Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Efficient Routing and Scheduling in Wireless Networks

The temporal and spatial variation in wireless channel conditions, node mobility make it challenging to design protocols for wireless networks. In this thesis, we design efficient routing and scheduling algorithms which adapt to changing network conditions caused by varying link quality or node mobility to improve user-level performance. We design and analyze routing protocols for static, mobile and heterogeneous wireless networks. We analyze the performance of opportunistic and cooperative forwarding in static mesh networks showing that opportunism outperforms cooperation; we identify interference as the main cause for mitigating the potential gains achievable with cooperative forwarding. For mobile networks, we quantitatively analyze the tradeoff between state information collection (sampling frequency and number of bits per sample) and power consumption for a fixed source-to-destination goodput constraint. For heterogeneous networks comprising of both static and mobile nodes, we propose a greedy algorithm (adaptive-flood) which dynamically classifies individual nodes as routers/flooders depending on network conditions and demonstrate that it achieves performance equivalent to, and in some cases significantly better than, that of network-wide routing or flooding alone. Last, we consider an application-level wireless streaming scenario where multiple clients are streaming different videos from a cellular base station. We design a greedy algorithm for efficiently scheduling multiple video streams from a base station to mobile clients so as to minimize the total number of application-playout stalls. We develop models for coarse timescale wireless channel variation to aid network and application-layer protocol design

IoT and Smart Cities: Modelling and Experimentation

Internet of Things (IoT) is a recent paradigm that envisions a near future, in which the objects of everyday life will communicate with one another and with the users, becoming an integral part of the Internet. The application of the IoT paradigm to an urban context is of particular interest, as it responds to the need to adopt ICT solutions in the city management, thus realizing the Smart City concept. Creating IoT and Smart City platforms poses many issues and challenges. Building suitable solutions that guarantee an interoperability of platform nodes and easy access, requires appropriate tools and approaches that allow to timely understand the effectiveness of solutions. This thesis investigates the above mentioned issues through two methodological approaches: mathematical modelling and experimenta- tion. On one hand, a mathematical model for multi-hop networks based on semi- Markov chains is presented, allowing to properly capture the behaviour of each node in the network while accounting for the dependencies among all links. On the other hand, a methodology for spatial downscaling of testbeds is proposed, implemented, and then exploited for experimental performance evaluation of proprietary but also standardised protocol solutions, considering smart lighting and smart building scenarios. The proposed downscaling procedure allows to create an indoor well-accessible testbed, such that experimentation conditions and performance on this testbed closely match the typical operating conditions and performance where the final solutions are expected to be deployed

EUROPEAN CONFERENCE ON QUEUEING THEORY 2016

International audienceThis booklet contains the proceedings of the second European Conference in Queueing Theory (ECQT) that was held from the 18th to the 20th of July 2016 at the engineering school ENSEEIHT, Toulouse, France. ECQT is a biannual event where scientists and technicians in queueing theory and related areas get together to promote research, encourage interaction and exchange ideas. The spirit of the conference is to be a queueing event organized from within Europe, but open to participants from all over the world. The technical program of the 2016 edition consisted of 112 presentations organized in 29 sessions covering all trends in queueing theory, including the development of the theory, methodology advances, computational aspects and applications. Another exciting feature of ECQT2016 was the institution of the Takács Award for outstanding PhD thesis on "Queueing Theory and its Applications"

Stochastic transport in complex environments : applications in cell biology

Living organisms would not be functional without active processes. This general statement is valid down to the cellular level. Transport processes are necessary to create, maintain and support cellular structures. In this thesis, intracellular transport processes, driven by concentration gradients and active matter, as well as the dynamics of migrating cells are studied. Many studies deal with diffusive intracellular transport in the complex environment of neuronal dendrites, however, focusing on a few spines. In this thesis, a model was developed for diffusive transport in a full dendritic tree. A link was established between complex structural changes by diseases and transport characteristics. Furthermore, recent experimental studies of search processes in migration of dendritic cells show a link between speed and persistence. In this thesis, a correlation between them was included in a stochastic model, which lead to increased search efficiency. Finally, this thesis deals with the question of how active, bidirectional transport by molecular motors in axons can be efficient. Generically, traffic jams are expected in confined environments. Limitations of bypassing mechanisms are discussed with a bidirectional non-Markovian exclusion process, developed in this thesis. Experimental findings of cooperative effects and microtubule modifications have been incorporated in a stochastic model, leading to self-organized lane-formation and thus, efficient bidirectional transport.Ohne aktive Prozesse wären lebendige Organismen nicht funktionsfähig. Dies gilt bis herab zur Zellebene. Transportprozesse sind notwendig um zelluläre Strukturen aufzubauen und zu erhalten. In dieser Arbeit werden intrazelluläre Transportprozesse, getrieben von Konzentrationsgradienten und aktiver Materie, sowie die Dynamik in Zellmigration untersucht. Viele Studien beschäftigen sich mit passivem Transport in der komplexen Umgebung von neuronalen Dendriten, vorwiegend jedoch mit einzelnen Dornvortsätzen (spines). In dieser Arbeit wurde ein Modell zu Diffusion in einer vollständigen Dendritenstruktur entwickelt und eine Relation zwischen Krankheitsverläufen und neuronalen Funktionen gefunden. Die Migration von dendritischen Zellen zeigen einen Zusammenhang zwischen ihrer Geschwindigkeit und Persistenz. Dieser wurde in ein stochastisches Modell übernommen welches zeigte, dass die Sucheffizienz der Zellen damit gesteigert werden kann. Außerdem geht es um die Frage wie aktiver, bidirektionaler Transport durch molekulare Motoren in Axonen effizient sein kann. In einem so begrenzten Raum sind Verkehrsstaus zu erwarten. In dieser Arbeit wurden lokale Austauschmechanismen anhand des entwickelten Nicht-Markovschen, bidirektionalen Exklusionsprozess diskutiert. Experimentell entdeckte kooperative Effekte und Mikrotubulimodifikationen wurde in ein stochastisches Modell übernommen, was zu selbstorganisierter Spurbildung und damit zu effizientem bidirektionalem Transport führte

On the queuing delay of time-varying channels in Low Earth Orbit satellite constellations

Low Earth Orbit (LEO) satellite constellations are envisioned as a complementary or integrated part of 5G and future 6G networks for broadband or massive access, given their capabilities of full Earth coverage in inaccessible or very isolated environments. Although the queuing and end-to-end delays of such networks have been analyzed for channels with fixed statistics, currently there is a lack in understanding the effects of more realistic time-varying channels for traffic aggregation across such networks. Therefore, in this work we propose a queuing model for LEO constellation-based networks that captures the inherent variability of realistic satellite channels, where ground-to-satellite/satellite-to-ground links may present extremely poor connection periods due to the Land Mobile Satellite (LMS) channel. We verify the validity of our model with an extensive event-driven simulator framework analysis capturing the characteristics of the considered scenario. We later study the queuing and end-to-end delay distributions under such channels with various link, traffic, packet and background conditions, while observing good match between theory and simulation. Our results show that ground-to-satellite/satellite-to-ground links and background traffic have a much stronger impact over the end-to-end delay in mean and particularly variance, even with moderate queues, than unobstructed inter-satellite connections in outer space on an established path between two ground stations and through the constellation. This might hinder the usability of these networks for services with stringent time requirements.This work was supported in part by the European Union’s Horizon 2020 Research and Innovation Programme under Grant 861111, in part by the Innovation Fund Denmark Project Drones4Energy under Project J.nr.8057-00038A, and in part by the Spanish Government through the Ministerio de Economía y Competitividad, Fondo Europeo de Desarrollo Regional (MINECO-FEDER) by the Project Future Internet Enabled Resilient smart CitiEs (FIERCE) under Grant RTI2018-093475-AI00

Resource Allocation in Relay Networks

Demand for high data rates is increasing rapidly, due to the rapid rise of mobile data traffic volume. In order to meet the demands, the future generation of wireless communication systems has to support higher data rates and quality of service. The inherent unreliable and unpredictable nature of wireless medium provides a challenge for increasing the data rate. Cooperative communications, is a prominent technique to combat the detrimental fading effect in wireless communications. Adding relay nodes to the network, and creating s virtual multiple-input multiple-output (MIMO) antenna array is proven to be an efficient method to mitigate the multipath fading and expand the network coverage. Therefore, cooperative relaying is considered as a fundamental element in the Long Term Evolution (LTE)-Advanced standard. In this thesis, we address the problem of resource allocation in cooperative networks. We provide a detailed review on the resource allocation problem. We look at the joint subcarrier-relay assignment and power allocation. The objective of this optimization problem is to allocate the resources fairly, so even the cell-edge users with weakest communication links receive a fair share of resources. We propose a simple and practical algorithm to find the optimal solution. We assess the performance of the proposed algorithm by providing simulations. Furthermore, we investigate the optimality and complexity of the proposed algorithm. Due to the layered architecture of the wireless networks, to achieve the optimal performance it is necessary that the design of the algorithms be based on the underlying physical and link layers. For a cooperative network with correlated channels, we propose a cross-layer algorithm for relay selection, based on both the physical and link-layer characteristics, in order to maximize the linklayer throughput. The performance of the proposed algorithm is studied in different network models. Furthermore, we investigate the optimum number of relays required for cooperation in order to achieve maximum throughput. Buffering has proven to improve the performance of the cooperative network. In light of this, we study the performance of buffer-aided relay selection. In order to move one step closer to the practical applications, we consider a system with coded transmissions. We study three different coding schemes: convolutional code, Turbo code, and distributed Turbo code (DTC). For each scheme, the performance of the system is simulated and assessed analytically. We derive a closed form expression of the average throughput. Using the analysis results, we investigate the diversity gain of the system in asymptotic conditions. Further, we investigate the average transmission delay for different schemes

Secure communication protocol design for buffer-aided relaying systems

指導教員：姜　暁

Learning in Engineered Multi-agent Systems

Consider the problem of maximizing the total power produced by a wind farm. Due to aerodynamic interactions between wind turbines, each turbine maximizing its individual power--as is the case in present-day wind farms--does not lead to optimal farm-level power capture. Further, there are no good models to capture the said aerodynamic interactions, rendering model based optimization techniques ineffective. Thus, model-free distributed algorithms are needed that help turbines adapt their power production on-line so as to maximize farm-level power capture. Motivated by such problems, the main focus of this dissertation is a distributed model-free optimization problem in the context of multi-agent systems. The set-up comprises of a fixed number of agents, each of which can pick an action and observe the value of its individual utility function. An individual's utility function may depend on the collective action taken by all agents. The exact functional form (or model) of the agent utility functions, however, are unknown; an agent can only measure the numeric value of its utility. The objective of the multi-agent system is to optimize the welfare function (i.e. sum of the individual utility functions). Such a collaborative task requires communications between agents and we allow for the possibility of such inter-agent communications. We also pay attention to the role played by the pattern of such information exchange on certain aspects of performance. We develop two algorithms to solve this problem. The first one, engineered Interactive Trial and Error Learning (eITEL) algorithm, is based on a line of work in the Learning in Games literature and applies when agent actions are drawn from finite sets. While in a model-free setting, we introduce a novel qualitative graph-theoretic framework to encode known directed interactions of the form "which agents' action affect which others' payoff" (interaction graph). We encode explicit inter-agent communications in a directed graph (communication graph) and, under certain conditions, prove convergence of agent joint action (under eITEL) to the welfare optimizing set. The main condition requires that the union of interaction and communication graphs be strongly connected; thus the algorithm combines an implicit form of communication (via interactions through utility functions) with explicit inter-agent communications to achieve the given collaborative goal. This work has kinship with certain evolutionary computation techniques such as Simulated Annealing; the algorithm steps are carefully designed such that it describes an ergodic Markov chain with a stationary distribution that has support over states where agent joint actions optimize the welfare function. The main analysis tool is perturbed Markov chains and results of broader interest regarding these are derived as well. The other algorithm, Collaborative Extremum Seeking (CES), uses techniques from extremum seeking control to solve the problem when agent actions are drawn from the set of real numbers. In this case, under the assumption of existence of a local minimizer for the welfare function and a connected undirected communication graph between agents, a result regarding convergence of joint action to a small neighborhood of a local optimizer of the welfare function is proved. Since extremum seeking control uses a simultaneous gradient estimation-descent scheme, gradient information available in the continuous action space formulation is exploited by the CES algorithm to yield improved convergence speeds. The effectiveness of this algorithm for the wind farm power maximization problem is evaluated via simulations. Lastly, we turn to a different question regarding role of the information exchange pattern on performance of distributed control systems by means of a case study for the vehicle platooning problem. In the vehicle platoon control problem, the objective is to design distributed control laws for individual vehicles in a platoon (or a road-train) that regulate inter-vehicle distances at a specified safe value while the entire platoon follows a leader-vehicle. While most of the literature on the problem deals with some inadequacy in control performance when the information exchange is of the nearest neighbor-type, we consider an arbitrary graph serving as information exchange pattern and derive a relationship between how a certain indicator of control performance is related to the information pattern. Such analysis helps in understanding qualitative features of the `right' information pattern for this problem