Decentralized data fusion and data harvesting framework for heterogeneous dynamic network systems

Diese Dissertation behandelt das Thema der dezentralisieren Sammlung und Fusion von Daten in heterogenen, ressourcenbeschraekten und dynamischen Netzwerkszenarien. Dazu wird ein generisches Framework vorgestellt, dass es erlaubt die Datensammlung, den Datenaustausch und auch die Datenfusion dynamisch zu konfigurieren. Im Zuge dessen wird auch eine Methode zur gerichteten Fusion von Daten auf graphentheoretischer Basis eingefrt, die es erlaubt eine logische Struktur fuer die Fusion von Informationen zu modellieren. Eine Markup-Sprache, die sowohl menschen- als auch maschinenlesbar ist, erlaubt es diese Struktur leicht zu editieren. Im Bereich der Protokolle zum Datenaustausch liegt der Fokus dieser Arbeit auf Energieeffizienz, um auch ressourcenbeschraenkte Geraete einzubinden. Ein weiterer Schwerpunkt liegt auf Robustheit fuer die betrachteten dynamischen Szenarien. Diese Dissertation schlaet zudem Design-Richtlinien vor, um verschiedene Ziele fuer unterschiedliche Applikationen umzusetzen. Diese lassen sich leicht in das vorgestellte Framework integrieren und darueber konfigurieren. Dadurch ergibt sich im Ganzen eine flexible Architektur, die sich leicht an dynamische Umgebungen anpassen laesst.With the increasing number of available smart phones, sensor nodes, and novel mobile smart devices such as Google glass, a large volume of data reflecting the environment is generated in the form of sensing data sources (such as GPS, received signal strength identification, accelerometer, microphone, images, videos and gyroscope, etc.). Some context-aware and data centric applications require the online processing of the data collected. The thesis researches on the decentralized data fusion and data harvesting framework for heterogeneous dynamic network system consisting of various devices with resource constraints. In order to achieve the flexible design, a general architecture is provided while the detailed data fusion and data exchange functions can be dynamically configured. A novel method to use directed fusion graph to model the logical structure of the distributed information fusion architecture is introduced. This directed fusion graph can accurately portray the interconnection among different data fusion components and the data exchange protocols, as well as the detailed data streams. The directed fusion graph is then transformed into a format with marked language, so that both human and machine can easily understand and edit. In the field of data exchange protocols, this thesis targets energy-efficiency considering the resource constraints of the devices and robustness, as the dynamic environment might cause failures to the system. It proposes a refined gossip strategy to reduce retransmission of redundant data. The thesis also suggests a design guideline to achieve different design aims for different applications. These results in this field can be integrated into the framework effortlessly. The configuration mechanism is another feature of this framework. Different from other research work which consider configuration as a post-design work separated from the main design of any middle-ware. This thesis considers the configuration part as another dimension of the framework. The whole strategy in configuration sets up the foundation for the flexible architecture, and makes it easy to adapt to the dynamic environment. The contributions in the above fields lead to a light-weight data fusion and data harvesting framework which can be deployed easily above wireless based, heterogeneous, dynamic network systems, even in extreme conditions, to handle data-centric applications

Cooperative Convex Optimization in Networked Systems: Augmented Lagrangian Algorithms with Directed Gossip Communication

We study distributed optimization in networked systems, where nodes cooperate to find the optimal quantity of common interest, x=x^\star. The objective function of the corresponding optimization problem is the sum of private (known only by a node,) convex, nodes' objectives and each node imposes a private convex constraint on the allowed values of x. We solve this problem for generic connected network topologies with asymmetric random link failures with a novel distributed, decentralized algorithm. We refer to this algorithm as AL-G (augmented Lagrangian gossiping,) and to its variants as AL-MG (augmented Lagrangian multi neighbor gossiping) and AL-BG (augmented Lagrangian broadcast gossiping.) The AL-G algorithm is based on the augmented Lagrangian dual function. Dual variables are updated by the standard method of multipliers, at a slow time scale. To update the primal variables, we propose a novel, Gauss-Seidel type, randomized algorithm, at a fast time scale. AL-G uses unidirectional gossip communication, only between immediate neighbors in the network and is resilient to random link failures. For networks with reliable communication (i.e., no failures,) the simplified, AL-BG (augmented Lagrangian broadcast gossiping) algorithm reduces communication, computation and data storage cost. We prove convergence for all proposed algorithms and demonstrate by simulations the effectiveness on two applications: l_1-regularized logistic regression for classification and cooperative spectrum sensing for cognitive radio networks.Comment: 28 pages, journal; revise

A survey of distributed data aggregation algorithms

Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, which can then be used to direct the execution of other applications. The resulting values are derived by the distributed computation of functions like COUNT, SUM, and AVERAGE. Some application examples deal with the determination of the network size, total storage capacity, average load, majorities and many others. In the last decade, many different approaches have been proposed, with different trade-offs in terms of accuracy, reliability, message and time complexity. Due to the considerable amount and variety of aggregation algorithms, it can be difficult and time consuming to determine which techniques will be more appropriate to use in specific settings, justifying the existence of a survey to aid in this task. This work reviews the state of the art on distributed data aggregation algorithms, providing three main contributions. First, it formally defines the concept of aggregation, characterizing the different types of aggregation functions. Second, it succinctly describes the main aggregation techniques, organizing them in a taxonomy. Finally, it provides some guidelines toward the selection and use of the most relevant techniques, summarizing their principal characteristics.info:eu-repo/semantics/publishedVersio

Energy-aware Gossip Protocol for Wireless Sensor Networks

Dissertação de mestrado em Engenharia InformáticaIn Wireless Sensor Networks (WSNs), typically composed of nodes with resource constraints, leveraging efficient processes is crucial to enhance the network longevity and consequently the sustainability in ultra-dense and heterogeneous environments, such as smart cities. Epidemic algorithms are usually efficient in delivering packets to a sink or to all it’s peers but have poor energy efficiency due to the amount of packet redundancy. Directional algorithms, such as Minimum Cost Forward Algorithm (MCFA) or Directed Diffusion, yield high energy efficiency but fail to handle mobile environments, and have poor network coverage. This work proposes a new epidemic algorithm that uses the current energy state of the network to create a topology that is cyclically updated, fault tolerant, whilst being able to handle the challenges of a static or mobile heterogeneous network. Depending on the application, tuning in the protocol settings can be made to prioritise desired characteristics. The proposed protocol has a small computational footprint and the required memory is proportional not to the size of the network, but to the number of neighbours of a node, enabling high scalability. The proposed protocol was tested, using a ESP8266 as an energy model reference, in a simulated environment with ad-hoc wireless nodes. It was implemented at the application level with UDP sockets, and resulted in a highly energy efficient protocol, capable of leveraging extended network longevity with different static or mobile topologies, with results comparable to a static directional algorithm in delivery efficiency.Em Redes de Sensores sem Fios (RSF), tipicamente compostas por nós com recursos lim-itados, alavancar processos eficientes é crucial para aumentar o tempo de vida da rede e consequentemente a sustentabilidade em ambientes heterogéneos e ultra densos, como cidades inteligentes por exemplo. Algoritmos epidêmicos são geralmente eficientes em en-tregar pacotes para um sink ou para todos os nós da rede, no entanto têm baixa eficiência energética devido a alta taxa de duplicação de pacotes. Algoritmos direcionais, como o MCFA ou de Difusão Direta, rendem alta eficiência energética mas não conseguem lidar com ambientes móveis, e alcançam baixa cobertura da rede. Este trabalho propõe um novo protocolo epidêmico que faz uso do estado energético atual da rede para criar uma topologia que por sua vez atualizada ciclicamente, tolerante a falhas, ao mesmo tempo que é capaz de lidar com os desafios de uma rede heterogênea estática ou móvel. A depender da aplicação, ajustes podem ser feitos às configurações do protocolo para que o mesmo priorize determinadas características. O protocolo proposto tem um pequeno impacto computacional e a memória requerida é proporcional somente à quantidade de vizinhos do nó, não ao tamanho da rede inteira, permitindo assim alta escalabilidade. O algoritmo proposto foi testado fazendo uso do modelo energético de uma ESP8266, em um ambiente simulado com uma rede sem fios ad-hoc. Foi implementado à nível aplicacional com sockets UDP, e resultou em um protocol energeticamente eficiente, capaz de disponibilizar alta longevidade da rede mesmo com diferentes topologias estáticas ou móveis com resultados comparáveis à um protocolo direcional em termos de eficiência na entrega de pacotes

Cognitive-Empowered Femtocells: An Intelligent Paradigm of a Robust and Efficient Media Access

Driven by both the need for ubiquitous wireless services and the stringent strain on radio spectrum faced in today's wireless communications, cognitive radio (CR) have been investigated as a promising solution to deploy Wireless Regional Area Networks (WRANs) for an efficient spectrum utilization. Communication devices with CR capabilities are able to access spectrum bands licensed for other wireless services in an opportunistic and secondary fashion, while preventing harmful interference to incumbent licensed services. However, a lesson learned from early experiences in developing such macro-cellular networks is that it becomes increasingly less economically viable to develop CR macrocellular infrastructures for increasing data rates in both line-of-sight as well as non-line-of-sight situation of WRAN, and the corresponding quality of service (QoS) in macrocellular networks is also noticeably degraded due to path loss, shadowing, and multipath fading due to wall penetration. Moreover, there are several challenges to make the real-world CR enabling dynamic spectrum access a difficult problem to implement without harmful interference. First, the hardware design of cognitive radio on the physical layer involves the tuning over a broad range of spectrum to detect a weak signal in a dynamic environment of fading channels, which in turn makes identification of the spectrum opportunities hard to achieve in an efficient and accurate manner. Second, opportunistic media access based on imperfect spectrum usage information obtain from physical layer brings up undesirable interference issue, as well as reliability issues introduced by mutual interference. Third, the curial issue is to determine which channels to use for data transmissions in presence of the dynamic and opportunistic nature of wireless environments, in the case where pre-defined dedicated control channel is not available in the complex and heterogenous networks. In this dissertation, a novel framework called Cognitive-Empowered Femtocell (CEF), which combines CR techniques with femtocell networking, is introduced to tackle these challenges and achieve better spectrum reuse, lower interference, easy integration, wider network coverage, as well as fast and cost effective early stage WRAN. In this framework, a sensing coordination scheme is proposed to gracefully unshackles the master/slave relationship between central controllers and end users, while maintaining order and coordination such that better sensing precision and efficiency can be achieved. As such, the network intelligence can be expanded from controlling the intelligence paradigm to better understand the satisfy wireless user needs. We also discuss design and deployment aspects such as sensing with reasoning approach, gossip-enabled stochastic media access without a dedicated control channel, all of which are important to the success of the CEF framework. We illustrate that such a framework allows wireless users to intelligently capture spectrum opportunities while mitigating interference to other users, as well as improving the network capacity. Performance analysis and simulations were conducted based on these techniques to provide insight on the future direction of interference suppression for dynamic spectrum access