A suitable environment for the growth of plants is the Greenhouse, that needs to be monitored by a continuous collection of data related to temperature, carbon dioxide concentration, humidity, illumination intensity using sensors, preferably in a wireless sensor network (WSN). Demand initiates various challenges for diversified applications of WSN in the field of IoT (Internet of Things). Network design in IoT based WSN faces challenges like limited energy capacity, hardware resources, and unreliable environment. Issues like cost and complexity can be limited by using sensors that are heterogeneous in nature. Since replacing or recharging of nodes in action is not possible, heterogeneity in terms of energy can overcome crucial issues like energy and lifetime. In this paper, an energy efficient routing process is discussed that considers three different sensor node categories namely normal, intermediate and advanced nodes. Also, the basic cluster head (CH) selection threshold value is modified considering important parameters like initial and residual energy with an optimum number of CHs in the network. When compared with routing algorithms like LEACH (Low Energy Adaptive Clustering Hierarchy) and SEP (Stable Election Protocol), the proposed model performs better for metrics like throughput, network stability and network lifetime for various scenarios.

No / In the future mobile network, satellites will operate alongside cellular networks in order to provide seamless connectivity irrespective of the location of the user. Such a service scenario requires that the next generation of mobility management (MM) procedures are able to ensure terminal and user mobility on a global scale. This paper considers how the principles of Mobile-IP can be used to develop MM procedures for a heterogeneous access network, comprizing of satellite and cellular elements, connected to an IP core network.Initially, the system architecture is described. This is followed by a discussion of issues related to MM, where location, address and handover management are considered. A description of the signaling protocols for macro-mobility using Mobile-IP is then presented, emphasizing the need to minimize the change to the existing access network procedures. Finally, the performance of the protocols is analyzed in terms of the additional signaling time required for registration and handover.

Yes

Wireless devices and applications have been an unavoidable part of human lives in the past decade. In the past few years, the global mobile data traffic has grown considerably and is expected to grow even faster in future. Given the fact that the number of wireless nodes has significantly increased, the contention and interference on the license-free industrial, scientific, and medical band has become severer than ever. Cognitive radio nodes were introduced in the past decade to mitigate the issues related to spectrum scarcity. In this dissertation, we focus on the interference and performance analysis of networks coexisting with cognitive radio networks and address the design and analysis of spectrum allocation and routing for cognitive radio networks. Spectrum allocation enables nodes to construct a link on a common channel at the same time so they can start communicating with each other. We introduce a new approach for the modeling and analysis of interference and spectrum allocation schemes for cognitive radio networks with arbitrarily-shaped network regions. First, for the first time in the literature, we propose a simple and efficient approach that can derive the distribution of the distance between an arbitrary interior/exterior reference point and a random point within an arbitrary convex/concave irregular polygon. This tool is essential in analyzing important distance-related performance metrics in wireless communication networks. Second, considering the importance of interference analysis in cognitive radio networks and its important role in designing spectrum allocation schemes, we model and analyze a heterogeneous cellular network consisting of several cognitive femto cells and a coexisting multi-cell network. Besides the cumulative interference, important distance-related performance metrics have been investigated, such as the signal-to-interference ratio and outage probability. Finally, the spectrum allocation and routing problems in cognitive radio networks have been discussed. Considering a wireless cognitive radio network coexisting with a cellular network with irregular polygon-shaped cells, we have used the tools developed in this dissertation and proposed a joint spectrum allocation and routing scheme. / Graduate

In wireless networks, there is an ever-increasing demand for higher system throughputs, along with growing expectation for all users to be available to multimedia and Internet services. This is especially difficult to maintain at the cell-edge. Therefore, a key challenge for future orthogonal frequency division multiple access (OFDMA)-based networks is inter-cell interference coordination (ICIC). With full frequency reuse, small inter-site distances (ISDs), and heterogeneous architectures, coping with co-channel interference (CCI) in such networks has become paramount. Further, the needs for more energy efficient, or “green,” technologies is growing. In this light, Uplink Interference Protection (ULIP), a technique to combat CCI via power reduction, is investigated. By reducing the transmit power on a subset of resource blocks (RBs), the uplink interference to neighbouring cells can be controlled. Utilisation of existing reference signals limits additional signalling. Furthermore, cell-edge performance can be significantly improved through a priority class scheduler, enhancing the throughput fairness of the system. Finally, analytic derivations reveal ULIP guarantees enhanced energy efficiency for all mobile stations (MSs), with the added benefit that overall system throughput gains are also achievable. Following this, a novel scheduler that enhances both network spectral and energy efficiency is proposed. In order to facilitate the application of Pareto optimal power control (POPC) in cellular networks, a simple feasibility condition based on path gains and signal-to-noise-plus- interference ratio (SINR) targets is derived. Power Control Scheduling (PCS) maximises the number of concurrently transmitting MSs and minimises their transmit powers. In addition, cell/link removal is extended to OFDMA operation. Subsequently, an SINR variation technique, Power SINR Scheduling (PSS), is employed in femto-cell networks where full bandwidth users prohibit orthogonal resource allocation. Extensive simulation results show substantial gains in system throughput and energy efficiency over conventional power control schemes. Finally, the evolution of future systems to heterogeneous networks (HetNets), and the consequently enhanced network management difficulties necessitate the need for a distributed and autonomous ICIC approach. Using a fuzzy logic system, locally available information is utilised to allocate time-frequency resources and transmit powers such that requested rates are satisfied. An empirical investigation indicates close-to-optimal system performance at significantly reduced complexity (and signalling). Additionally, base station (BS) reference signals are appropriated to provide autonomous cell association amongst multiple co-located BSs. Detailed analytical signal modelling of the femto-cell and macro/pico-cell layouts reveal high correlation to experimentally gathered statistics. Further, superior performance to benchmarks in terms of system throughput, energy efficiency, availability and fairness indicate enormous potential for future wireless networks.

In this dissertation, power allocation approaches considering path loss, shadowing, and Rayleigh and Nakagami-m fading are proposed. The goal is to improve power consumption, and energy and throughput efficiency based on user target signal to interference plus noise ratio (SINR) requirements and an outage probability threshold. First, using the moment generating function (MGF), the exact outage probability over Rayleigh and Nakagami-m fading channels is derived. Then upper and lower bounds on the outage probability are derived using the Weierstrass, Bernoulli and exponential inequalities. Second, the problem of minimizing the user power subject to outage probability and user target SINR constraints is considered. The corresponding power allocation problems are solved using Perron-Frobenius theory and geometric programming (GP). A GP problem can be transformed into a nonlinear convex optimization problem using variable substitution and then solved globally and efficiently by interior point methods. Then, power allocation problems for throughput maximization and energy efficiency are proposed. As these problems are in a convex fractional programming form, parametric transformation is used to convert the original problems into subtractive optimization problems which can be solved iteratively. Simulation results are presented which show that the proposed approaches are better than existing schemes in terms of power consumption, throughput, energy efficiency and outage probability. Prioritized cell association and power allocation (CAPA) to solve the load balancing issue in heterogeneous networks (HetNets) is also considered in this dissertation. A Hetnet is a group of macrocell base stations (MBSs) underlaid by a diverse set of small cell base stations (SBSs) such as microcells, picocells and femtocells. These networks are considered to be a good solution to enhance network capacity, improve network coverage, and reduce power consumption. However, HetNets are limited by the disparity of power levels in the different tiers. Conventional cell association approaches cause MBS overloading, SBS underutilization, excessive user interference and wasted resources. Satisfying priority user (PU) requirements while maximizing the number of normal users (NUs) has not been considered in existing power allocation algorithms. Two stage CAPA optimization is proposed to address the prioritized cell association and power allocation problem. The first stage is employed by PUs and NUs and the second stage is employed by BSs. First, the product of the channel access likelihood (CAL) and channel gain to interference plus noise ratio (GINR) is considered for PU cell association while network utility is considered for NU cell association. Here, CAL is defined as the reciprocal of the BS load. In CAL and GINR cell association, PUs are associated with the BSs that provide the maximum product of CAL and GINR. This implies that PUs connect to BSs with a low number of users and good channel conditions. NUs are connected to BSs so that the network utility is maximized, and this is achieved using an iterative algorithm. Second, prioritized power allocation is used to reduce power consumption and satisfy as many NUs with their target SINRs as possible while ensuring that PU requirements are satisfied. Performance results are presented which show that the proposed schemes provide fair and efficient solutions which reduce power consumption and have faster convergence than conventional CAPA schemes. / Graduate

There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for resources management in diverse wireless networks. The core operation of the proposed architecture is decision-making for resource allocation and system’s parameters adaptation. Thus, we develop the decision-making mechanism using different artificial intelligence techniques, evaluate the performance achieved and determine the tradeoff of using one technique over the others. The techniques include decision-trees, genetic algorithm, hybrid engine based on decision-trees and case based reasoning, and supervised engine with machine learning contribution to determine the ultimate technique that suits the current environment conditions. All the proposed techniques are evaluated using testbed implementation in different topologies and scenarios. LTE networks have been considered as a potential environment for demonstration of our proposed cognitive based resource allocation techniques as they lack of radio resource management. In addition, we explore the use of enhanced online learning to perform efficient resource allocation in the upcoming 5G networks to maximize energy efficiency and data rate. The considered 5G structures are heterogeneous multi-tier networks with device to device communication and heterogeneous cloud radio access networks. We propose power and resource blocks allocation schemes to maximize energy efficiency and data rate in heterogeneous 5G networks. Moreover, traffic offloading from large cells to small cells in 5G heterogeneous networks is investigated and an online learning based traffic offloading strategy is developed to enhance energy efficiency. Energy efficiency problem in heterogeneous cloud radio access networks is tackled using online learning in centralized and distributed fashions. The proposed online learning comprises improvement features that reduce the algorithms complexities and enhance the performance achieved.

Nos dias atuais há uma quantidade massiva de dados textuais sendo produzida e armazenada diariamente na forma de e-mails, relatórios, artigos e postagens em redes sociais ou blogs. Processar, organizar ou gerenciar essa grande quantidade de dados textuais manualmente exige um grande esforço humano, sendo muitas vezes impossível de ser realizado. Além disso, há conhecimento embutido nos dados textuais, e analisar e extrair conhecimento de forma manual também torna-se inviável devido à grande quantidade de textos. Com isso, técnicas computacionais que requerem pouca intervenção humana e que permitem a organização, gerenciamento e extração de conhecimento de grandes quantidades de textos têm ganhado destaque nos últimos anos e vêm sendo aplicadas tanto na academia quanto em empresas e organizações. Dentre as técnicas, destaca-se a classificação automática de textos, cujo objetivo é atribuir rótulos (identificadores de categorias pré-definidos) à documentos textuais ou porções de texto. Uma forma viável de realizar a classificação automática de textos é por meio de algoritmos de aprendizado de máquina, que são capazes de aprender, generalizar, ou ainda extrair padrões das classes das coleções com base no conteúdo e rótulos de documentos textuais. O aprendizado de máquina para a tarefa de classificação automática pode ser de 3 tipos: (i) indutivo supervisionado, que considera apenas documentos rotulados para induzir um modelo de classificação e classificar novos documentos; (ii) transdutivo semissupervisionado, que classifica documentos não rotulados de uma coleção com base em documentos rotulados; e (iii) indutivo semissupervisionado, que considera documentos rotulados e não rotulados para induzir um modelo de classificação e utiliza esse modelo para classificar novos documentos. Independente do tipo, é necessário que as coleções de documentos textuais estejam representadas em um formato estruturado para os algoritmos de aprendizado de máquina. Normalmente os documentos são representados em um modelo espaço-vetorial, no qual cada documento é representado por um vetor, e cada posição desse vetor corresponde a um termo ou atributo da coleção de documentos. Algoritmos baseados no modelo espaço-vetorial consideram que tanto os documentos quanto os termos ou atributos são independentes, o que pode degradar a qualidade da classificação. Uma alternativa à representação no modelo espaço-vetorial é a representação em redes, que permite modelar relações entre entidades de uma coleção de textos, como documento e termos. Esse tipo de representação permite extrair padrões das classes que dificilmente são extraídos por algoritmos baseados no modelo espaço-vetorial, permitindo assim aumentar a performance de classificação. Além disso, a representação em redes permite representar coleções de textos utilizando diferentes tipos de objetos bem como diferentes tipos de relações, o que permite capturar diferentes características das coleções. Entretanto, observa-se na literatura alguns desafios para que se possam combinar algoritmos de aprendizado de máquina e representações de coleções de textos em redes para realizar efetivamente a classificação automática de textos. Os principais desafios abordados neste projeto de doutorado são (i) o desenvolvimento de representações em redes que possam ser geradas eficientemente e que também permitam realizar um aprendizado de maneira eficiente; (ii) redes que considerem diferentes tipos de objetos e relações; (iii) representações em redes de coleções de textos de diferentes línguas e domínios; e (iv) algoritmos de aprendizado de máquina eficientes e que façam um melhor uso das representações em redes para aumentar a qualidade da classificação automática. Neste projeto de doutorado foram propostos e desenvolvidos métodos para gerar redes que representem coleções de textos, independente de domínio e idioma, considerando diferentes tipos de objetos e relações entre esses objetos. Também foram propostos e desenvolvidos algoritmos de aprendizado de máquina indutivo supervisionado, indutivo semissupervisionado e transdutivo semissupervisionado, uma vez que não foram encontrados na literatura algoritmos para lidar com determinados tipos de relações, além de sanar a deficiência dos algoritmos existentes em relação à performance e/ou tempo de classificação. É apresentado nesta tese (i) uma extensa avaliação empírica demonstrando o benefício do uso das representações em redes para a classificação de textos em relação ao modelo espaço-vetorial, (ii) o impacto da combinação de diferentes tipos de relações em uma única rede e (iii) que os algoritmos propostos baseados em redes são capazes de superar a performance de classificação de algoritmos tradicionais e estado da arte tanto considerando algoritmos de aprendizado supervisionado quanto semissupervisionado. As soluções propostas nesta tese demonstraram ser úteis e aconselháveis para serem utilizadas em diversas aplicações que envolvam classificação de textos de diferentes domínios, diferentes características ou para diferentes quantidades de documentos rotulados. / A massive amount of textual data, such as e-mails, reports, articles and posts in social networks or blogs, has been generated and stored on a daily basis. The manual processing, organization and management of this huge amount of texts require a considerable human effort and sometimes these tasks are impossible to carry out in practice. Besides, the manual extraction of knowledge embedded in textual data is also unfeasible due to the large amount of texts. Thus, computational techniques which require little human intervention and allow the organization, management and knowledge extraction from large amounts of texts have gained attention in the last years and have been applied in academia, companies and organizations. The tasks mentioned above can be carried out through text automatic classification, in which labels (identifiers of predefined categories) are assigned to texts or portions of texts. A viable way to perform text automatic classification is through machine learning algorithms, which are able to learn, generalize or extract patterns from classes of text collections based on the content and labels of the texts. There are three types of machine learning algorithms for automatic classification: (i) inductive supervised, in which only labeled documents are considered to induce a classification model and this model are used to classify new documents; (ii) transductive semi-supervised, in which all known unlabeled documents are classified based on some labeled documents; and (iii) inductive semi-supervised, in which labeled and unlabeled documents are considered to induce a classification model in order to classify new documents. Regardless of the learning algorithm type, the texts of a collection must be represented in a structured format to be interpreted by the algorithms. Usually, the texts are represented in a vector space model, in which each text is represented by a vector and each dimension of the vector corresponds to a term or feature of the text collection. Algorithms based on vector space model consider that texts, terms or features are independent and this assumption can degrade the classification performance. Networks can be used as an alternative to vector space model representations. Networks allow the representations of relations among the entities of a text collection, such as documents and terms. This type of representation allows the extraction patterns which are not extracted by algorithms based on vector-space model. Moreover, text collections can be represented by networks composed of different types of entities and relations, which provide the extraction of different patterns from the texts. However, there are some challenges to be solved in order to allow the combination of machine learning algorithms and network-based representations to perform text automatic classification in an efficient way. The main challenges addressed in this doctoral project are (i) the development of network-based representations efficiently generated which also allows an efficient learning; (ii) the development of networks which represent different types of entities and relations; (iii) the development of networks which can represent texts written in different languages and about different domains; and (iv) the development of efficient learning algorithms which make a better use of the network-based representations and increase the classification performance. In this doctoral project we proposed and developed methods to represent text collections into networks considering different types of entities and relations and also allowing the representation of texts written in any language or from any domain. We also proposed and developed supervised inductive, semi-supervised transductive and semi-supervised inductive learning algorithms to interpret and learn from the proposed network-based representations since there were no algorithms to handle certain types of relations considered in this thesis. Besides, the proposed algorithms also attempt to obtain a higher classification performance and a faster classification than the existing network-based algorithms. In this doctoral thesis we present (i) an extensive empirical evaluation demonstrating the benefits about the use of network-based representations for text classification, (ii) the impact of the combination of different types of relations in a single network and (iii) that the proposed network-based algorithms are able to surpass the classification performance of traditional and state-of-the-art algorithms considering both supervised and semi-supervised learning. The solutions proposed in this doctoral project have proved to be advisable to be used in many applications involving classification of texts from different domains, areas, characteristics or considering different numbers of labeled documents.

Future wireless networks are evolving to become ever more heterogeneous, including small cells such as picocells and femtocells, and direct device-to-device (D2D) communication that bypasses base stations (BSs) altogether to share stored and personalized content. Conventional user association schemes are unsuitable for heterogeneous networks (HetNets), due to the massive disparities in transmit power and capabilities of different BSs. To make the most of the new low-power infrastructure and D2D communication, it is desirable to facilitate and encourage users to be offloaded from the macro BSs. This dissertation characterizes the gain in network performance (e.g., the rate distribution) from offloading users to small cells and the D2D network, and develops efficient user association, resource allocation, and interference management schemes aiming to achieve the performance gain. First, we optimize the load-aware user association in HetNets with single-antenna BSs, which bridges the gap between the optimal solution and a simple small cell biasing approach. We then develop a low-complexity distributed algorithm that converges to a near-optimal solution with a theoretical performance guarantee. Simulation results show that the biasing approach loses surprisingly little with appropriate bias factors, and there is a large rate gain for cell-edge users. This framework is then extended to a joint optimization of user association and resource blanking at the macro BSs – similar to the enhanced intercell interference coordination (eICIC) proposed in the global cellular standards, 3rd Generation Partnership Project (3GPP). Though the joint problem is nominally combinatorial, by allowing users to associate to multiple BSs, the problem becomes convex. We show both theoretically and through simulation that the optimal solution of the relaxed problem still results in a mostly unique association. Simulation shows that resource blanking can further improve the network performance. Next, the above framework with single-antenna transmission is extended to HetNets with BSs equipped with large-antenna arrays and operating in the massive MIMO regime. MIMO techniques enable the option of another interference management: serving users simultaneously by multiple BSs – termed joint transmission (JT). This chapter formulates a unified utility maximization problem to optimize user association with JT and resource blanking, exploring which an efficient dual subgradient based algorithm approaching optimal solutions is developed. Moreover, a simple scheduling scheme is developed to implement near-optimal solutions. We then change direction slightly to develop a flexible and tractable framework for D2D communication in the context of a cellular network. The model is applied to study both shared and orthogonal resource allocation between D2D and cellular networks. Analytical SINR distributions and average rates are derived and applied to maximize the total throughput, under an assumption of interference randomization via time and/or frequency hopping, which can be viewed as an optimized lower bound to other more sophisticated scheduling schemes. Finally, motivated by the benefits of cochannel D2D links, this dissertation investigates interference management for D2D links sharing cellular uplink resources. Showing that the problem of maximizing network throughput while guaranteeing the service of cellular users is non-convex and hence intractable, a distributed approach that is computationally efficient with minimal coordination is proposed instead. The key algorithmic idea is a pricing mechanism, whereby BSs optimize and transmit a signal depending on the interference to D2D links, who then play a best response (i.e., selfishly) to this signal. Numerical results show that our algorithms converge quickly, have low overhead, and achieve a significant throughput gain, while maintaining the quality of cellular links at a predefined service level. / text

H υποστήριξη της τεχνολογίας των femtocells, σε περιβάλλον Long Term Evolution Advanced (LTE-A), δίνει ώθηση στο δυναμικό ερευνητικά πεδίο των ετερογενών δικτύων. Αν και προσφέρουν πολλαπλά πλεονεκτήματα, η συνύπαρξη ετερογενών σταθμών βάσεων δημιουργεί και μια σειρά τεχνικών προκλήσεων, με κυριότερη τη δημιουργία παρεμβολών που έχουν ως αποτέλεσμα την υποβάθμιση των παρεχόμενων υπηρεσιών. Στα πλαίσια της εργασίας αυτής παρουσιάζονται και αναλύονται μέθοδοι διαχείρισης ισχύος και κατανομής συχνοτήτων για την ακύρωση παρεμβολών, και αναπτύσσεται και προτείνεται ένα περιβάλλον προσομοίωσης ετερογενών LTE-A δικτύων. Ο προσομοιωτής επιτρέπει τη δημιουργία δισδιάστατης τοπολογίας με παράταξη femtocells πάνω από macrocell δίκτυο σε γραφικό περιβάλλον, την εξομοίωση των συνακόλουθων φαινομένων παρεμβολών και την πρόβλεψη και σύγκριση της απόδοσης του δικτύου μετά από την εφαρμογή διαφορετικών μοντέλων διαχείρισης ισχύος και σχημάτων κατανομής συχνοτήτων για την εξάλειψη των παρεμβολών. / Femtocells technology support, in Long Term Evolution Advanced (LTE-A) environments, promotes the dynamic research on the field of heterogeneous networks. While offering many advantages, the coexistence of heterogeneous base stations creates a number of technical challenges, the main of which is interference phenomena that lead to the degradation of service. In this work, we present and analyze methods of power management and frequency allocation for interference cancellation, and develop and propose a simulation environment for heterogeneous LTE-A networks. The simulator allows the creation of 2-D topologies with femtocells deployments over macrocell networks, the simulation of the resulting interference phenomena and the prediction and comparison of the network's performance after the application of different models of power management and frequency allocation schemes for interference cancellation.