Connectivity Analysis of Directed Highway VANETs using Graph Theory

Graph theory is a promising approach in handling the problem of estimating the connectivity probability of vehicular ad-hoc networks (VANETs). With a communication network represented as graph, graph connectivity indicators become valid for connectivity analysis of communication networks as well. In this article, we discuss two different graph-based methods for VANETs connectivity analysis showing that they capture the same behavior as estimated using probabilistic models. The study is, then, extended to include the case of directed VANETs, resulting from the utilization of different communication ranges by different vehicles. Overall, the graph-based methods prove a robust performance, as they can be simply diversified into scenarios that are too complex to acquire a rigid probabilistic model for them.Comment: 21 pages, 6 figure

Routing, Localization And Positioning Protocols For Wireless Sensor And Actor Networks

Wireless sensor and actor networks (WSANs) are distributed systems of sensor nodes and actors that are interconnected over the wireless medium. Sensor nodes collect information about the physical world and transmit the data to actors by using one-hop or multi-hop communications. Actors collect information from the sensor nodes, process the information, take decisions and react to the events. This dissertation presents contributions to the methods of routing, localization and positioning in WSANs for practical applications. We first propose a routing protocol with service differentiation for WSANs with stationary nodes. In this setting, we also adapt a sports ranking algorithm to dynamically prioritize the events in the environment depending on the collected data. We extend this routing protocol for an application, in which sensor nodes float in a river to gather observations and actors are deployed at accessible points on the coastline. We develop a method with locally acting adaptive overlay network formation to organize the network with actor areas and to collect data by using locality-preserving communication. We also present a multi-hop localization approach for enriching the information collected from the river with the estimated locations of mobile sensor nodes without using positioning adapters. As an extension to this application, we model the movements of sensor nodes by a subsurface meandering current mobility model with random surface motion. Then we adapt the introduced routing and network organization methods to model a complete primate monitoring system. A novel spatial cut-off preferential attachment model and iii center of mass concept are developed according to the characteristics of the primate groups. We also present a role determination algorithm for primates, which uses the collection of spatial-temporal relationships. We apply a similar approach to human social networks to tackle the problem of automatic generation and organization of social networks by analyzing and assessing interaction data. The introduced routing and localization protocols in this dissertation are also extended with a novel three dimensional actor positioning strategy inspired by the molecular geometry. Extensive simulations are conducted in OPNET simulation tool for the performance evaluation of the proposed protocol

Hybrid Device-to-Device and Device-to-Vehicle Networks for Energy-Efficient Emergency Communications

Recovering postdisaster communications has become a major challenge for search and rescue. Device-to-device (D2D) and device-to-vehicle (D2V) networks have drawn attention. However, due to the limited D2D coverage and onboard energy, establishing a hybrid D2D and D2V network is promising. In this article, we jointly establish, optimize, and fuse D2D and D2V networks to support energy-efficient emergency communications. First, we establish a D2D network by optimally dividing ground devices (GDs) into multiple clusters and identifying temporary data caching centers (TDCCs) from GDs in clusters. Accordingly, emergency data returned from GDs is cached in TDCCs. Second, given the distribution of TDCCs, unmanned aerial vehicles (UAVs) are dispatched to fetch data from TDCCs. Therefore, we establish a UAV-assisted D2V network through path planning and network configuration optimization. Specifically, optimal path planning is implemented using cascaded waypoint and motion planning and optimal network configurations are determined by multiobjective optimization. Consequently, the best tradeoff between emergency response time and energy consumption is achieved, subject to a given set of constraints on signal-to-interference-plus-noise ratios, the number of UAVs, transmit power, and energy. Simulation results show that our proposed approach outperforms benchmark schemes in terms of energy efficiency, contributing to large-scale postdisaster emergency response.Comment: 12 page

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

Estratégias de encaminhamento para recolha oportunística de informação em redes móveis de internet das coisas

High vehicular mobility in urban scenarios originates inter-vehicles communication discontinuities, a highly important factor when designing a forwarding strategy for vehicular networks. Store, carry and forward mechanisms enable the usage of vehicular networks in a large set of applications, such as sensor data collection in IoT, contributing to smart city platforms. This work focuses on two main topics to enhance the forwarding decision: i) forwarding strategies that make use of location-aware and social-based to perform neighborhood selection, ii) and packet selection mechanisms to provide Quality of Service (QoS). The neighborhood selection is performed through multiple metrics, resulting in three forwarding strategies: (1) Gateway Location Awareness (GLA), a location-aware ranking classification making use of velocity, heading angle and distance to the gateway, to select the vehicles with higher chance to deliver the information in a shorter period of time, thus differentiating nodes through their movement patterns; (2) Aging Social-Aware Ranking (ASAR) that exploits the social behaviours of each vehicle, where nodes are ranked based on a historical contact table, differentiating vehicles with a high number of contacts from those who barely contact with other vehicles; (3) and to merge both location and social aforementioned algorithms, a hybrid approach emerges, thus generating a more intelligent mechanism. Allied to the forwarding criteria, two packet selection mechanisms are proposed to address distinct network functionalities, namely: Distributed Packet Selection, that focuses primarily on data type prioritization and secondly, on packet network lifetime; and Equalized Packet Selection, which uses network metrics to calculate a storage packet ranking. To do so, the packet number of hops, the packet type and packet network lifetime are used. In order to perform the evaluation of the proposed mechanisms, both real and emulation experiments were performed. For each forwarding strategy, it is evaluated the influence of several parameters in the network's performance, as well as comparatively evaluate the strategies in different scenarios. Experiment results, obtained with real traces of both mobility and vehicular connectivity from a real city-scale urban vehicular network, are used to evaluate the performance of GLA, ASAR and HYBRID schemes, and their results are compared to lower- and upper-bounds. Later, these strategies' viability is also validated in a real scenario. The obtained results show that these strategies are a good tradeoff to maximize data delivery ratio and minimize network overhead, while making use of moving networks as a smart city network infrastructure. To evaluate the proposed packet selection mechanisms, a First In First Out packet selection technique is used as ground rule, thus contrasting with the more objective driven proposed techniques. The results show that the proposed mechanisms are capable of provide distinct network functionalities, from prioritizing a packet type to enhancing the network's performance.A elevada mobilidade em cenários veiculares urbanos origina descontinuidades de comunicação entre veículos, um fator altamente importante quando se desenha uma estratégia de encaminhamento para redes veiculares. Mecanismos de store, carry and forward (guardar, carregar e entregar) possibilitam a recolha de dados de sensores em aplicações da Internet das coisas, contribuindo para plataformas de cidades inteligentes. Este trabalho é focado em dois tópicos principais de forma a melhorar a decisão de encaminhamento: i) estratégias de encaminhamento que fazem uso de métricas sociais e de localização para efetuar a seleção de vizinhos, ii) e mecanismos de seleção de pacotes que qualificam a rede com qualidade de serviço. A seleção de vizinhos é feita através de múltiplas métricas, resultando em três estratégias de encaminhamento: Gateway Location Awareness (GLA), uma classificação baseada em localização que faz uso de velocidade, ângulo de direção e distância até uma gateway, para selecionar os veículos com maior probabilidade de entregar a informação num menor período temporal, distinguindo os veículos através dos seus padrões de movimento. Aging Social-Aware Ranking (ASAR) explora os comportamentos sociais de cada veículo, onde é atribuída uma classificação aos veículos com base num histórico de contactos, diferenciando veículos com um alto número de contactos de outros com menos. Por fim, por forma a tirar partido das distintas características de cada uma das destas estratégias, é proposta uma abordagem híbrida, Hybrid between GLA and ASAR (HYBRID). Aliado ao critério de encaminhamento, são propostos dois mecanismos de seleção de pacotes que focam distintas funcionalidades na rede, sendo estes: Distributed Packet Selection, que foca em primeiro lugar na prioritização de determinados tipos de pacotes e em segundo lugar, no tempo de vida que resta ao pacote na rede; e Equalized Packet Selection, que usa métricas da rede para calcular a classificação de cada pacote em memória. Para tal, é usado o numero de saltos do pacote, o tipo de dados do pacote e o tempo de vida que resta ao pacote na rede. De forma a avaliar os mecanismos propostos, foram realizadas experiências em emulador e em cenário real. Para cada estratégia de encaminhamento, e avaliada a influência de vários parâmetros de configuração no desempenho da rede. Para além disso, é feita uma avaliação comparativa entre as várias estratégias em diferentes cenários. Resultados experimentais, obtidos usando traços reais de mobilidade e conetividade de uma rede veicular urbana, são utilizados para avaliar a performance dos esquemas GLA, ASAR e HYRID. Posteriormente, a viabilidade destas estratégias é também validada em cenário real. Os resultados obtidos mostram que estas estratégias são um bom tradeoff para maximizar a taxa de entrega de dados e minimizar a sobrecarga de dados na rede. Para avaliar os mecanismos de seleção de pacotes, um simples mecanismo First In First Out é utilizado como base, contrapondo com as técnicas propostas mais orientadas a objectivos concretos. Os resultados obtidos mostram que os mecanismos propostos são capazes de proporcionar à rede diferentes funcionalidades, desde prioritização de determinado tipos de dados a melhoramentos no desempenho da rede.Agradeço à Fundação Portuguesa para a Ciência e Tecnologia pelo suporte financeiro através de fundos nacionais e quando aplicável cofi nanciado pelo FEDER, no âmbito do Acordo de Parceria PT2020 pelo projecto MobiWise através do programa Operacional Competitividade e Internacionalização (COMPETE 2020) do Portugal 2020 (POCI-01-0145-FEDER-016426).Mestrado em Engenharia Eletrónica e Telecomunicaçõe

The Power of Quasi-Shortest Paths: ρ-Geodesic Betweenness Centrality

International audienceBetweenness centrality metrics usually underestimate the importance of nodes that are close to shortest paths but do not exactly fall on them. In this paper, we reevaluate the importance of such nodes and propose the ρ-geodesic betweenness centrality, a novel metric that assigns weights to paths (and, consequently, to nodes on these paths) according to how close they are to shortest paths. The paths that are just slightly longer than the shortest one are defined as quasi-shortest paths, and they are able to increase or to decrease the importance of a node according to how often the node falls on them. We compare the proposed metric with the traditional, distance-scaled, and random walk betweenness centralities using four network datasets with distinct characteristics. The results show that the proposed metric, besides better assessing the topological role of a node, is also able to maintain the rank position of nodes overtime compared to the other metrics; this means that network dynamics affect less our metric than others. Such a property could help avoid, for instance, the waste of resources caused when data follow only the shortest paths and reduce associated costs

Optimization and Communication in UAV Networks

UAVs are becoming a reality and attract increasing attention. They can be remotely controlled or completely autonomous and be used alone or as a fleet and in a large set of applications. They are constrained by hardware since they cannot be too heavy and rely on batteries. Their use still raises a large set of exciting new challenges in terms of trajectory optimization and positioning when they are used alone or in cooperation, and communication when they evolve in swarm, to name but a few examples. This book presents some new original contributions regarding UAV or UAV swarm optimization and communication aspects

Contributions to energy-aware demand-response systems using SDN and NFV for fog computing

Ever-increasing energy consumption, the depletion of non-renewable resources, the climate impact associated with energy generation, and finite energy-production capacity are important concerns worldwide that drive the urgent creation of new energy management and consumption schemes. In this regard, by leveraging the massive connectivity provided by emerging communications such as the 5G systems, this thesis proposes a long-term sustainable Demand-Response solution for the adaptive and efficient management of available energy consumption for Internet of Things (IoT) infrastructures, in which energy utilization is optimized based on the available supply. In the proposed approach, energy management focuses on consumer devices (e.g., appliances such as a light bulb or a screen). In this regard, by proposing that each consumer device be part of an IoT infrastructure, it is feasible to control its respective consumption. The proposal includes an architecture that uses Network Functions Virtualization (NFV) and Software Defined Networking technologies as enablers to promote the primary use of energy from renewable sources. Associated with architecture, this thesis presents a novel consumption model conditioned on availability in which consumers are part of the management process. To efficiently use the energy from renewable and non-renewable sources, several management strategies are herein proposed, such as the prioritization of the energy supply, workload scheduling using time-shifting capabilities, and quality degradation to decrease- the power demanded by consumers if needed. The adaptive energy management solution is modeled as an Integer Linear Programming, and its complexity has been identified to be NP-Hard. To verify the improvements in energy utilization, an optimal algorithmic solution based on a brute force search has been implemented and evaluated. Because the hardness of the adaptive energy management problem and the non-polynomial growth of its optimal solution, which is limited to energy management for a small number of energy demands (e.g., 10 energy demands) and small values of management mechanisms, several faster suboptimal algorithmic strategies have been proposed and implemented. In this context, at the first stage, we implemented three heuristic strategies: a greedy strategy (GreedyTs), a genetic-algorithm-based solution (GATs), and a dynamic programming approach (DPTs). Then, we incorporated into both the optimal and heuristic strategies a prepartitioning method in which the total set of analyzed services is divided into subsets of smaller size and complexity that are solved iteratively. As a result of the adaptive energy management in this thesis, we present eight strategies, one timal and seven heuristic, that when deployed in communications infrastructures such as the NFV domain, seek the best possible scheduling of demands, which lead to efficient energy utilization. The performance of the algorithmic strategies has been validated through extensive simulations in several scenarios, demonstrating improvements in energy consumption and the processing of energy demands. Additionally, the simulation results revealed that the heuristic approaches produce high-quality solutions close to the optimal while executing among two and seven orders of magnitude faster and with applicability to scenarios with thousands and hundreds of thousands of energy demands. This thesis also explores possible application scenarios of both the proposed architecture for adaptive energy management and algorithmic strategies. In this regard, we present some examples, including adaptive energy management in-home systems and 5G networks slicing, energy-aware management solutions for unmanned aerial vehicles, also known as drones, and applicability for the efficient allocation of spectrum in flex-grid optical networks. Finally, this thesis presents open research problems and discusses other application scenarios and future work.El constante aumento del consumo de energía, el agotamiento de los recursos no renovables, el impacto climático asociado con la generación de energía y la capacidad finita de producción de energía son preocupaciones importantes en todo el mundo que impulsan la creación urgente de nuevos esquemas de consumo y gestión de energía. Al aprovechar la conectividad masiva que brindan las comunicaciones emergentes como los sistemas 5G, esta tesis propone una solución de Respuesta a la Demanda sostenible a largo plazo para la gestión adaptativa y eficiente del consumo de energía disponible para las infraestructuras de Internet of Things (IoT), en el que se optimiza la utilización de la energía en función del suministro disponible. En el enfoque propuesto, la gestión de la energía se centra en los dispositivos de consumo (por ejemplo, electrodomésticos). En este sentido, al proponer que cada dispositivo de consumo sea parte de una infraestructura IoT, es factible controlar su respectivo consumo. La propuesta incluye una arquitectura que utiliza tecnologías de Network Functions Virtualization (NFV) y Software Defined Networking como habilitadores para promover el uso principal de energía de fuentes renovables. Asociada a la arquitectura, esta tesis presenta un modelo de consumo condicionado a la disponibilidad en el que los consumidores son parte del proceso de gestión. Para utilizar eficientemente la energía de fuentes renovables y no renovables, se proponen varias estrategias de gestión, como la priorización del suministro de energía, la programación de la carga de trabajo utilizando capacidades de cambio de tiempo y la degradación de la calidad para disminuir la potencia demandada. La solución de gestión de energía adaptativa se modela como un problema de programación lineal entera con complejidad NP-Hard. Para verificar las mejoras en la utilización de energía, se ha implementado y evaluado una solución algorítmica óptima basada en una búsqueda de fuerza bruta. Debido a la dureza del problema de gestión de energía adaptativa y el crecimiento no polinomial de su solución óptima, que se limita a la gestión de energía para un pequeño número de demandas de energía (por ejemplo, 10 demandas) y pequeños valores de los mecanismos de gestión, varias estrategias algorítmicas subóptimos más rápidos se han propuesto. En este contexto, en la primera etapa, implementamos tres estrategias heurísticas: una estrategia codiciosa (GreedyTs), una solución basada en algoritmos genéticos (GATs) y un enfoque de programación dinámica (DPTs). Luego, incorporamos tanto en la estrategia óptima como en la- heurística un método de prepartición en el que el conjunto total de servicios analizados se divide en subconjuntos de menor tamaño y complejidad que se resuelven iterativamente. Como resultado de la gestión adaptativa de la energía en esta tesis, presentamos ocho estrategias, una óptima y siete heurísticas, que cuando se despliegan en infraestructuras de comunicaciones como el dominio NFV, buscan la mejor programación posible de las demandas, que conduzcan a un uso eficiente de la energía. El desempeño de las estrategias algorítmicas ha sido validado a través de extensas simulaciones en varios escenarios, demostrando mejoras en el consumo de energía y el procesamiento de las demandas de energía. Los resultados de la simulación revelaron que los enfoques heurísticos producen soluciones de alta calidad cercanas a las óptimas mientras se ejecutan entre dos y siete órdenes de magnitud más rápido y con aplicabilidad a escenarios con miles y cientos de miles de demandas de energía. Esta tesis también explora posibles escenarios de aplicación tanto de la arquitectura propuesta para la gestión adaptativa de la energía como de las estrategias algorítmicas. En este sentido, presentamos algunos ejemplos, que incluyen sistemas de gestión de energía adaptativa en el hogar, en 5G networkPostprint (published version