Towards Opportunistic Data Dissemination in Mobile Phone Sensor Networks

Recently, there has been a growing interest within the research community in developing opportunistic routing protocols. Many schemes have been proposed; however, they differ greatly in assumptions and in type of network for which they are evaluated. As a result, researchers have an ambiguous understanding of how these schemes compare against each other in their specific applications. To investigate the performance of existing opportunistic routing algorithms in realistic scenarios, we propose a heterogeneous architecture including fixed infrastructure, mobile infrastructure, and mobile nodes. The proposed architecture focuses on how to utilize the available, low cost short-range radios of mobile phones for data gathering and dissemination. We also propose a new realistic mobility model and metrics. Existing opportunistic routing protocols are simulated and evaluated with the proposed heterogeneous architecture, mobility models, and transmission interfaces. Results show that some protocols suffer long time-to-live (TTL), while others suffer short TTL. We show that heterogeneous sensor network architectures need heterogeneous routing algorithms, such as a combination of Epidemic and Spray and Wait

A Distance-Aware Replica Adaptive Data Gathering Protocol for Delay Tolerant Mobile Sensor Networks

In Delay Tolerant Mobile Sensor Networks (DTMSNs) that have the inherent features of intermitted connectivity and frequently changing network topology it is reasonable to utilize multi-replica schemes to improve the data gathering performance. However, most existing multi-replica approaches inject a large amount of message copies into the network to increase the probability of message delivery, which may drain each mobile node’s limited battery supply faster and result in too much contention for the restricted resources of the DTMSN, so a proper data gathering scheme needs a trade off between the number of replica messages and network performance. In this paper, we propose a new data gathering protocol called DRADG (for Distance-aware Replica Adaptive Data Gathering protocol), which economizes network resource consumption through making use of a self-adapting algorithm to cut down the number of redundant replicas of messages, and achieves a good network performance by leveraging the delivery probabilities of the mobile sensors as main routing metrics. Simulation results have shown that the proposed DRADG protocol achieves comparable or higher message delivery ratios at the cost of the much lower transmission overhead than several current DTMSN data gathering schemes

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

Towards efficacy and efficiency in sparse delay tolerant networks

The ubiquitous adoption of portable smart devices has enabled a new way of communication via Delay Tolerant Networks (DTNs), whereby messages are routed by the personal devices carried by ever-moving people. Although a DTN is a type of Mobile Ad Hoc Network (MANET), traditional MANET solutions are ill-equipped to accommodate message delivery in DTNs due to the dynamic and unpredictable nature of people\u27s movements and their spatio-temporal sparsity. More so, such DTNs are susceptible to catastrophic congestion and are inherently chaotic and arduous. This manuscript proposes approaches to handle message delivery in notably sparse DTNs. First, the ChitChat system [69] employs the social interests of individuals participating in a DTN to accurately model multi-hop relationships and to make opportunistic routing decisions for interest-annotated messages. Second, the ChitChat system is hybridized [70] to consider both social context and geographic information for learning the social semantics of locations so as to identify worthwhile routing opportunities to destinations and areas of interest. Network density analyses of five real-world datasets is conducted to identify sparse datasets on which to conduct simulations, finding that commonly-used datasets in past DTN research are notably dense and well connected, and suggests two rarely used datasets are appropriate for research into sparse DTNs. Finally, the Catora system is proposed to address congestive-driven degradation of service in DTNs by accomplishing two simultaneous tasks: (i) expedite the delivery of higher quality messages by uniquely ordering messages for transfer and delivery, and (ii) avoid congestion through strategic buffer management and message removal. Through dataset-driven simulations, these systems are found to outperform the state-of-the-art, with ChitChat facilitating delivery in sparse DTNs and Catora unencumbered by congestive conditions --Abstract, page iv