Design and analysis of a beacon-less routing protocol for large volume content dissemination in vehicular ad hoc networks

Largevolumecontentdisseminationispursuedbythegrowingnumberofhighquality applications for Vehicular Ad hoc NETworks(VANETs), e.g., the live road surveillance service and the video-based overtaking assistant service. For the highly dynamical vehicular network topology, beacon-less routing protocols have been proven to be efficient in achieving a balance between the system performance and the control overhead. However, to the authors’ best knowledge, the routing design for large volume content has not been well considered in the previous work, which will introduce new challenges, e.g., the enhanced connectivity requirement for a radio link. In this paper, a link Lifetime-aware Beacon-less Routing Protocol (LBRP) is designed for large volume content delivery in VANETs. Each vehicle makes the forwarding decision based on the message header information and its current state, including the speed and position information. A semi-Markov process analytical model is proposed to evaluate the expected delay in constructing one routing path for LBRP. Simulations show that the proposed LBRP scheme outperforms the traditional dissemination protocols in providing a low end-to-end delay. The analytical model is shown to exhibit a good match on the delay estimation with Monte Carlo simulations, as well

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

Dashboard para apoio à decisão na análise de tráfego e ambiente de uma cidade inteligente

Mestrado em Engenharia InformáticaCities are continuously growing in population, vehicles, infrastructures and intelligence. Using and deploying smart technologies in the cities infrastructure can improve the multiple existing areas of a city, such as mobility by improving the road network, infrastructure by improving the urban planning and population by contributing with better services. Porto city has an in-place infrastructure of xed and moving sensors in more than 400 buses and roadside units, with both GPS and mobility sensors in moving elements, and with environmental sensors in xed units. This infrastructure can provide valuable data that can extract information to better understand the city and, eventually, support actions to improve the city mobility, urban planning, and environment. This work has the objective of using the information generated by the sensors placed in the buses of Porto, and using it to analyze the road tra c information based on the mobility patterns of the buses. The data from the environmental sensors deployed in Porto is also provided and used to analyze the air quality of the city and its in uence by the tra c. The developed system provides a full stack integration of the information into a city dashboard that displays and correlates the data generated from the buses movement and the environment from the xed sensors, allowing di erent visualizations over the road tra c and the environment in the city, and decisions over the current status of the city. A good example is the relation of bus speed variation with possible anomalies on the road or tra c jams. Visualizing such information with a superior level of detail on the road tra c, more anomalies can be found, adding more value to a city manager when taking urban planning decisions to improve the city mobility in a smart way.As cidades tem estado continuamente a crescer tanto em populaçao, como em veiculos, infra-estruturas e inteligencia. Ao implementar e usar tecnologias inteligentes na infra-estrutura das cidades, e possivel melhorar as diversas areas de uma cidade, como a mobilidade ao melhorar a infra-estrutura das estradas, as infra-estruturas ao melhorar o planeamento urbano e a populaçao ao disponibilizar melhores serviços. A cidade do Porto tem neste momento uma infra-estrutura de sensores fixos e moveis em mais de 400 autocarros, e unidades de comunicação na estrada, com GPS e sensores de mobilidade nos elementos moveis, e com sensores ambientais nas unidades fixas. Esta infra-estrutura proporciona dados valiosos baseados nos padrões de mobilidade dos autocarros. Os dados dos sensores ambientais são também disponibilizados e usados para analisar a qualidade do ar da cidade e a sua influencia perante o trafego de veículos. O sistema desenvolvido fornece uma integração completa da informação num dashboard da cidade que mostra e correlaciona os dados gerados pelo movimento dos autocarros e do ambiente a partir dos sensores fixos, permitindo diferentes visualizações do trânsito nas estradas e do ambiente na cidade, e decisões sobre o estado actual da cidade. Um bom exemplo e a relação da variação da velocidade dos autocarros com possíveis anomalias na estrada ou engarrafamentos. Ao visualizar esta informação com um nível de detalhe superior nas anomalias encontradas na estrada, o gestor da cidade pode beneficiar do dashboard quando precisa de tomar decisões relacionadas com o planeamento urbano e assim melhorar de uma maneira inteligente a mobilidade da cidade

On the Feasibility of Social Network-based Pollution Sensing in ITSs

Intense vehicular traffic is recognized as a global societal problem, with a multifaceted influence on the quality of life of a person. Intelligent Transportation Systems (ITS) can play an important role in combating such problem, decreasing pollution levels and, consequently, their negative effects. One of the goals of ITSs, in fact, is that of controlling traffic flows, measuring traffic states, providing vehicles with routes that globally pursue low pollution conditions. How such systems measure and enforce given traffic states has been at the center of multiple research efforts in the past few years. Although many different solutions have been proposed, very limited effort has been devoted to exploring the potential of social network analysis in such context. Social networks, in general, provide direct feedback from people and, as such, potentially very valuable information. A post that tells, for example, how a person feels about pollution at a given time in a given location, could be put to good use by an environment aware ITS aiming at minimizing contaminant emissions in residential areas. This work verifies the feasibility of using pollution related social network feeds into ITS operations. In particular, it concentrates on understanding how reliable such information is, producing an analysis that confronts over 1,500,000 posts and pollution data obtained from on-the- field sensors over a one-year span.Comment: 10 pages, 15 figures, Transaction Forma

Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches