Collision-free prioritized medium access control in wireless networks with hidden nodes

We propose a collision-free medium access control (MAC) protocol, which implements static-priority scheduling and works in the presence of hidden nodes. The MAC protocol allows multiple masters and is fully distributed; it is an adaptation to a wireless channel of the dominance protocol used in the CAN bus. But unlike that protocol, our protocol does not require a node having the ability to sense the channel while transmitting to the channel. Our protocol is collision-free even in the presence of hidden nodes and it achieves this without synchronized clocks or out-of-band busy tones. In addition, the protocol is designed to ensure that many non-interfering nodes can transmit in parallel and it functions for both broadcast and unicast transmissions.

Beacon scheduling in cluster-tree IEEE 802.15.4/ZigBee wireless sensor networks

The recently standardized IEEE 802.15.4/Zigbee protocol stack offers great potentials for ubiquitous and pervasive computing, namely for Wireless Sensor Networks (WSNs). However, there are still some open and ambiguous issues that turn its practical use a challenging task. One of those issues is how to build a synchronized multi-hop cluster-tree network, which is quite suitable for QoS support in WSNs. In fact, the current IEEE 802.15.4/Zigbee specifications restrict the synchronization in the beacon-enabled mode (by the generation of periodic beacon frames) to star-based networks, while it supports multi-hop networking using the peer-to-peer mesh topology, but with no synchronization. Even though both specifications mention the possible use of cluster-tree topologies, which combine multi-hop and synchronization features, the description on how to effectively construct such a network topology is missing. This report tackles this problem, unveils the ambiguities regarding the use of the cluster-tree topology and proposes two collisionfree beacon frame scheduling schemes.

ENCOURAGE architecture: support for heterogeneous smart grids

Our society relies on energy for most of its activities. One application domain inciding heavily on the energy budget regards the energy consumption in residential and non-residential buildings. The ever increasing needs for energy, resulting from the industrialization of developing countries and from the limited scalability of the traditional technologies for energy production, raises both problems and opportunities. The problems are related to the devastating effects of the greenhouse gases produced by the burning of oil and gas for energy production, and from the dependence of whole countries on companies providing gas and oil. The opportunities are mostly technological, since novel markets are opening for both energy production via renewable sources, and for innovations that can rationalize energy usage. An enticing research effort can be the mixing of these two aspects, by leveraging on ICT technologies to rationalize energy production, acquisition, and consumption. The ENCOURAGE project aims to develop embedded intelligence and integration technologies that will directly optimize energy use in buildings and enable active participation in the future smart grid environment.The primary application domains targeted by the ENCOURAGE project are non-residential buildings (e.g.: campuses) and residential buildings (e.g.: neighborhoods). The goal of the project is to achieve 20% of energy savings through the improved interoperability between various types of energy generation, consumption and storage devices; interbuilding energy exchange; and systematic performance monitoring.

A theoretical study on modelling the respiratory tract with ladder networks by means of intrinsic fractal geometry

Fractional order modeling of biological systems has received significant interest in the research community. Since the fractal geometry is characterized by a recurrent structure, the self-similar branching arrangement of the airways makes the respiratory system an ideal candidate for the application of fractional calculus theory. To demonstrate the link between the recurrence of the respiratory tree and the appearance of a fractional-order model, we develop an anatomically consistent representation of the respiratory system. This model is capable of simulating the mechanical properties of the lungs and we compare the model output with in vivo measurements of the respiratory input impedance collected in 20 healthy subjects. This paper provides further proof of the underlying fractal geometry of the human lungs, and the consequent appearance of constant-phase behavior in the total respiratory impedance.

Using remote lab networks to provide support to public secondary school education level

The advantages of networking are widely known in many areas (from business to personal ones). One particular area where networks have also proved their benefits is education. Taking the secondary school education level into account, some successful cases can be found in literature. In this paper we describe a particular remote lab network supporting physical experiments accessible to students of institutions geographically separated. The network architecture and application examples of using some of the available remote experiments are illustrated in detail.

Sistema Multi-Agente de prestação de serviços de seguimento adaptados aos requisitos do utilizador

Mestrado em Engenharia Electrotécnica e de Computadores - Área de Especialização de Telecomunicações

Recomendação de conteúdos multimédia

A quantidade e variedade de conteúdos multimédia actualmente disponíveis cons- tituem um desafio para os utilizadores dado que o espaço de procura e escolha de fontes e conteúdos excede o tempo e a capacidade de processamento dos utilizado- res. Este problema da selecção, em função do perfil do utilizador, de informação em grandes conjuntos heterogéneos de dados é complexo e requer ferramentas específicas. Os Sistemas de Recomendação surgem neste contexto e são capazes de sugerir ao utilizador itens que se coadunam com os seus gostos, interesses ou necessidades, i.e., o seu perfil, recorrendo a metodologias de inteligência artificial. O principal objectivo desta tese é demonstrar que é possível recomendar em tempo útil conteúdos multimédia a partir do perfil pessoal e social do utilizador, recorrendo exclusivamente a fontes públicas e heterogéneas de dados. Neste sen- tido, concebeu-se e desenvolveu-se um Sistema de Recomendação de conteúdos multimédia baseado no conteúdo, i.e., nas características dos itens, no historial e preferências pessoais e nas interacções sociais do utilizador. Os conteúdos mul- timédia recomendados, i.e., os itens sugeridos ao utilizador, são provenientes da estação televisiva britânica, British Broadcasting Corporation (BBC), e estão classificados de acordo com as categorias dos programas da BBC. O perfil do utilizador é construído levando em conta o historial, o contexto, as preferências pessoais e as actividades sociais. O YouTube é a fonte do histo- rial pessoal utilizada, permitindo simular a principal fonte deste tipo de dados - a Set-Top Box (STB). O historial do utilizador é constituído pelo conjunto de vídeos YouTube e programas da BBC vistos pelo utilizador. O conteúdo dos vídeos do YouTube está classificado segundo as categorias de vídeo do próprio YouTube, sendo efectuado o mapeamento para as categorias dos programas da BBC. A informação social, que é proveniente das redes sociais Facebook e Twit- ter, é recolhida através da plataforma Beancounter. As actividades sociais do utilizador obtidas são filtradas para extrair os filmes e séries que são, por sua vez, enriquecidos semanticamente através do recurso a repositórios abertos de dados interligados. Neste caso, os filmes e séries são classificados através dos géneros da IMDb e, posteriormente, mapeados para as categorias de programas da BBC. Por último, a informação do contexto e das preferências explícitas, através da classificação dos itens recomendados, do utilizador são também contempladas. O sistema desenvolvido efectua recomendações em tempo real baseado nas actividades das redes sociais Facebook e Twitter, no historial de vídeos Youtube e de programas da BBC vistos e preferências explícitas. Foram realizados testes com cinco utilizadores e o tempo médio de resposta do sistema para criar o conjunto inicial de recomendações foi 30 s. As recomendações personalizadas são geradas e actualizadas mediante pedido expresso do utilizador.

Using the learning tools interoperability framework for LMS integration in service oriented architectures

The LMS plays an indisputable role in the majority of the eLearning environments. This eLearning system type is often used for presenting, solving and grading simple exercises. However, exercises from complex domains, such as computer programming, require heterogeneous systems such as evaluation engines, learning objects repositories and exercise resolution environments. The coordination of networks of such disparate systems is rather complex. This work presents a standard approach for the coordination of a network of eLearning systems supporting the resolution of exercises. The proposed approach use a pivot component embedded in the LMS with two roles: provide an exercise resolution environment and coordinate the communication between the LMS and other systems exposing their functions as web services. The integration of the pivot component with the LMS relies on the Learning Tools Interoperability. The validation of this approach is made through the integration of the component with LMSs from two vendors.

Modelling a network of heterogeneous e-learning systems

In recent years emerged several initiatives promoted by educational organizations to adapt Service Oriented Architectures (SOA) to e-learning. These initiatives commonly named eLearning Frameworks share a common goal: to create flexible learning environments by integrating heterogeneous systems already available in many educational institutions. However, these frameworks were designed for integration of systems participating in business like processes rather than on complex pedagogical processes as those related to automatic evaluation. Consequently, their knowledge bases lack some fundamental components that are needed to model pedagogical processes. The objective of the research described in this paper is to study the applicability of eLearning frameworks for modelling a network of heterogeneous eLearning systems, using the automatic evaluation of programming exercises as a case study. The paper surveys the existing eLearning frameworks to justify the selection of the e-Framework. This framework is described in detail and identified the necessary components missing from its knowledge base, more precisely, a service genre, expression and usage model for an evaluation service. The extensibility of the framework is tested with the definition of this service. A concrete model for evaluation of programming exercises is presented as a validation of the proposed approach.

Fast mobility support in low-power wireless networks: smart-HOP over RPL/6LoWPAN

With the emergence of low-power wireless hardware new ways of communication were needed. In order to standardize the communication between these low powered devices the Internet Engineering Task Force (IETF) released the 6LoWPAN stand- ard that acts as an additional layer for making the IPv6 link layer suitable for the lower-power and lossy networks. In the same way, IPv6 Routing Protocol for Low- Power and Lossy Networks (RPL) has been proposed by the IETF Routing Over Low power and Lossy networks (ROLL) Working Group as a standard routing protocol for IPv6 routing in low-power wireless sensor networks. The research performed in this thesis uses these technologies to implement a mobility process. Mobility management is a fundamental yet challenging area in low-power wireless networks. There are applications that require mobile nodes to exchange data with a xed infrastructure with quality-of-service guarantees. A prime example of these applications is the monitoring of patients in real-time. In these scenarios, broadcast- ing data to all access points (APs) within range may not be a valid option due to the energy consumption, data storage and complexity requirements. An alternative and e cient option is to allow mobile nodes to perform hand-o s. Hand-o mechanisms have been well studied in cellular and ad-hoc networks. However, low-power wireless networks pose a new set of challenges. On one hand, simpler radios and constrained resources ask for simpler hand-o schemes. On the other hand, the shorter coverage and higher variability of low-power links require a careful tuning of the hand-o parameters. In this work, we tackle the problem of integrating smart-HOP within a standard protocol, speci cally RPL. The simulation results in Cooja indicate that the pro- posed scheme minimizes the hand-o delay and the total network overhead. The standard RPL protocol is simply unable to provide a reliable mobility support sim- ilar to other COTS technologies. Instead, they support joining and leaving of nodes, with very low responsiveness in the existence of physical mobility.

Management Driven Hybrid Multicast Framework for Content Aware Networks

The need for better adaptation of networks to transported flows has led to research on new approaches such as content aware networks and network aware applications. In parallel, recent developments of multimedia and content oriented services and applications such as IPTV, video streaming, video on demand, and Internet TV reinforced interest in multicast technologies. IP multicast has not been widely deployed due to interdomain and QoS support problems; therefore, alternative solutions have been investigated. This article proposes a management driven hybrid multicast solution that is multi-domain and media oriented, and combines overlay multicast, IP multicast, and P2P. The architecture is developed in a content aware network and network aware application environment, based on light network virtualization. The multicast trees can be seen as parallel virtual content aware networks, spanning a single or multiple IP domains, customized to the type of content to be transported while fulfilling the quality of service requirements of the service provider.

Multiple Linear Regression and Artificial Neural Networks to Predict Time and Efficiency of Soil Vapor Extraction

The prediction of the time and the efficiency of the remediation of contaminated soils using soil vapor extraction remain a difficult challenge to the scientific community and consultants. This work reports the development of multiple linear regression and artificial neural network models to predict the remediation time and efficiency of soil vapor extractions performed in soils contaminated separately with benzene, toluene, ethylbenzene, xylene, trichloroethylene, and perchloroethylene. The results demonstrated that the artificial neural network approach presents better performances when compared with multiple linear regression models. The artificial neural network model allowed an accurate prediction of remediation time and efficiency based on only soil and pollutants characteristics, and consequently allowing a simple and quick previous evaluation of the process viability.

Performance evaluation of a cooperative reputation system for vehicular delay-tolerant networks

In the last decade, both scientific community and automotive industry enabled communications among vehicles in different kinds of scenarios proposing different vehicular architectures. Vehicular delay-tolerant networks (VDTNs) were proposed as a solution to overcome some of the issues found in other vehicular architectures, namely, in dispersed regions and emergency scenarios. Most of these issues arise from the unique characteristics of vehicular networks. Contrary to delay-tolerant networks (DTNs), VDTNs place the bundle layer under the network layer in order to simplify the layered architecture and enable communications in sparse regions characterized by long propagation delays, high error rates, and short contact durations. However, such characteristics turn contacts very important in order to exchange as much information as possible between nodes at every contact opportunity. One way to accomplish this goal is to enforce cooperation between network nodes. To promote cooperation among nodes, it is important that nodes share their own resources to deliver messages from others. This can be a very difficult task, if selfish nodes affect the performance of cooperative nodes. This paper studies the performance of a cooperative reputation system that detects, identify, and avoid communications with selfish nodes. Two scenarios were considered across all the experiments enforcing three different routing protocols (First Contact, Spray and Wait, and GeoSpray). For both scenarios, it was shown that reputation mechanisms that punish aggressively selfish nodes contribute to increase the overall network performance.

Cloud-assisted Wireless Body Area Networks

Wireless Body Area Networks (WBANs) have emerged as a promising technology for medical and non-medical applications. WBANs consist of a number of miniaturized, portable, and autonomous sensor nodes that are used for long-term health monitoring of patients. These sensor nodes continuously collect information of patients, which are used for ubiquitous health monitoring. In addition, WBANs may be used for managing catastrophic events and increasing the effectiveness and performance of rescue forces. The huge amount of data collected by WBAN nodes demands scalable, on-demand, powerful, and secure storage and processing infrastructure. Cloud computing is expected to play a significant role in achieving the aforementioned objectives. The cloud computing environment links different devices ranging from miniaturized sensor nodes to high-performance supercomputers for delivering people-centric and context-centric services to the individuals and industries. The possible integration of WBANs with cloud computing (WBAN-cloud) will introduce viable and hybrid platform that must be able to process the huge amount of data collected from multiple WBANs. This WBAN-cloud will enable users (including physicians and nurses) to globally access the processing and storage infrastructure at competitive costs. Because WBANs forward useful and life-critical information to the cloud – which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Both the cloud providers and the users must take strong security measures to protect the storage infrastructure.

Task assignment algorithms for two-type heterogeneous multiprocessors

Consider the problem of assigning implicit-deadline sporadic tasks on a heterogeneous multiprocessor platform comprising two different types of processors—such a platform is referred to as two-type platform. We present two low degree polynomial time-complexity algorithms, SA and SA-P, each providing the following guarantee. For a given two-type platform and a task set, if there exists a task assignment such that tasks can be scheduled to meet deadlines by allowing them to migrate only between processors of the same type (intra-migrative), then (i) using SA, it is guaranteed to find such an assignment where the same restriction on task migration applies but given a platform in which processors are 1+α/2 times faster and (ii) SA-P succeeds in finding a task assignment where tasks are not allowed to migrate between processors (non-migrative) but given a platform in which processors are 1+α times faster. The parameter 0<α≤1 is a property of the task set; it is the maximum of all the task utilizations that are no greater than 1. We evaluate average-case performance of both the algorithms by generating task sets randomly and measuring how much faster processors the algorithms need (which is upper bounded by 1+α/2 for SA and 1+α for SA-P) in order to output a feasible task assignment (intra-migrative for SA and non-migrative for SA-P). In our evaluations, for the vast majority of task sets, these algorithms require significantly smaller processor speedup than indicated by their theoretical bounds. Finally, we consider a special case where no task utilization in the given task set can exceed one and for this case, we (re-)prove the performance guarantees of SA and SA-P. We show, for both of the algorithms, that changing the adversary from intra-migrative to a more powerful one, namely fully-migrative, in which tasks can migrate between processors of any type, does not deteriorate the performance guarantees. For this special case, we compare the average-case performance of SA-P and a state-of-the-art algorithm by generating task sets randomly. In our evaluations, SA-P outperforms the state-of-the-art by requiring much smaller processor speedup and by running orders of magnitude faster.