Community-based Message Opportunistic Transmission

Mobile Social Networks (MSNs) is a kind of opportunistic networks, which is composed of a large number of mobile nodes with social characteristic. Up to now, the prevalent communitybased routing algorithms mostly select the most optimal social characteristic node to forward messages. But they almost don\u27t consider the effect of community distribution on mobile nodes and the time-varying characteristic of network. These algorithms usually result in high consumption of network resources and low successful delivery ratio if they are used directly in mobile social networks. We build a time-varying community-based network model, and propose a community-aware message opportunistic transmission algorithm (CMOT) in this paper. For inter-community messages transmission, the CMOT chooses an optimal community path by comparing the community transmission probability. For intra-community in local community, messages are forwarded according to the encounter probability between nodes. The simulation results show that the CMOT improves the message successful delivery ratio and reduces network overhead obviously, compared with classical routing algorithms, such as PRoPHET, MaxProp, Spray and Wait, and CMTS

Data Dissemination And Information Diffusion In Social Networks

Data dissemination problem is a challenging issue in social networks, especially in mobile social networks, which grows rapidly in recent years worldwide with a significant increasing number of hand-on mobile devices such as smart phones and pads. Short-range radio communications equipped in mobile devices enable mobile users to access their interested contents not only from access points of Internet but also from other mobile users. Through proper data dissemination among mobile users, the bandwidth of the short-range communications can be better utilized and alleviate the stress on the bandwidth of the cellular networks. In this dissertation proposal, data dissemination problem in mobile social networks is studied. Before data dissemination emerges in the research of mobile social networks, routing protocol of finding efficient routing path in mobile social networks was the focus, which later became the pavement for the study of the efficient data dissemination. Data dissemination priorities on packet dissemination from multiple sources to multiple destinations while routing protocol simply focus on finding routing path between two ends in the networks. The first works in the literature of data dissemination problem were based on the modification and improvement of routing protocols in mobile social networks. Therefore, we first studied and proposed a prediction-based routing protocol in delay tolerant networks. Delay tolerant network appears earlier than mobile social networks. With respect to delay tolerant networks, mobile social networks also consider social patterns as well as mobility patterns. In our work, we simply come up with the prediction-based routing protocol through analysis of user mobility patterns. We can also apply our proposed protocol in mobile social networks. Secondly, in literature, efficient data dissemination schemes are proposed to improve the data dissemination ratio and with reasonable overhead in the networks. However, the overhead may be not well controlled in the existing works. A social-aware data dissemination scheme is proposed in this dissertation proposal to study efficient data dissemination problem with controlled overhead in mobile social networks. The data dissemination scheme is based on the study on both mobility patterns and social patterns of mobile social networks. Thirdly, in real world cases, an efficient data dissemination in mobile social networks can never be realized if mobile users are selfish, which is true unfortunately in fact. Therefore, how to strengthen nodal cooperation for data dissemination is studied and a credit-based incentive data dissemination protocol is also proposed in this dissertation. Data dissemination problem was primarily researched on mobile social networks. When consider large social networks like online social networks, another similar problem was researched, namely, information diffusion problem. One specific problem is influence maximization problem in online social networks, which maximize the result of information diffusion process. In this dissertation proposal, we proposed a new information diffusion model, namely, sustaining cascading (SC) model to study the influence maximization problem and based on the SC model, we further plan our research work on the information diffusion problem aiming at minimizing the influence diffusion time with subject to an estimated influence coverage

Mobility management-based autonomous energy-aware framework using machine learning approach in dense mobile networks

A paramount challenge of prohibiting increased CO2 emissions for network densification is to deliver the Fifth Generation (5G) cellular capacity and connectivity demands, while maintaining a greener, healthier and prosperous environment. Energy consumption is a demanding consideration in the 5G era to combat several challenges such as reactive mode of operation, high latency wake up times, incorrect user association with the cells, multiple cross-functional operation of Self-Organising Networks (SON), etc. To address this challenge, we propose a novel Mobility Management-Based Autonomous Energy-Aware Framework for analysing bus passengers ridership through statistical Machine Learning (ML) and proactive energy savings coupled with CO2 emissions in Heterogeneous Network (HetNet) architecture using Reinforcement Learning (RL). Furthermore, we compare and report various ML algorithms using bus passengers ridership obtained from London Overground (LO) dataset. Extensive spatiotemporal simulations show that our proposed framework can achieve up to 98.82% prediction accuracy and CO2 reduction gains of up to 31.83%

Roteamento em redes tolerantes a atrasos e interrupções: uma abordagem baseada em redes neurais

Tese (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Automação e Sistemas, Florianópolis, 2015.As Redes Tolerantes a Atrasos e Interrupções (DTN) foram concebidas para operar considerando interrupções e grandes atrasos na comunicação. O roteamento se torna uma tarefa mais desafiadora em contextos com alta frequência de mudança da topologia e poucas informações a respeito da topologia futura. Em uma rede formada somente por ônibus do sistema de transporte público, os contatos entre os ônibus acontecem de forma quase-oportunista devido à regularidade não estritamente seguida nos itinerários. Uma forma de melhorar o roteamento nas DTNs é explorar informações históricas e do cenário para aumentar a taxa de entrega de mensagens e diminuir o atraso na entrega e o consumo de recursos. Com poucas informações a serem exploradas no cenário, o roteamento fica mais difícil de ser tratado. Neste contexto, esta tese propõe uma nova abordagem de roteamento baseado em Redes Neurais Artificiais (RNA) que apresenta vantagens em relação as outras estratégias aplicáveis nas mesmas condições, tal como a estratégia do caminho de probabilidade máxima (MaxProp). Um mecanismo de predição de contatos baseado em RNA foi desenvolvido para permitir a obtenção de contatos futuros que então são utilizados em um mecanismo de construção de jornadas, permitindo estimar a melhor jornada até o destino. Um procedimento para projetar as RNAs é apresentado. Um simulador de troca de mensagens foi desenvolvido para testar as estratégias avaliadas. Os resultados obtidos demonstram que a abordagem desenvolvida atinge um maior número de mensagens entregues, menor atraso e menor custo de uso da rede. Esses resultados foram obtidos nas versões com ou sem replicação de mensagens utilizando dados reais ou sintéticos. Uma modelagem para a implementação da estratégia de roteamento proposta projetada para funcionar na arquitetura da Internet Research Task Force (IRTF) é apresentada.Abstract : Delay/disruption Tolerant Networks (DTN) are designed to operate considering interruptions and high delays in communication. The routing becomes a more challenging task in a context of a high frequency of topology changes in which little information are available. In a network formed just by buses of a bus transportation system, the contacts are quasi-opportunistic due the regularity in the itineraries not strictly followed by the bus. A manner to improve the routing in DTN is to exploit historical information to increase the delivery rate and decrease the delivery delay and resources consumption. In this context, this thesis describes a new routing approach based on Artificial Neural Networks (ANN) presenting advantages over other applicable strategies in the same conditions, such as the maximum probability path (Max- Prop). A contact predictor based on ANN was developed to achieve future contacts. The predicted contacts are used in a journey predictor, aiming to obtain the best journey to the destination. A procedure for designing of ANNs is presented. A message forwarding simulator was developed to test the evaluated strategies. The obtained results demonstrate that the developed approach increases the number of delivered messages, decreases the delivery delay and decreases the delivery cost. These results are verified both in the version with message replication, as without message replication, using synthetic or real data. A modeling for the implementation of the proposed routing strategy designed to work in the Internet Research Task Force (IRTF) architecture is presented