Network and Information Technologies

Thesis proposal


Research group

Industrial IoT(IIoT) and M2M networks

Industrial networks have developed alongside the traditional Internet. This is because an industrial network (an “operational technology”– OT) has requirements very different from the Internet (an “information technology” – IT). While the Internet is built to interconnect billions of heterogeneous devices communicating large amounts of data over a long distance, an industrial network is typically deployed within a factory floor, typically connecting hundreds or thousands of devices. And, although the amount of data in typical industrial process applications may not be large, what are critical are reliability (all data is received by its final destination), latency (using guaranteed time bounds, as opposed to best-effort) and battery lifetime. Popular wireless link layers (based on carrier sense multiple access – CSMA) do not meet these expectations; thus, industrial networks have remained traditionally wired.

However, the cost of wires is very high, to the point of being prohibitive in many cases. Industrial settings such as steel mills, oil refineries, chemical industries, power plants, infrastructures and smart cities are adopting wireless communications to implement complex industrial monitoring and management processes. Hundreds or thousands of devices report sensed values such as temperature, pressure, traffic flows or tank fill level to a process monitoring centre which uses that information to control actuators or coordinate production stages. This important shift is driving the emergence of M2M communications or the concept of the industrial Internet of things.

Around this concept this line of research aims to develop technologies and standards to improve industrial and machine-to-machine communications while ensuring the future of those networks is IP enabled and Internet connected.

Dr. Xavi Vilajosana

WINE Research Group

Applying contextual intelligence in IoE networking

Ubiquitous computing together with wireless networking is enabling the Internet of everything, where information systems, people, and a wide variety of objects are becoming seamlessly interconnected. However, bringing objects to the Internet presents challenges regarding data throughput, number of devices, power consumption or read range. Around this concept, this proposal aims to exploit context-aware information to improve “Internet of everything” networking technologies such as RFID or other low power networks. The goal is to apply contextual intelligence to improve the quality and usability of the above networks in the context of smart cities or industrial scenarios.

Dr. Joan Meli

Dr. Xavi Vilajosana

WINE Research Group

Context-aware applications for ambient intelligence

Smart cities are a new scenario where a variety of data sources open the door to innovative applications, with the final goal of improving the quality of life of citizens, industry competitiveness and government. The Internet of everything is responsible for collecting and transmitting this data at different levels, by using a wide variety of sensors (including peoples' smartphones). We propose to make use of contextual intelligence tools and techniques (like data mining or statistical learning) to process signals (accelerometers, acoustic, etc.) with the overall goal of transforming the above collected data into useful information, enabling new applications or improving existing ones within the ambient intelligence context.

Dr. Joan Meli

Dr. Carlos Monzo

WINE Research Group

Mobility and radio resources management in 5G cellular networks

The main challenge of the future cellular networks (also known as 5G) is to meet the ever-increasing demand for massive connectivity and massive capacity. Although it has been shown that these two objectives must be addressed from a multi-fold approach, the densification of the network and the exploitation of new spectrum bands arise as two possible enablers.

In this context, the research community has clearly drawn attention to the millimetre wave (mmWave) bands, where bandwidths of up to 1 GHz could be allocated to cellular systems. These new spectrum bands are essential to fuel the network capacity, but the propagation impairments at high frequencies, along with the highly directive antenna gains required to counteract them, pose new architectural and radio resource management challenges.

This doctoral thesis aims to design a medium access control (MAC) layer to provide efficient mechanisms in mmWave bands, eg cell discovery, cell association, self-backhauling, etc.

Related references:

[1] Shokri-Ghadikolaei, H. et al. “Millimeter Wave Cellular Networks: A MAC Layer Perspective”,  available at:

[2] Niu. Y, et al. “A Survey of the Millimeter Wave (mmWave) Communications for 5G: Opportunities and Challenges”, Wireless Networks, to appear. Available at:

Dr. Ferran Adelantado WINE Research Group

Wireless network optimization via experience share

Wireless networks are facing an exponential increase in traffic demand along with a massive increase in density of devices and heterogeneity. Given such complexities, traditional optimization frameworks based on analytical characterization of the network behaviour are expected to be intractable or provide answers difficult to understand and implement in practice. In contrast, learning techniques (such as reinforcement learning) can inherently capture the resulting complex interactions among devices and provide optimality by learning from experience. In this thesis, the use of reinforcement learning mechanisms will be studied in order to overcome the limitations of analytical characterization with the goal of achieving efficient and fair spectrum usage in complex wireless systems, such as 5G networks and in the IoT landscape. The main challenges in applying reinforcement learning to wireless networking optimization are: a) to reduce complexity of network abstractions, b) to amortize the cost of exploratory tasks used to obtain new knowledge, and c) to reduce convergence times. This thesis will explore the use of experience share among multiple network devices to address these challenges and make reinforcement learning applicable to wireless network optimization.

Dra. Cristina Cano

Dr. Xavi Vilajosana

WINE Research Group