Abstract The high increase in traffic and data rate for future generations of mobile communication systems, with simultaneous requirement for reduced power consumption, makes Multihop Cellular Networks (MCNs) an attractive technology. To exploit the potentials of MCNs a number of new network paradigms are proposed in this thesis. First, a new algorithm for efficient relaying topology control is presented to jointly optimize the relaying topology, routing and scheduling resulting in a two dimensional or space time routing protocol. The algorithm is aware of intercell interference (ICI), and requires coordinated action between the cells to jointly choose the relaying topology and scheduling to minimize the system performance degradation due to ICI. This framework is extended to include the optimization of power control. Both conventional and cooperative relaying schemes are considered. In addition, a novel sequential genetic algorithm (SGA) is proposed as a heuristic approximation to reconfigure the optimum relaying topology as the network traffic changes. Network coding is used to combine the uplink and downlink transmissions, and incorporate it into the optimum bidirectional relaying with ICI awareness. Seeking for a more tractable network model to effectively use context awareness and relying on the latest results on network information theory, we apply a hexagonal tessellation for inner partition of the cell into smaller subcells of radius r. By using only one single topology control parameter (r), we jointly optimize routing, scheduling and power control to obtain the optimum trade-off between throughput, delay and power consumption in multicast MCNs. This model enables high resolution optimization and motivates the further study of network protocols for MCNs. A new concept for route discovery protocols is developed and the trade-off between cooperative diversity and spatial reuse is analyzed by using this model. Finally, a new architecture for MCN is considered where multihop transmissions are performed by a Delay Tolerant Network, and new solutions to enhance the performance of multicast applications for multimedia content delivery are presented. Numerical results have shown that the algorithms suggested in this thesis provide significant improvement with respect to the existing results, and are expected to have significant impact in the analysis and design of future cellular networks. / Tiivistelmä Tiedonsiirron ja tiedonsiirtonopeuksien suuri kasvu sekä tehonkulutuksen pieneneminen tulevien sukupolvien matkapuhelinjärjestelmissä tekevät monihyppyiset matkapuhelinverkot houkutteleviksi vaihtoehdoiksi. Tässä työssä esitetään uusia tiedonsiirtoverkkojen paradigmoja monihyppyisten matkapuhelinverkkojen hyödyntämiseksi. Työssä esitellään uusi algoritmi tehokkaaseen releointitopologian hallintaan, joka optimoi yhtäaikaisesti topologian, reitityksen sekä lähetyshetkien ajoituksen ja mahdollistaa tila-aika-reititysprotokollan toteutuksen. Esitetty algoritmi huomioi solujen keskinäishäiriön ja vaaditulla solujen välisellä koordinoidulla hallinnalla saadaan yhdessä valittua topologia ja ajoitus, jotka minimoivat solujen keskinäisistä häiriöistä johtuvan suorituskyvyn heikentymisen. Myöhemmin tätä viitekehystä on laajennettu lisäämällä siihen tehonsäädön optimointi. Työssä on tutkittu sekä perinteisiä että kooperatiivisia releointimenetelmiä. Lisäksi työssä esitetään uusi geneettinen algoritmi heuristiseksi approksimaatioksi verkon liikenteen muutoksen vaatimaan releointitopologian uudelleen järjestelyyn. Työssä tarkastellaan lisäksi verkkokoodausta ylä- ja alasuuntaan tapahtuvan tiedonsiirron yhdistämiseksi sisällyttämällä se solujen keskinäishäiriön huomioivaan kahdensuuntaiseen releointiin. Etsittäessä paremmin mukautuvaa ja kontekstitietoisuutta hyödyntävää verkkomallia, joka käyttää hyväkseen viimeisimpiä verkkojen informaatioteoreettisia tuloksia, voidaan verkon solut pilkkoa pienempiin kuusikulmaisiin alisoluihin. Käyttämällä ainoastaan näiden alisolujen sädettä r voidaan puolestaan verkon reititys, ajoitus ja tehon säätö optimoida yhtäaikaisesti saavuttaen paras mahdollinen kompromissi verkon läpäisyn, viiveen ja tehonkulutuksen välillä. Kehitetty malli mahdollistaa korkean resoluution optimoinnin ja motivoi uusien verkkoprotokollien kehitystä monihyppyisissä matkapuhelinverkoissa. Tätä mallia käyttäen esitellään myös uusi konsepti reitinetsintäprotokollille sekä analysoidaan kooperatiivisen diversiteetin ja tila-avaruudessa tapahtuvan uudelleenkäytön välistä kompromissiratkaisua. Lopuksi työssä tarkastellaan monihyppyisen matkapuhelinverkon uutta arkkitehtuuria, jossa monihyppylähetykset suoritetaan viivesietoisella verkolla ja esitetään uusia ratkaisuja multimediasisällön monilähetysten tehokkuuden parantamiseksi. Työssä saadut tulokset osoittavat, että ehdotetut algoritmit parantavat järjestelmien suorituskykyä verrattuna aiemmin tiedossa olleisiin tuloksiin. Työn tuloksilla voidaan olettaa myös olevan suuri vaikutus tulevaisuuden matkapuhelinverkkojen analysointiin ja suunnitteluun.

This diploma thesis describes the work performed in the optimization of the corporate network in the company Alps Electric Czech to expand the amount of information obtained about the state of the network and thus improve the response of the IT department to potencial errors. During the optimization, a monitoring system was also deployed and its connection to the helpdesk and a tailor-made website running on the Flask microframework. With the gradual deployment, adjustments were made based on feedback from the IT department staff. Overall, the network has become more clarified, increasing the efficiency of maintenance and service.

本論文は,様々なネットワーク最適化問題の数学的構造について様々な観点から調べたものである.主たる結果はネットワーク最適化問題の代表例である最大流問題に,関するいくつかの結果と,Min-Plus代数に値をもつ行列の固有値と固有ベクトルに関する特徴づけに関する結果からなっている. / 博士(理学) / Doctor of Philosophy in Science / 同志社大学 / Doshisha University

<p><br></p> <p>Police use of force has become a topic of significant concern, particularly given the disparate impact on communities of color. Research has shown that police officer involved shootings, misconduct and excessive use of force complaints exhibit network effects, where officers are at greater risk of being involved in these incidents when they socialize with officers who have a history of use of force and misconduct. Given that use of force and misconduct behavior appear to be transmissible across police networks, we are attempting to address if police networks can be altered to reduce use of force and misconduct events in a limited scope.</p> <p><br></p> <p>In this work, we analyze a novel dataset from the Indianapolis Metropolitan Police Department on officer field training, subsequent use of force, and the role of network effects from field training officers. We construct a network survival model for analyzing time-to-event of use of force incidents involving new police trainees. The model includes network effects of the diffusion of risk from field training officers (FTOs) to trainees. We then introduce a network rewiring algorithm to maximize the expected time to use of force events upon completion of field training. We study several versions of the algorithm, including constraints that encourage demographic diversity of FTOs. The results show that FTO use of force history is the best predictor of trainee's time to use of force in the survival model and rewiring the network can increase the expected time (in days) of a recruit's first use of force incident by 8%. </p> <p>We then discuss the potential benefits and challenges associated with implementing such an algorithm in practice.</p> <p><br></p>

Made available in DSpace on 2014-12-17T14:55:11Z (GMT). No. of bitstreams: 1 DanielGC_TESE_Capa_pag90.pdf: 3923138 bytes, checksum: b23776867381c62bd332c913640275ac (MD5) Previous issue date: 2013-04-29 / The development of wireless sensor networks for control and monitoring functions has created a vibrant investigation scenario, covering since communication aspects to issues related with energy efficiency. When source sensors are endowed with cameras for visual monitoring, a new scope of challenges is raised, as transmission and monitoring requirements are considerably changed. Particularly, visual sensors collect data following a directional sensing model, altering the meaning of concepts as vicinity and redundancy but allowing the differentiation of source nodes by their sensing relevancies for the application. In such context, we propose the combined use of two differentiation strategies as a novel QoS parameter, exploring the sensing relevancies of source nodes and DWT image coding. This innovative approach supports a new scope of optimizations to improve the performance of visual sensor networks at the cost of a small reduction on the overall monitoring quality of the application. Besides definition of a new concept of relevance and the proposition of mechanisms to support its practical exploitation, we propose five different optimizations in the way images are transmitted in wireless visual sensor networks, aiming at energy saving, transmission with low delay and error recovery. Putting all these together, the proposed innovative differentiation strategies and the related optimizations open a relevant research trend, where the application monitoring requirements are used to guide a more efficient operation of sensor networks / O desenvolvimento de redes de sensores sem fio para fun??es de controle e monitoramento tem criado um pulsante cen?rio de investiga??o, abrangendo desde aspectos da comunica??o em rede at? quest?es como efici?ncia energ?tica. Quando sensores s?o equipados com c?meras para fun??es de monitoramento visual, um novo escopo de desafios ? lan?ado, uma vez que h? uma mudan?a significativa nos requisitos de monitoramento e transmiss?o. Em particular, sensores visuais coletam dados seguindo um modelo direcional de monitoramento, alterando conceitos j? estabelecidos de vizinhan?a e redund?ncia, por?m tornando poss?vel a diferencia??o de sensores pelas suas relev?ncias de monitoramento para a aplica??o. Nesse contexto, propomos que a relev?ncia de monitoramento dos sensores fontes sejam exploradas em conjunto com a codifica??o de imagens por transformada DWT, unindo assim dois diferentes escopos de relev?ncia para a cria??o de novos par?metros de QoS. Essa abordagem inovadora permite uma nova gama de otimiza??es da opera??o da rede, possibilitando aumento de desempenho com pequenas perdas na qualidade global de monitoramento. Al?m da defini??o de um novo conceito de relev?ncia e a proposi??o de mecanismos para suportar sua utiliza??o pr?tica, cinco diferentes otimiza??es da transmiss?o de imagens em redes de sensores visuais sem fio s?o propostas, visando economia de energia, transmiss?o com baixo atraso e recupera??o de erros. Em conjunto, as estrat?gias de diferencia??o e as otimiza??es relacionadas abrem uma importante vertente de pesquisa, onde os requisitos de monitoramento das aplica??es s?o utilizados para guiar uma opera??o mais eficiente da rede

<p>The swift emergence and wide-ranging utilization of machine learning (ML) across various industries, including healthcare, transportation, and robotics, have underscored the escalating need for efficient, scalable, and privacy-preserving solutions. Recognizing this, we present an integrated examination of three novel frameworks, each addressing different aspects of distributed learning and privacy issues: Two Timescale Hybrid Federated Learning (TT-HF), Delay-Aware Federated Learning (DFL), and Differential Privacy Hierarchical Federated Learning (DP-HFL). TT-HF introduces a semi-decentralized architecture that combines device-to-server and device-to-device (D2D) communications. Devices execute multiple stochastic gradient descent iterations on their datasets and sporadically synchronize model parameters via D2D communications. A unique adaptive control algorithm optimizes step size, D2D communication rounds, and global aggregation period to minimize network resource utilization and achieve a sublinear convergence rate. TT-HF outperforms conventional FL approaches in terms of model accuracy, energy consumption, and resilience against outages. DFL focuses on enhancing distributed ML training efficiency by accounting for communication delays between edge and cloud. It also uses multiple stochastic gradient descent iterations and periodically consolidates model parameters via edge servers. The adaptive control algorithm for DFL mitigates energy consumption and edge-to-cloud latency, resulting in faster global model convergence, reduced resource consumption, and robustness against delays. Lastly, DP-HFL is introduced to combat privacy vulnerabilities in FL. Merging the benefits of FL and Hierarchical Differential Privacy (HDP), DP-HFL significantly reduces the need for differential privacy noise while maintaining model performance, exhibiting an optimal privacy-performance trade-off. Theoretical analysis under both convex and nonconvex loss functions confirms DP-HFL’s effectiveness regarding convergence speed, privacy performance trade-off, and potential performance enhancement with appropriate network configuration. In sum, the study thoroughly explores TT-HF, DFL, and DP-HFL, and their unique solutions to distributed learning challenges such as efficiency, latency, and privacy concerns. These advanced FL frameworks have considerable potential to further enable effective, efficient, and secure distributed learning.</p>

Remote Electrical Tilt (RET) is a method for configuring antenna downtilt in base stations to optimize mobile network performance. Reinforcement Learning (RL) is an approach to automating the process by letting an agent learn an optimal control strategy and adapt to the dynamic environment. Applying RL in real world comes with challenges, for the RET problem there are performance requirements and partial observability of the system through exogenous factors inducing noise in observations. This thesis proposes a solution method through modeling the problem by a Partially Observable Markov Decision Process (POMDP). The set of hidden states are modeled as a high- level representation of situations requiring one of the possible actions uptilt, downtilt, no change. From this model, a Bayesian Neural Network (BNN) is trained to predict an observation model, relating observed Key Performance Indicators (KPIs) to the hidden states. The observation model is used for estimating belief state probabilities of each hidden state, from which decision of control action is made through a restrictive threshold policy. Experiments comparing the method to a baseline Deep Q- network (DQN) agent shows the method able to reach the same average performance increase as the baseline while outperforming the baseline in two metrics important for robust and safe control behaviour, the worst- case minimum reward increase and the average reward increase per number of tilt actions. / Fjärrstyrning av Elektrisk Lutning (FEL) är en metod för att reglera lutningen av antenner i basstationer för att optimera presentandan i ett mobilnätverk. Förstärkande Inlärning (FI) används som metod för att automatisera processen genom att låta en agent lära sig en optimal strategi för reglering och anpassa sig till den dynamiska miljön. Att tillämpa FI i ett verkligt scenario innebär utmaningar, för FEL specifikt finns det krav på en viss nivå av prestanda samt endast en delvis observerbarhet av systemet på grund av externa faktorer som orsakar brus i observationerna. I detta arbete föreslås en metod för att hantera detta genom att modellera problemet som en Delvis Observerbar Markovprocess (DOM). De dolda tillstånden modelleras för att representera situationer där var och en av de möjliga aktionerna behövs, det vill säga att luta antennen upp, ner eller inte ändra på lutningen. Utifrån denna modellering så tränas ett Bayesiskt Neuralt Nätverk (BNN) för att estimera en observationsmodel som kopplar observerade nyckeltal till de dolda tillstånden. Denna observationsmodel används för att estimera sannolikheten att vardera dolt tillstånd är det rätta. Utifrån dessa sannolikheter så görs valet av aktion genom ett tröskelvärde på sannolikheterna. Genom experiment som jämför metoden med en standardimplementering av en agent baserad på ett Djupt Qnätverk (DQN) visas att metoden har samma prestation när det kommer till en medelnivå på prestandaökning i nätverket. Metoden överträffar dock standardmetoden i två andra mätvärden som är viktiga ur aspekten säker och robust reglering, minimumvärdet på prestandaökningen samt medelökningen av prestandan per antal up- och nerlutningar som används.

In dieser Arbeit wird ein Algorithmus zur Lösung von endlichdimensionalen Optimierungsproblemen mit stückweise linearer Zielfunktion und stückweise linearen Nebenbedingungen vorgestellt. Dabei wird angenommen, dass die Funktionen in der sogenannten Abs-Linear Form, einer Matrix-Vektor-Darstellung, vorliegen. Mit Hilfe dieser Form lässt sich der Urbildraum in Polyeder zerlegen, so dass die Nichtglattheiten der stückweise linearen Funktionen mit den Kanten der Polyeder zusammenfallen können. Für die Klasse der abs-linearen Funktionen werden sowohl für den unbeschränkten als auch für den beschränkten Fall notwendige und hinreichende Optimalitätsbedingungen bewiesen, die in polynomialer Zeit verifiziert werden können. Für unbeschränkte stückweise lineare Optimierungsprobleme haben Andrea Walther und Andreas Griewank bereits 2019 mit der Active Signature Method (ASM) einen Lösungsalgorithmus vorgestellt. Aufbauend auf dieser Methode und in Kombination mit der Idee der aktiven Mengen Strategie zur Behandlung von Ungleichungsnebenbedingungen entsteht ein neuer Algorithmus mit dem Namen Constrained Active Signature Method (CASM) für beschränkte Probleme. Beide Algorithmen nutzen die stückweise lineare Struktur der Funktionen explizit aus, indem sie die Abs-Linear Form verwenden. Teil der Analyse der Algorithmen ist der Nachweis der endlichen Konvergenz zu lokalen Minima der jeweiligen Probleme sowie die Betrachtung effizienter Berechnung von Lösungen der in jeder Iteration der Algorithmen auftretenden Sattelpunktsysteme. Die numerische Performanz von CASM wird anhand verschiedener Beispiele demonstriert. Dazu gehören akademische Probleme, einschließlich bi-level und lineare Komplementaritätsprobleme, sowie Anwendungsprobleme aus der Gasnetzwerkoptimierung und dem Einzelhandel. / This thesis presents an algorithm for solving finite-dimensional optimization problems with a piecewise linear objective function and piecewise linear constraints. For this purpose, it is assumed that the functions are in the so-called Abs-Linear Form, a matrix-vector representation. Using this form, the domain space can be decomposed into polyhedra, so that the nonsmoothness of the piecewise linear functions can coincide with the edges of the polyhedra. For the class of abs-linear functions, necessary and sufficient optimality conditions that can be verified in polynomial time are given for both the unconstrained and the constrained case. For unconstrained piecewise linear optimization problems, Andrea Walther and Andreas Griewank already presented a solution algorithm called the Active Signature Method (ASM) in 2019. Building on this method and combining it with the idea of the Active Set Method to handle inequality constraints, a new algorithm called the Constrained Active Signature Method (CASM) for constrained problems emerges. Both algorithms explicitly exploit the piecewise linear structure of the functions by using the Abs-Linear Form. Part of the analysis of the algorithms is to show finite convergence to local minima of the respective problems as well as an efficient solution of the saddle point systems occurring in each iteration of the algorithms. The numerical performance of CASM is illustrated by several examples. The test problems cover academic problems, including bi-level and linear complementarity problems, as well as application problems from gas network optimization and inventory problems.