipedia.org/wiki/Facility_location_problem

[2] Brandon Heller, Rob Sherwood, Nick McKeown, The Controller Placement Problem, ACM SIGCOMM Computer Communication Review 42(4), 2012.

[3] http://www.topology-zoo.org/

[4] C.J. Colbourn, The combinatorics of network reliability, New York: Oxford University Press, 1987.

[5] Willem Pino, Teresa Gomes, and Robert Kooij, A Comparison between Two All-Terminal Reliability Algorithms, Journal of Advances in Computer Networks, vol. 3, no. 3, pp. 284-290, 2015.

## System identification: theory and applications

**Introduction**

Complex networks, in general, have two properties:

- The underlying topology, defined by a graph
- The processes/functions that happen in the network, determined by the dynamic equation

To analyse the dynamics of the entire network, both underlying topology and individual systems processes should be merged.

**Challenges**

- Given the measurements of input and output from a (partly) unknown system, to find the systems equations with the best descriptive/predictive power
- Extraction of the underlying topology
- How to steer the dynamics on a network towards some “desired” regime/state
- To apply this methodology to a real-world case and possibly extend/specialise the methods

**Reference**

*[1] L. Xiang, F. Chen, W. Ren and G. Chen, "Advances in Network Controllability," in IEEE Circuits and Systems Magazine, vol. 19, no. 2, pp. 8-32, Secondquarter 2019.*

## Effective Resistance versus Weight of Shortest Path

**Introduction**

In graph theory, two of the many metrics that are used to qualify a graph *G* are (i) Effective resistance [1,2,3] and (ii) weight of the Shortest path. An effective resistance *w** _{ij}* can be defined between each node pair (

*i*,

*j*) with

*i*,

*j*∈

*N*, where

*N*is the number of nodes in the graph. Likewise, the weight

*s*of the Shortest path can be defined between each node pair (

_{ij}*i*,

*j*), assuming that graph

*G*is connected. The set of

*w*

*for all (*

_{ij}*i*,

*j*) can be reflected in an Effective resistance matrix

*Ω*, with elements

*w*

*. Similarly, the set of all weights*

_{ij}*s*of the Shortest path can be reflected in a shortest path matrix

_{ij}*S*.

Shortest paths are used for *path based communication*, such as in data networks and communication networks, whereby information (data) will flow through the shortest path between two points in the network. Effective resistance, on the other hand, is used for *flow based communication*, such as in electrical circuits, whereby current will flow through all possible paths, in accordance with the resistance of the respective paths.

Both the effective resistance matrix *Ω* and the shortest path matrix *S* provide a description of the graph *G*. The distribution of the elements in *Ω* and the distribution of the elements in *S* provide insight in how effective transport in flow and path networks is.

**Challenges**

A research question is formed by the relation between *Ω* and *S* for a graph *G*. In other words, how are the effective resistance values, for all pairs of nodes, correlated with the corresponding shortest paths. We define hereto the difference matrix *C* = *S* – *Ω*. Each element *c _{ij}* indicates how much the shortest path and the effective resistance differ for node pair (

*i*,

*j*).

The challenge for this MSc thesis project is to quantify, visualize and explain the relation between *S* and *Ω* for different classes of graph.

**Approach**

The suggested approach is as follows:

- Graph simulation: generate graphs of various class, and determine
*Ω*,*S*and*C*for each generated graph. Study the corresponding probability distribution of their elements. - Analysis (I): apply general analysis of the
*C*matrix in relation to the graph class, e.g. using probability distribution of*c*._{ij} - Analysis (II): derive a possible cause of an unexpected high
*c*value or an unexpected low_{ij}*c*value for a node pair (_{ij}*i*,*j*). - Graph qualification: define a suitable qualification of a graph through the
*C*matrix; for example, the*C*matrix is a non-negative matrix and its spectrum may contain interesting information. - The effective graph resistance
*R*captures the entire_{G}*Ω*matrix in a single scalar value. Propose a similar metric*S*for the_{G}*S*matrix*.*

**References**

[1] D.J. Klein, M. Randic, Resistance distance, J. Math. Chem. 12 (1993) 81–95.

[2] D.J. Klein, Resistance-distance sum rules, Croatica Chemica Acta 73(2) (2002).

[3] P. Van Mieghem, K. Devriendt and H. Cetinay, 2017, "Pseudoinverse of the Laplacian and best spreader node in a network", Physical Review E, vol. 96, No. 3, p 032311.