The power of quasi-shortest paths and the impact of node mobility on dynamic networks

The objective of this thesis is to investigate three important aspects of dynamic networks: the impact of node mobility on multihop data transmission, the effect of the use of longer paths on the relative importance of nodes and the performance of the network in the presence of failure on central nodes. To analyze the first aspect, this work proposes the (κ, λ)-vicinity, which extends the traditional vicinity to consider as neighbors nodes at multihop distance and restricts the link establishment according to the relative speed between nodes. This proposal is used later on the development of three forwarding strategies. The relative speed restriction imposed on these strategies results in significant reduction of resources consumption, without incurring significant impact on the average packet delivery ratio. To analyze the second aspect, we propose the ρ-geodesic betweenness centrality, which uses shortest and quasi-shortest paths to quantify the relative importance of a node. The quasishortest paths are limited by a spreadness factor, ρ. The use of non-optimal paths causes the reranking of several nodes and its main effect is a reduced occupation of the most central positions by articulation points. Lastly, the network performenace in presence of failures is investigated through simulations, in which failures happen on nodes defined as the most central according to distinct centrality metrics. The result is a severe reduction of the average network throughput, and it is independent of the metric used to determine which nodes are the most central. The major strength of the proposed metric, then, is that, despite the severe reduction of the throughput, there is a high probability of maintaining the network connected after a failure, because it is unlikely that a failing node in the most central position is also an articulation point.O objetivo desta tese é investigar três aspectos importantes das redes dinâmicas: o impacto da mobilidade dos nós na transmissão de dados em múltiplos saltos, o efeito do uso de caminhos mais longos na importância relativa dos nós, e o desempenho da rede na presença de falha em nós centrais. Para analisar o primeiro aspecto, este trabalho propõe a (κ, λ)-vizinhança, que estende a vizinhança tradicional para considerar como vizinhos nós a múltiplos saltos de distância e restringe o estabelecimento de enlaces de acordo com a velocidade relativa entre os nós. Essa proposta é usada posteriormente no desenvolvimento de três estratégias de encaminhamento. A restrição de velocidade relativa imposta nessas estratégias resulta em uma redução significativa do consumo de recursos, sem que ocorra impacto significativo na taxa média de entrega de pacotes. Para analisar o segundo aspecto, propõe a centralidade de intermediação ρ-geodésica, que usa caminhos mais curtos e quase mais curtos para quantificar a importância relativa dos nos. Os caminhos quase mais curtos são limitados por um fator de espalhamento ρ. O uso de caminhos não ótimos provoca o reranqueamento de diversos nós e tem como principal efeito uma menor ocupação de posições mais centrais por pontos de articulação. Por fim, o desempenho da rede em presença de falha é investigado através de simulações nas quais as falhas atingem nós definidos como os mais centrais de acordo com métricas de centralidade distintas. O resultado é uma redução brusca da vazão média da rede, independentemente da métrica usada para determinar quais são os nós mais centrais. O grande trunfo da métrica proposta é que, apesar da severa redução na vazão, é grande a probabilidade de manter a rede conectada após a falha, uma vez que é pouco provável que um nó em falha nas posições mais centrais seja também um ponto de articulação

Selbstorganisierende Strukturen und Metriken komplexer Netzwerke

In nature, society and technology many disordered systems exist, that show emergent behaviour, where the interactions of numerous microscopic agents result in macroscopic, systemic properties, that may not be present on the microscopic scale. Examples include phase transitions in magnetism and percolation, for example in porous unordered media, biological, and social systems. Also technological systems that are explicitly designed to function without central control instances, like their prime example the Internet, or virtual networks, like the World Wide Web, which is defined by the hyperlinks from one web page to another, exhibit emergent properties. The study of the common network characteristics found in previously seemingly unrelated fields of science and the urge to explain their emergence, form a scientific field in its own right, the science of complex networks. In this field, methodologies from physics, leading to simplification and generalization by abstraction, help to shift the focus from the implementation's details on the microscopic level to the macroscopic, coarse grained system level. By describing the macroscopic properties that emerge from microscopic interactions, statistical physics, in particular stochastic and computational methods, has proven to be a valuable tool in the investigation of such systems. The mathematical framework for the description of networks is graph theory, in hindsight founded by Euler in 1736 and an active area of research since then. In recent years, applied graph theory flourished through the advent of large scale data sets, made accessible by the use of computers. A paradigm for microscopic interactions among entities that locally optimize their behaviour to increase their own benefit is game theory, the mathematical framework of decision finding. With first applications in economics e.g. Neumann (1944), game theory is an approved field of mathematics. However, game theoretic behaviour is also found in natural systems, e.g. populations of the bacterium Escherichia coli, as described by Kerr (2002). In the present work, a combination of graph theory and game theory is used to model the interactions of selfish agents that form networks. Following brief introductions to graph theory and game theory, the present work approaches the interplay of local self-organizing rules with network properties and topology from three perspectives. To investigate the dynamics of topology reshaping, coupling of the so called iterated prisoners' dilemma (IPD) to the network structure is proposed and studied in Chapter 4. In dependence of a free parameter in the payoff matrix, the reorganization dynamics result in various emergent network structures. The resulting topologies exhibit an increase in performance, measured by a variance of closeness, of a factor 1.2 to 1.9, depending in the chosen free parameter. Presented in Chapter 5, the second approach puts the focus on a static network structure and studies the cooperativity of the system, measured by the fixation probability. Heterogeneous strategies to distribute incentives for cooperation among the players are proposed. These strategies allow to enhance the cooperative behaviour, while requiring fewer total investments. Putting the emphasis on communication networks in Chapters 6 and 7, the third approach investigates the use of routing metrics to increase the performance of data packet transport networks. Algorithms for the iterative determination of such metrics are demonstrated and investigated. The most successful of these algorithms, the hybrid metric, is able to increase the throughput capacity of a network by a factor of 7. During the investigation of the iterative weight assignments a simple, static weight assignment, the so called logKiKj metric, is found. In contrast to the algorithmic metrics, it results in vanishing computational costs, yet it is able to increase the performance by a factor of 5.In Natur, Gesellschaft und Technik existiert eine Vielzahl ungeordneter Systeme, für die die Emergenz makroskopischer Eigenschaften aus mikroskopischen Wechselwirkungen charakteristisch sind. Beispiele für emergente Eigenschaften in physikalischen Systemen sind Phasenübergänge, wie sie etwa in der Perkolation auftreten. Weitere bedeutende Beispiele sind komplexe technologische Systeme, insbesondere solche, bei deren Entwicklung eine hohe Ausfallsicherheit ohne zentrale Kontrollinstanz eine wichtige Rolle spielt. Ein archetypisches Beispiel eines komplexen, selbstorganisierten Systems, gesteuert durch eigennützig handelnde Einheiten, sind Kommunikationsnetzwerke, insbesondere das Internet. Motiviert durch die immense Bedeutung solcher Kommunikationsnetze für die heutige moderne Gesellschaft untersucht die vorliegende Arbeit Wege zur Optimierung dieser Netze. Hier helfen Methoden der Physik, wie Generalisierung und Reduktion auf grundlegende Eigenschaften, die Aufmerksamkeit von implementationsspezifischen Details der mikroskopischen Dynamik auf makroskopische Folgen zu lenken. Insbesondere die Konzepte und Methoden der Statistischen Physik erweisen sich im Umgang mit komplexen Netzwerken als nützlich. Der mathematische Rahmen zur Beschreibung von Netzwerken ist die Graphentheorie, in deren Formalismus vernetzte Strukturen als Menge von Knoten dargestellt werden, welche durch Kanten miteinander verbunden sind. Ein mathematischer Formalismus zur Beschreibung von eigenständig handelnden Entitäten und deren Wechselwirkung ist die Spieltheorie. Diese beschreibt das Verhalten und die Entscheidungsfindung von Agenten bzw. Spielern, die eigenständig und eigennützig ihr Verhalten, charakterisiert durch ihre Strategie, optimieren. Die vorliegende Arbeit nutzt die Verknüpfung dieser beiden Formalismen um Interaktionen vernetzter eigenständiger Entitäten zu modellieren, und die daraus resultierenden emergenten Eigenschaften der Netzstruktur zu untersuchen. Das Konzept der Reorganisation von Netzwerken wird durch Kopplung der Netzstruktur an die Spieldynamik des Iterated Prisoners' Dilemma untersucht. Dies erlaubt eine Beeinflussung der Reorganisationsdynamik durch kontinuierliche Änderung der Spielparameter und ermöglicht damit eine Optimierung der Spieldynamik in Bezug auf Eigenschaften der emergenten Netzstruktur. Die vorgestellte Art der selbstorganisierenden Netzwerkoptimierung wird exemplarisch anhand einer Quantifizierung der Netzwerkperformanz demonstriert. In Abhängigkeit des gewählten Spielparameters wird im Vergleich zu Zufallsgraphen eine Erhöhung der Performanz um den Faktor 1.2 bis 1.9 erreicht. Eine weitere Herangehensweise zur Untersuchung von Spielen auf Netzwerken wird verfolgt, indem die Kooperativität von Prisoners' Dilemma Spielern auf dem Netz, quantifiziert durch die sogenannte Fixation Probability, untersucht wird. Hier werden Strategien zur Verteilung individueller Kooperationsanreize untersucht. Die vorgeschlagenen Strategien resultieren relativ zur globalen homogenen Verteilung derselben Summe der Anreize zu einer um den Faktor 5 erhöhten Kooperativität. An die spieltheoretischen Betrachtungen anschließend liegt das Hauptaugenmerk der folgenden Betrachtungen auf Kommunikationsnetzwerken. Zusätzlich zu durch vorangegangene Arbeiten vorgeschlagenen Metriken werden drei weitere Metriken eingeführt, von denen sich zwei, die Hybrid Metrik und die logKiKj Metrik, als äußerst erfolgreich im Sinne einer Optimierung der Throughput Capacity erweisen, was durch ausführliche numerische Simulationen belegt wird. Die Vorteile der hier eingeführten Metriken liegen im Fall der Hybrid Metrik in der unter den verglichenen Metriken besten resultierenden Performanz für Netze mit mehr als 3000 Knoten, mit einer mittleren Steigerung um den Faktor 7 im Vergleich zur Performanz ohne Metrik. Für Netze mit bis zu 3000 Knoten erreicht die sogenannte Extremal Metrik zwar eine leicht höhere Performanz, sie ist jedoch wegen ihrer sehr hohen numerischen Anforderungen für größere Netze nicht anwendbar. Im Falle der logKiKj Metrik ist die numerische Komplexität vernachlässigbar, dieser Vorteil an vermindertem numerischen Aufwand wird jedoch durch eine leichte Reduktion des Performanzgewinns erkauft, nichtsdestotrotz bewirkt auch diese Metrik eine mittlere Performanzsteigerung um den Faktor 5 und erreicht damit die Größenordnung der Hybrid Metrik

Critical Node Identifcation for accessing network vulnerability, a necessary consideration

Timely identification of critical nodes is crucial for assessing network vulnerability and survivability. This thesis presents two new approaches for the identification of critical nodes in a network with the first being an intuition based approach and the second being build on a mathematical framework. The first approach which is referred to as the Combined Banzhaf & Diversity Index (CBDI) uses a newly devised diversity metric, that uses the variability of a node’s attributes relative to its neighbours and the Banzhaf power index which characterizes the degree of participation of a node in forming the shortest path route. The Banzhaf power index is inspired from the theory of voting games in game theory whereas, the diversity index is inspired from the analysis and understanding of the influence of the average path length of a network on its performance. This thesis also presents a new approach for evaluating this average path length metric of a network with reduced computational complexity and proposes a new mechanism for reducing the average path length of a network for relatively larger network structures. The proposed average path length reduction mechanism is tested for a wireless sensor network and the results compared for multiple existing approaches. It has been observed using simulations that, the proposed average path length reduction mechanism outperforms existing approaches by reducing the average path length to a greater extent and with a simpler hardware requirement. The second approach proposed in this thesis for the identification of critical nodes is build on a mathematical framework and it is based on suboptimal solutions of two optimization problems, namely the algebraic connectivity minimization problem and a min-max network utility problem. The former attempts to address the topological as- pect of node criticality whereas, the latter attempts to address its connection-oriented nature. The suboptimal solution of the algebraic connectivity minimization problem is obtained through spectral partitioning considerations. This approach leads to a distributed solution which is computationally less expensive than other approaches that exist in the literature and is near optimal, in the sense that it is shown through simulations to approximate a lower bound which is obtained analytically. Despite the generality of the proposed approaches, this thesis evaluates their performance on a wireless ad hoc network and demonstrates through extensive simulations that the proposed solutions are able to choose more critical nodes relative to other approaches, as it is observed that when these nodes are removed they lead to the highest degrada- tion in network performance in terms of the achieved network throughput, the average network delay, the average network jitter and the number of dropped packets

Socially-aware congestion control in ad-hoc networks: Current status and the way forward

Ad-hoc social networks (ASNETs) represent a special type of traditional ad-hoc network in whicha user’s social properties (such as the social connections and communications metadata as wellas application data) are leveraged for offering enhanced services in a distributed infrastructurelessenvironments. However, the wireless medium, due to limited bandwidth, can easily suffer from theproblem of congestion when social metadata and application data are exchanged among nodes—a problem that is compounded by the fact that some nodes may act selfishly and not share itsresources. While a number of congestion control schemes have been proposed for the traditional ad-hoc networks, there has been limited focus on incorporating social awareness into congestion controlschemes. We revisit the existing traditional ad-hoc congestion control and data distribution protocolsand motivate the need for embedding social awareness into these protocols to improve performance.We report that although some work is available in opportunistic network that uses socially-awaretechniques to control the congestion issue, this area is largely unexplored and warrants more researchattention. In this regards, we highlight the current research progress and identify multiple futuredirections of research

Socially aware integrated centralized infrastructure and opportunistic networking: a powerful content dissemination catalyst

The classic centralized infrastructure (CI) exhibits low efficiency in disseminating the content of common interest across its requesters. In order to overcome the limitations of CI-based content dissemination, smart mobile devices are capable of activating direct opportunistic communications among mobile users, which returns in integrated cellular and opportunistic networks. During the content dissemination process, the social characteristics of multiple users, including their common interest in the content, their mobility patterns, their social ties, and their altruistic forwarding behaviors, should be carefully considered in order to design an efficient content dissemination scheme. We demonstrate that the integrated network-based content dissemination scheme outperforms its CI-based counterpart in terms of both content delivery ratio and its various energy and delay metrics. Furthermore, the opportunistic network is capable of offloading a large fraction of tele-traffic from the overloaded CI-based network

Socio-economic aware data forwarding in mobile sensing networks and systems

The vision for smart sustainable cities is one whereby urban sensing is core to optimising city operation which in turn improves citizen contentment. Wireless Sensor Networks are envisioned to become pervasive form of data collection and analysis for smart cities but deployment of millions of inter-connected sensors in a city can be cost-prohibitive. Given the ubiquity and ever-increasing capabilities of sensor-rich mobile devices, Wireless Sensor Networks with Mobile Phones (WSN-MP) provide a highly flexible and ready-made wireless infrastructure for future smart cities. In a WSN-MP, mobile phones not only generate the sensing data but also relay the data using cellular communication or short range opportunistic communication. The largest challenge here is the efficient transmission of potentially huge volumes of sensor data over sometimes meagre or faulty communications networks in a cost-effective way. This thesis investigates distributed data forwarding schemes in three types of WSN-MP: WSN with mobile sinks (WSN-MS), WSN with mobile relays (WSN-HR) and Mobile Phone Sensing Systems (MPSS). For these dynamic WSN-MP, realistic models are established and distributed algorithms are developed for efficient network performance including data routing and forwarding, sensing rate control and and pricing. This thesis also considered realistic urban sensing issues such as economic incentivisation and demonstrates how social network and mobility awareness improves data transmission. Through simulations and real testbed experiments, it is shown that proposed algorithms perform better than state-of-the-art schemes.Open Acces

Energy efficient in cluster head and relay node selection for wireless sensor networks

Wireless Sensor Networks (WSNs) are defined as networks of nodes that work in a cooperative way to sense and control the surrounding environment. However, nodes contain limited energy which is the key limiting factor of the sensor network operation. In WSN architecture, the nodes are typically grouped into clusters where one node from each cluster is selected as the Cluster Head (CH) and relays utilisation to minimise energy consumption. Currently, the selection of CH based on a different combination of input variables. Example of these variables includes residual energy, communication cost, node density, mobility, cluster size and many others. Improper selection of sensor node (i.e. weak signal strength) as CH can cause an increase in energy consumption. Additionally, a direct transmission in dual-hop communication between sensor nodes (e.g. CH) with the base station (BS) uses high energy consumption. A proper selection of the relay node can assist in communication while minimising energy consumption. Therefore, the research aim is to prolong the network lifetime (i.e. reduce energy consumption) by improving the selection of CHs and relay nodes through a new combination of input variables and distance threshold approach. In CH selection, the Received Signal Strength Indicator (RSSI) scheme, residual energy, and centrality variable were proposed. Fuzzy logic was utilized in selecting the appropriate CHs based on these variables in the MATLAB. In relay node selection, the selection is based on the distance threshold according to the nearest distance with the BS. The selection of the optimal number of relay nodes is performed using K-Optimal and K-Means techniques. This ensures that all CHs are connected to at least one corresponding relay node (i.e. a 2-tier network) to execute the routing process and send the data to BS. To evaluate the proposal, the performance of Multi-Tier Protocol (MAP) and Stable Election Protocol (SEP) was compared based on 100, 200, and 800 nodes with 1 J and random energy. The simulation results showed that our proposed approach, refer to as Energy Efficient Cluster Heads and Relay Nodes (EECR) selection approach, extended the network lifetime of the wireless sensor network by 43% and 33% longer than SEP and MAP, respectively. This thesis concluded that with effective combinations of variables for CHs and relay nodes selection in static environment for data routing, EECR can effectively improve the energy efficiency of WSNs