<p>In this work, we share the dataset collected during the Smart Green Homes (SGH) project. The project’s goal was to develop integrated products and technology solutions for households, as well as to improve the standards of comfort and user satisfaction. This was to be achieved while improving household energy efficiency and reducing the usage of gaseous pollutants, in response to the planet’s sustainability issues. One of the tasks executed within the project was the collection of data from volunteers’ homes, including environmental information and the level of comfort as perceived by the volunteers themselves. While used in the original project, the resulting dataset contains valuable information that could not be explored at the time. We now share this dataset with the community, which can be used for various scenarios. These may include heating appliance optimisation, presence detection and environmental prediction.</p>

A common issue that occurs after conducting usability tests is how to prioritize issues for the next design iteration, that is, which should enter production first. It is also necessary to prioritize these issues in an easy-to-understand manner for internal stakeholders and in accordance with their criticality.With these two goals in mind, we propose an analysis framework with data visualization schemes to help UI/UX research teams analyze and present data from usability tests. This framework visually indicates the most problematic interaction areas. Ranked in a four-level user experience criticality scale of a digital product or prototype, the framework highlights functionalities that require improvements.To exemplify the innovative issues of this framework, we also applied it to a proof-of-concept prototype: a Unified Communications & Collaboration (UC&C) scheduling system. We had 27 participants test the prototype. The usability test proved to be particularly valuable when it came to validating the framework. This framework has enabled us to identify the most critical interaction and graphical interface problems of the prototype that need attention before the next design iteration.Overall, our results highlight the framework's innovative nature and relevance for companies or organizations concerned with the efficiency of their development process. The proposed method would enable them to make an augmented analysis and also help to optimize iterative design cycles.

<p>The technological world has grown by incorporating billions of small sensing devices, collecting and sharing large amounts of diversified data over the new generation of wireless and mobile networks. We can use semantic similarity models to help organize and optimize these devices. Even so, many of the proposed semantic similarity models do not consider the constrained and dynamic environments where these devices are present (IoT, edge computing, 5g, and next-generation networks). In this paper, we review the commonly used models, discuss the limitations of our previous model, and explore latent space methods (through matrix factorization) to reduce noise and correct the model profiles with no additional data. The new proposal is evaluated with corpus-based state-of-the-art approaches achieving competitive results while having four times faster training time than the next fastest model and occupying 36 times less disk space than the next smallest model.</p>

Monitoring road traffic is extremely important given the possibilities it opens up in terms of studying the behavior of road users, road design and planning problems, as well as because it can be used to predict future traffic. Especially on highways that connect beaches and larger urban areas, traffic is characterized by having peaks that are highly dependent on weather conditions and rest periods. This paper describes a dataset of mobility patterns of a coastal area in Aveiro region, Portugal, fully covered with traffic classification radars, over a two-year period. The sensing infrastructure was deployed in the scope of the PASMO project, an open living lab for co-operative intelligent transportation systems. The data gathered includes the speed of the detected objects, their position, and their type (heavy vehicle, light vehicle, two-wheeler, and pedestrian). The dataset includes 74,305 records, corresponding to the aggregation of road information at 10 min intervals. A brief analysis of the dataset shows the highly dynamic nature of traffic during the two-year period. In addition, the existence of meteorological records from nearby stations, and the recording of daily data on COVID-19 infections, make it possible to cross-reference information and study the influence of weather conditions and infections on traffic behavior. Dataset: https://figshare.com/s/d324f5be912e7f7a0d21 Dataset License: CC BY 4.0

Data collection within a real-world environment may be compromised by several factors such as data-logger malfunctions and communication errors, during which no data is collected. As a consequence, appropriate tools are required to handle the missing values when analysing and processing such data. This problem is often tackled via matrix decomposition. While it has been successfully applied in a wide range of applications, in this work we report an issue that has been neglected in literature and “degenerates” the quality of the imputations obtained by matrix decomposition in multivariate time-series (with smooth evolution). Briefly, the problem consists of the misalignment of the matrix decomposition result: the missing values imputations fall within an incorrect range of values and the transitions between observed and imputed values are not smooth. We address this problem by proposing a post-processing alignment strategy. According to our experiments, the post-processing adjustment substantially improves the accuracy of the imputations (when the misalignment occurs). Moreover, the results also suggest that the misalignment occurs mostly when dealing with a small number of time-series due to lack of generalization ability.

Radio astronomy and Space Infrastructures in the Azores have a great scientific and industrial interest because they benefit from a unique geographical location in the middle of the North Atlantic allowing a vast improvement in the sky coverage. This fact obviously has a very high added value for: i) the establishment of space tracking and communications networks for the emergent global small satellite fleets ii) it is invaluable to connect the radio astronomy infrastructure networks in Africa, Europe and America continents using Very Large Baseline Interferometry (VLBI) techniques, iii) it allows excellent potential for monitoring space debris and Near Earth Objects (NEOs). There is in S. Miguel island a 32-metre SATCOM antenna that could be integrated in advanced VLBI networks and be capable of additional Deep Space Network ground support. This paper explores the space science opportunities offered by the upgrade of the S. Miguel 32-metre SATCOM antenna into a world-class infrastructure for radio astronomy and space exploration: it would enable a Deep Space Network mode and would constitute a key space facility for data production, promoting local digital infrastructure investments and the testing of cutting-edge information technologies. Its Atlantic location also enables improvements in angular resolution, provides many baselines in East–West and North–South directions connecting the emergent VLBI stations in America to Europe and Africa VLBI arrays therefore contributing for greater array imaging capabilities especially for sources or well studied fields close to or below the celestial equator, where ESO facilities, ALMA, SKA and its precursors do or will operate and observe in the coming decades.

IoT platforms have become quite complex from a technical viewpoint, becoming the cornerstone for information sharing, storing, and indexing given the unprecedented scale of smart services being available by massive deployments of a large set of data-enabled devices. These platforms rely on structured formats that exploit standard technologies to deal with the gathered data, thus creating the need for carefully designed customised systems that can handle thousands of heterogeneous data sensors/actuators, multiple processing frameworks, and storage solutions. We present the SCoT2.0 platform, a generic-purpose IoT Platform that can acquire, process, and visualise data using methods adequate for both real-time processing and long-term Machine Learning (ML)-based analysis. Our goal is to develop a large-scale system that can be applied to multiple real-world scenarios and is potentially deployable on private clouds for multiple verticals. Our approach relies on extensive service containerisation, and we present the different design choices, technical challenges, and solutions found while building our own IoT platform. We validate this platform supporting two very distinct IoT projects (750 physical devices), and we analyse scaling issues within the platform components.

In order to adapt to the recent phenomenon of exponential growth of time series data sets in both academic and commercial environments, and with the goal of deriving valuable knowledge from this data, a multitude of analysis software tools have been developed to allow groups of collaborating researchers to find and annotate meaningful behavioral patterns. However, these tools commonly lack appropriate mechanisms to handle massive time series data sets of high cardinality, as well as suitable visual encodings for annotated data. In this paper we conduct a comparative study of architectural, persistence and visualization methods that can enable these analysis tools to scale with a continuously-growing data set and handle intense workloads of concurrent traffic. We implement these approaches within a web platform, integrated with authentication, versioning, and locking mechanisms that prevent overlapping contributions or unsanctioned changes. Additionally, we measure the performance of a set of databases when writing and reading varying amounts of series data points, as well as the performance of different architectural models at scale.

Fifth-generation (5G) networks are envisioned to simultaneously support several services with different connectivity requirements. In this respect, service creation time is a key performance indicator (KPI) for service providers when planning the migration to 5G. For example, the European 5G infrastructure public private partnership (5G-PPP) suggests to reduce this time from 90 hours to 90 minutes, in the different phases of the service creation time KPI identified by this organization. This reduction can be achieved by leveraging on 5G state-of-the-art technologies: network function virtualization, network slicing, software-defined networking, and cloud computing, among others. Although some authors and projects have already studied the service creation time KPI in 5G, there is no literature that comprehensively analyzes and presents results related to each phase of this KPI. In this article, we explore the potential of network function virtualization technologies to reduce service creation time. To this end, we investigate the various phases of the service creation time KPI by designing and implementing, a realistic as well as complex network service that leverages on network function virtualization and related technologies. For our use case, we chose a content delivery network service specifically designed to distribute video. This decision was based on an analysis where we considered several parameters, like the complexity in the phases of design, fulfillment, and service assurance. We dissected all phases of the service creation time KPI required to turn our service blueprint into a deployment by utilizing network function virtualization tools. Henceforth, we defined and conducted several experiments, which were oriented to analyzing the different phases of the service creation time KPI. After analyzing the obtained results, we can conclude that using these new tools permits a substantial reduction in the time taken by each phase of the service creation time KPI.

Heating appliances consume approximately 48% of the energy spent on household appliances every year. Furthermore, a malfunctioning device can increase the cost even further. Thus, there is a need to create methods that can identify the equipment's malfunctions and eventual failures before they occur. This is only possible with a combination of data acquisition, analysis and prediction/forecast. This paper presents an infrastructure that supports the previously mentioned capabilities and was deployed for failure detection in boilers, making possible to forecast faults and errors. We also present our initial predictive maintenance models based on the collected data.

Delegation of processing tasks to the network has moved from cloud-based schemes to edge computing solutions where nearby servers process requests in a timely manner. Virtualisation technologies have recently given data cloud and network providers the required flexibility to offer such on-demand resources. However, the maintenance of close computing resources presents a challenge when the served devices are on the move. In this case, if processing continuity is desired, a transference of processing resources and task state should be committed to maintain the service to end devices. The solution here presented, MIGRATE, proposes the concept of virtual mobile devices (vMDs) implemented as Virtual Functions (VxF) and acting as virtual representatives of physical processing devices. vMDs are instantiated at the edge of the access network, following a Multi-Access Edge Computing (MEC) approach, and move across different virtualisation domains. MIGRATE provides seamless and efficient transference of these software entities to follow the real location of mobile devices and continue supporting their physical counterparts. Software Defined Networks and Management and Operation functions are exploited to 'migrate' vMDs to new virtualisation domains by forwarding data flows to the former domain until the new one is prepared, while a distributed data base avoids the transference of data. The solution has been deployed in a reference vehicular scenario at the Institute of Telecommunications Aveiro premises within the 5GINFIRE European project. In particular, the system has been evaluated under different virtualisation domains to study the operation of the migration approach in a vehicular monitoring scenario. The results validate the system from the application viewpoint with a Web monitoring tool, and the migration of the digital twin provided as VxF is analysed attending to the modification of data flows, indicating a seamless transition between virtualisation domains in a timely manner.

Network slicing emerges as a key technology in next generation networks, boosted by the integration of software‐defined networking and network functions virtualization. However, while allowing resource sharing among multiple tenants, such networks must also ensure the security requirements needed for the scenarios they are employed. This letter presents the leading security challenges on the use of network slices at the packet core, the solutions that academy and industry are proposing to address them, pointing out some directions that should be considered.

The untapped source of information, extracted from the increasing number of sensors, can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. The lack of a standard context representation scheme is one of the main struggles in this research area. Conventional methods for extracting knowledge from data rely on a standard representation or a priori relation, which may not be feasible for IoT and M2M scenarios. With this in mind we propose a stream characterization model in order to provide the foundations for a novel stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic stream organizational model without enforcing specific representations. In this paper we extend our work on stream characterization and devise a novel similarity method.

This article describes a co-design process in the context of user experience (UX) and usability testing and analysis of a first proof of concept of e-collaboration features based on unified communications, co-designed within an organization aiming to optimize users' communication cognitive load. An initial digital prototype with a detailed graphical interface, and simulated user narratives was established and the qualitative validation process is described and discussed. The implemented R&D process is mainly supported on user-centred design (UCD) methodology, namely action research with service design thinking method and co-design techniques. Qualitative data was gathered with concurrent think-aloud activities (CTA) stimulated by user experience expectation questions, observation notes, with integration in an eye tracking technology system. The UCD process and results are discussed, substantiating the added value due to the individual contributions and consequent usefulness of a final unified communication service for the organization.

Mobile networks today see increasingly large numbers of connected user equipments (UE), allowing users to go beyond voice calling and tap into rich on-line services. Parallel to this evolution, other devices, such as TV’s, home automation or even Internet of Things controllers, have become integrated with connectivity capabilities, allowing them to not only be locally connected, but also be reachable from the Internet. In this paper we propose a deviceless communication approach, where data and media flows reaching a user can be individually shifted into nearby devices. To support this, we present a framework that explores and enhances Software Defined Network and Network Function Virtualisation concepts, allowing the opportunistic utilization of nearby devices as the user moves, while still being perceived as a single end-point towards external entities. An experimental validation scenario is presented, showcasing a video stream being delivered to a nearby large TV screen, allowing the user to watch the video while a voice call is routed to a nearby phone. Results showcase the feasibility of the proposed framework and how virtualisation of both the UE and the points of attachment contribute to reduce the impact of flow management in the physical devices.

Viewers post a lot of TV program-related information on social networks while they are watching TV, especially during its key moments. Therefore, this social buzz has the potential to be used as an automatic editorial criterion. Having this premise in consideration, this paper reports on the nowUP solution, a service developed with the main goal of automatically creating TV summaries of popular television programs (like football matches, talent or reality shows) based on the Twitter activity and integrating a part of that activity in the TV show summary. A data-mining engine continuously processes the activity of this social network looking for tweets associated with the TV shows. Based on the program metadata it indexes the twitter activity; correlates tweets; and creates clusters of peaks, being the relevant clusters associated with the highlights of the TV show. With this, the video engine automatically creates a full video summary (an edited sequence of TV highlights) and publishes it in an online platform and on a Catch-up TV service. The paper reports on the nowUP development and on the results of its evaluation, namely comparing its outputs with official editorial/professional video summaries. The results show that the solution was very successful in achieving the project main goal and users want to have access to this type of social buzz-based video summaries. The nowUP solution also promises potential gains in the value chain of TV producers and broadcasters.

The technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller–Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.

The number of connected devices collecting and distributing real-world information through various systems, is expected to soar in the coming years. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, but none of them have been widely adopted. In previous work we addressed this challenge, however our solution had some drawbacks: poor semantic extraction and scalability. In this paper we discuss ways to efficiently deal with representation schemes' diversity and propose a novel d-dimension organization model. Our evaluation shows that d-dimension model improves scalability and semantic extraction.

Although many environments are powered by smart solutions, users do not have a simple way to gather their collective knowledge and program devices’ behaviour. Telecommunication (Telco) providers wish to act as the facilitator, but still lack proper components for enabling integrated services over their networks, even though this work is already in active standardisation. In this work we present the architecture of a IoT solution developed in close collaboration with Telco providers, its evolution, which we call SCoT (Smart Cloud of Things), and the best practices from the several pilots we have been running. The architecture builds upon ETSI standards, combined with a rich service execution environment, providing facilitated orchestration of services and devices, as well as integrated portals for end users access.

The continuous flow of technological developments in communications and electronic industries has led to the growing expansion of the Internet of Things (IoT). By leveraging the capabilities of smart networked devices and integrating them into existing industrial, leisure and communication applications, the IoT is expected to positively impact both economy and society, reducing the gap between the physical and digital worlds. Therefore, several efforts have been dedicated to the development of networking solutions addressing the diversity of challenges associated with such a vision. In this context, the integration of Information Centric Networking (ICN) concepts into the core of IoT is a research area gaining momentum and involving both research and industry actors. The massive amount of heterogeneous devices, as well as the data they produce, is a significant challenge for a wide-scale adoption of the IoT. In this paper we propose a service discovery mechanism, based on Named Data Networking (NDN), that leverages the use of a semantic matching mechanism for achieving a flexible discovery process. The development of appropriate service discovery mechanisms enriched with semantic capabilities for understanding and processing context information is a key feature for turning raw data into useful knowledge and ensuring the interoperability among different devices and applications. We assessed the performance of our solution through the implementation and deployment of a proof-of-concept prototype. Obtained results illustrate the potential of integrating semantic and ICN mechanisms to enable a flexible service discovery in IoT scenarios.

Personal information is increasingly gathered and used for providing services tailored to user preferences, but the datasets used to provide such functionality can represent serious privacy threats if not appropriately protected. Work in privacy-preserving data publishing targeted privacy guarantees that protect against record re-identification, by making records indistinguishable, or sensitive attribute value disclosure, by introducing diversity or noise in the sensitive values. However, most approaches fail in the high-dimensional case, and the ones that don’t introduce a utility cost incompatible with tailored recommendation scenarios. This paper aims at a sensible trade-off between privacy and the benefits of tailored recommendations, in the context of privacy-preserving data publishing. We empirically demonstrate that significant privacy improvements can be achieved at a utility cost compatible with tailored recommendation scenarios, using a simple partition-based sanitization method.

The explosion in mobile data traffic is a driver for future network operator technologies, given its large potential to affect both network performance and generated revenue. The concept of distributed mobility management (DMM) has emerged in order to overcome efficiency-wise limitations in centralized mobility approaches, proposing not only the distribution of anchoring functions but also dynamic mobility activation sensitive to the applications needs. Nevertheless, there is not an acceptable solution for IP multicast in DMM environments, as the first proposals based on MLD Proxy are prone to tunnel replication problem or service disruption. We propose the application of PIM-SM in mobility entities as an alternative solution for multicast support in DMM, and introduce an architecture enabling mobile multicast listeners support over distributed anchoring frameworks in a network-efficient way. The architecture aims at providing operators with flexible options to provide multicast mobility, supporting three modes: the first one introduces basic IP multicast support in DMM; the second improves subscription time through extensions to the mobility protocol, obliterating the dependence on MLD protocol; and the third enables fast listener mobility by avoiding potentially slow multicast tree convergence latency in larger infrastructures, by benefiting from mobility tunnels. The different modes were evaluated by mathematical analysis regarding disruption time and packet loss during handoff against several parameters, total and tunneling packet delivery cost, and regarding packet and signaling overhead.

Mobile robotics is a transforming field that presents a varying set of challenges. The discussion on the autonomy of (self-powered) robots is not settled, and as the communication infrastructure evolves, centralized concepts become more attractive over distributed concepts. This paper presents the CloudThinking architecture applied to intelligent cloud-based robotic operation. CloudThinking offloads most of complex robotic tasks to a central cloud, which retrieves inputs from the environment as a whole in order to instruct the robots to perform its actions. CloudThinking is a natural approach to the orchestration of multiple specialized robotic systems, defining the best mechanisms for reaching a goal. Furthermore, this architecture provides a set of automatic features which can be useful for application developers. These features can fully exploit novel cloud tools development as it becomes available, providing a time-resilient infrastructure of easy upgrade. The resulting approach has the potential to create a different set of market for robotic application developers. © 2014 Springer Science+Business Media New York.

Pervasive environments involve the interaction of users with the objects that surround them and also other participants. In this way, pervasive communities can lead the user to participate beyond traditional pervasive spaces, enabling the cooperation among groups taking into account not only individual interests, but also the collective and social context. In this study, the authors explore the potential of using context-aware information in CSCW application in order to support collaboration in pervasive environments. In particular this paper describes the approach used in the design and development of a context-aware framework utilizing users' context information interpretation for behaviour adaptation of collaborative applications in pervasive communities.

ABSTRACT A revolution is happening that is triggered by a brave new world made by interconnected devices. The internet of things is already a reality that is creating new business opportunities across different industrial sectors. Communication service providers have a major opportunity to shape this reality by leading this transformation. For that, they need to evolve their existing architectures to enable a flexible creation of advanced services over distributed networks, interconnecting heterogeneous sensors and actuators. This paper presents an approach for telecoms to take advantage of the upcoming machine-to-machine markets and defines an advanced architecture able to withstand the demands of a new plethora of evermore clever and useful services.

Deploying smart environments often implies a plethora of co-existing devices and services, each with their own set of features, requirements and interfacing characteristics. These intricate scenarios are further exacerbated when such devices are coupled with networking capabilities, globalizing their interaction opportunities to create the so-called Internet of Things. In such interconnected heterogeneous environments, the joint operation of entities requires a flexible framework that enables and simplifies interfacing between elements. In this paper, we propose MINDiT, a framework that provides a common abstract interface towards the communication support with different entities. It incorporates cross-layer mechanisms inspired on the existing IEEE 802.21 technology, suitably modified to facilitate and optimize deployment in scenarios featuring both high-level, and low-powered network-restricted entities. MINDiT was validated through a prototype built over an open-source IEEE 802.21 implementation. We further compared its signaling impact against other solutions, and evaluated its performance over a smart environment featuring a multimedia scenario with multiple devices and services. © 2012 Elsevier B.V. All rights reserved.

The increased demand for multimedia services by mobile end users in recent years have driven both Broadcast and Wireless Network operators to develop new systems and architectures for the deployment of such services. The proposed solutions are nonetheless limited either in terms of QoS or Capabilities to deliver new interactive services. This paper highlights strengths and drawbacks of the existing technologies in terms of QoS, Security and Mobility. In order to fill the gap between current solutions we propose a new architecture that builds itself on the synergies created by a heterogeneous network made of existing delivering technologies, such as 3GPP/MBMS and DVB, where services can be delivered to end-users in the most appropriate way for end-users and operators alike. A prototype implementation is further described. © 2008 Springer Science+Business Media, LLC.

The 3GPP Multimedia Broadcast Multicast Service (MBMS) aims to introduce group communications into the 3G networks. One of the current key challenges is how to evolve these incipient features towards the "beyond 3G vision" of a converged global network where multimedia content can be delivered over one or more selected broadcast transport bearers. This paper presents potential multicast/broadcast technologies convergence and discusses the issues and challenges in moving towards this next generation network vision from the viewpoint of evolving MBMS. © 2007 Elsevier B.V. All rights reserved.

This paper addresses future scenarios for the telecommunications field, addressing the impact of three important research trends in computer networks: Context, Sensors and Wireless Networks. The proposed scenarios clearly highlight the possible synergies between the defined areas, and describe the role of Users and Network Operators in order to achieve the described goals. The potential danger of an over-encompassing network is identified, with a brief discussion on the challenges associated to the implementation of such a knowledge-aware communications network. The paper finalizes presenting a classification on the typical scenarios to be expected, and highlighting the associated challenges. It also presents a proposal on a scalable network infrastructure for Context processing. © 2008 Springer Science+Business Media, LLC.

Trends towards Next Generation Networks (NGN) are also changing the fundamental nature of TV services. Entertainment is becoming an increasingly participative experience, both with user feedback, and with user-generated content. Furthermore, the diversity of delivery mechanisms is increasing, reaching the mobile environment. The diversity of actors and technologies is now leading to a trend into IP-based content delivery, as a solution to heterogeneity management. We present an architecture for future environments, which bridges the gap between the content production and communication infrastructures, able to operate across different networks. This architecture resorts to the notion of Service Enablers deployed above evolved converged multimedia networks, based on the IP protocol. We demonstrated this architecture by laboratory prototyping. © 2008, Inderscience Publishers.

5G technologies provide several advancements regarding low latency and high bandwidth network scenarios, thus enabling new vertical use cases. Nonetheless, the lack of testing and validation mechanisms for NFV-based services poses a severe challenge in reducing their time to market. In this work we showcase a service to automate the validation of 5G NetApps, thus striving towards reducing the time to market of these applications, and introducing a new layer of trust in their reliability and availability.

NILM or Non-Intrusive Load Monitoring is the task of disag-gregating the energy consumed by a building in the energy con-sumed by its constituent appliances. With the increase in energy demand, governments started searching for solutions to reduce energy wastage on the demand side, and the deployment of smart meters was one of them. Their purpose was to give users information about the aggregated energy consumed in a given household at any given time. Since the smart meters collect the aggregated readings, the interest in NILM grew, as researchers could focus their attention on the disaggregation algorithm. Regarding the disaggre-gation algorithms, deep learning models have shown remarkable results surpassing the previous state-of-the-art models. With this in mind, this paper proposes three different deep learning models: a convolutional neural network with residual blocks, a recurrent neural network, and a multilayer perceptron that uses discrete wavelet transform as features. These models are trained on the UK-DALE and REFIT datasets and compared with the state-of-the-art models present in NILMTK-Contrib. The models are evaluated on their generalization and transfer learning ability, as these are two critical factors for a broad NILM deployment. Our models have shown competitive results compared to the state-of-the-art, achieving lower errors than 2 out of the three models used, getting closer to the performance of the third. The main advantage of our models is the ability to do real-time disaggregation, while the best model has a 30 min delay.

Network Softwarization, the process by which net- work equipment’s are being replaced by software running in a cloud environment, is playing an important role in the trans- formation of next-generation networks starting with 5G. In this environment, the use of AI/ML aided network automation to perform management tasks is vital to provide a more reliable and cost-effective network. An aspect in which AI/ML can play an important role is on the quick diagnose and track of root causes of anomalies. In this paper, we propose a system capable of automating root cause analysis in a cellular network scenario. By collecting log files from negative-impacting events occurring throughout the whole network levels, we are able to find the correlation between them according to a series of defined rules and track down the root cause using a graph-based approach to dependencies.

As 5G standards have largely matured, further enabling the radical development of 5G networks, the pressure for mobile operators to keep up with widening range of industry and contemporary use cases increases. Consequently, many approaches have emerged from the perspective of the vertical's interaction with the 5G system especially while onboarding specific vertical Network Applications (or NetApps). This work discusses an approach which reduces the complexity of this interaction by introducing a novel yet standard based vertical onboarding model. Our approach focuses on preserving interoperability and reproducibility with other systems through the exclusive utilization of data models derived by embracing well-known industry standards. Being also pragmatic, the mechanisms presented in this paper are implemented and applied in a large distributed 5G infrastructure.

A general architecture for collection and processing of water resources data, in terms of quality and quantity, is presented and discussed. The proposed architecture includes the sensing of physical and chemical water parameters, data communications, and high levels of information processing, namely machine learning. The architecture adopts an Internet of Things perspective and resulted from a survey of the most commonly measured water quality parameters, processing and data acquisition computing modules, and communications hardware and software protocols. It integrates state of the art technologies in the fields of long distance communications, software containers and blockchain technologies. Geographical information is associated with the sensor data. The top layer joins data analysis and machine learning of all the gathered information. Visualization of the raw data and of the results of the data analysis and machine learning procedures is also part of the system. The integration of weather and remote sensing data, and offline biochemical information is presented in this architecture. The architecture is supported on common commercial of the shelf components and open source software.

Reinforcement Learning has seen some interesting development over the last years, which made it very attractive to use on recommendation scenarios. In this work, we have extended the previously developed pervasive system, which is aware of the conversational context to suggest documents potentially useful to the users, with the ability to use users' click data as a way to perform better suggestions over time, through a Reinforcement Learning approach. Furthermore, to assure the real significance of these types of approaches in conversational environments, we also conducted a case study regarding the accuracy of feedback on context limited conversational systems.

The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, hand in hand with this growth goes the increasing difficulty to manage and organize all this new information. In fact, it becomes increasingly difficult to properly evaluate IoT and M2M context-aware platforms. Currently, these platforms use advanced machine learning algorithms to improve and optimize several processes. Having the ability to test them for a long time in a controlled environment is extremely important. In this paper, we discuss two distinct methods to generate a data stream from a small real-world dataset. The first model relies on first order Markov chains, while the second is based on GANs. Our preliminiar evalution shows that both achieve sufficient resolution for most real-world scenarios.

Internet of Things (IoT) solutions are becoming very popular since everything can now be processed through a technological service. Currently, data is digital information, creating the need to design platforms and services that fill the gap between data sensors and processing frameworks. IoT Platforms are responsible for attaching data sources with the remaining processing architecture. This paper presents a Machine to Machine (M2M) platform able to monitor data acquisition, processing, and visualization. The use of SCoTv2 allows users to integrate several sources and obtain relevant information only by connecting their sensors with the platform. As our preeminent goal is creating a large scale platform useful for several scenarios, a significant part of the study is related to software challenges, and the connection between technologies. Therefore, our principal contribution is the definition of effective architecture able to reply to different use cases.

The fact that most IoT solutions are provided by 3rd-parties, along with the pervasiveness of the collected data, raises privacy and security concerns. There is a need to verify which data is being sent to the 3rd-party, as well as preventing those channels from becoming an exploitation avenue. We propose to use existing API definition languages to create contracts which define the data that can be transmitted, in what format, and with which constraints. To verify the compliance with these contracts, we propose a converging "Multi-Access Edge Computing" architecture which validates RESTalike API requests/responses against a Swagger schema. We deal with encrypted traffic using an SFC-enabled Man-in-the-Middle, allowing us to do verifications in "real-time". We devised a Proof of Concept and shown that we were able to detect (and stop) contract violations.

Heating appliances such as HVAC systems consume around 48% of the energy power spent on household appliances every year. With this in mind, it is relevant to increase the efficiency of those solutions. Moreover, a malfunctioning device can increase this value even further. Thus, there is a need to develop methods that allow the identification of eventual failures before they occur. This is only achievable when services capable of analyzing data, interpret it and obtaining knowledge from it, are created. This paper presents an infrastructure that supports the inspection of failure detection in boilers, making viable to forecast faults and errors. A major part of the work is data analysis and the creation of procedures that can process it. The main goal is creating an efficient system able to identify, predict and notify the occurrence of failure events. Our fundamental contribution is the possibility to scale the system to others datasets, being able to resolve different Big Data issues.

The recent expansion of metrification on a daily basis has led to the production of massive quantities of data, which in many cases correspond to time series. To streamline the discovery and sharing of meaningful information within time series, a multitude of analysis software tools were developed. However, these tools lack appropriate mechanisms to handle massive time series data sets and large quantities of simultaneous requests, as well as suitable visual representations for annotated data. We propose a distributed, scalable, secure and high-performant architecture that allows a group of researchers to curate a mutual knowledge base deployed over a network and to annotate patterns while preventing data loss from overlapping contributions or unsanctioned changes. Analysts can share annotation projects with peers over a reactive web interface with a customizable workspace. Annotations can express meaning not only over a segment of time but also over a subset of the series that coexist in the same segment. In order to reduce visual clutter and improve readability, we propose a novel visual encoding where annotations are rendered as arcs traced only over the affected curves. The performance of the prototype under different architectural approaches was benchmarked.

Over the last few years, pervasive systems have seen some interesting development. Nevertheless, human–human interaction can also take advantage of those systems by using their ability to perceive the surrounding environment. In this work, we have developed a pervasive system – named CLASSY – that is aware of the conversational context and suggests documents potentially useful to the users based on an Information Retrieval system, and proposed a new scoring approach that uses semantics and distance based on proximity data in order to classify the relationship between tokens.

With the advent of smart IoT and M2M scenarios it becomes necessary to develop autonomous systems that optimize themselves with minimal human intervention. One possible method to achieve this is through Knee/elbow point estimation. Most of the time these points represent ideal compromises for parameters, methods and algorithms. However, estimating the knee/elbow point in curves is a challenging task. Our focus is on determining the ideal number of clusters autonomously. We analyse and discuss well-known knee/elbow estimators and two extensions based on the theoretical definition. The proposed methods (named AL and S methods) were evaluated against state-of-the-art estimators. The proposed methods are a viable stable solution for knee/elbow estimation.

The fact that most IoT solutions are provided by third parties, along with the pervasiveness of the collected data, raises privacy and security concerns. There is a need to verify which data is being sent to the third party, as well as preventing those channels from becoming an exploitation avenue. We propose to use existing API definition languages to create contracts which define the data that can be transmitted, their format and constraints. To verify the compliance with these contracts, we propose a Network Service architecture which validates REST-like API requests/responses against a Swagger schema. We deal with encrypted traffic using an Service Function Chaining (SFC)-enabled Man-in-the-Middle (MITM), allowing verifications in 'real-time.' We devised a Proof of Concept and showed that we were able to detect (and stop) contract violations.

The evergrowing number of small devices with sensing capabilities produce massive amounts of diverse data. However, it becomes increasingly difficult to manage all these new data sources. Currently, there is no single way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices become intelligent: communicate, work and learn together with minimal human intervention. Pursuing these goals, we developed methods to estimate semantic similarity based on distributional profiles. Cluster algorithms were used to learn semantic categories and improve the model accuracy. In this paper, we discuss the impact of the clustering algorithm and respective heuristic to estimate input parameters for the task of learning semantic categories. Our evaluation has shown that K-means combined with silhouette methods achieved the higher result.

The heterogeneity of 5G requirements commands more complex network architectures, imposing the need for network orchestration. ETSI NFV MANO is the standard which defines a common framework for vendors and operators to integrate their orchestration efforts. In this paper, we evaluated how an ETSI NFV MANO compliant orchestrator (OSM) fares while orchestrating an SFC-enabled SSL/TLS encrypted traffic processing architecture, which supports both edge and cloud deployments. A quantitative evaluation was carried-out, which assessed the responsiveness and overheads of OSM, as well as the actual functionality of our SSL/TLS processing architecture (with edge computing components). A qualitative evaluation was also carried-out, providing insight into the maturity of the current OSM release, what works well, what requires workarounds, and the actual limitations. A demonstration of the architecture evaluated in this work was accepted as a contribution to the ETSI OSM PoC Framework.

Estimating the knee/elbow point in curves is a challenging task. However, most of the time these points represent ideal compromises for parameters, methods and algorithms. Nowadays several IoT and M2M scenarios require autonomous systems that optimize themselves with minimal human intervention. Thus, knee/elbow estimation has become an important research area. Our focus is determining the ideal number of clusters autonomously. In this paper, we formalize the notion of knee/elbow point based on continuous curvature function and propose two theoretical methods based on the same function. We analyse and discuss well-known knee/elbow estimators and propose our own method. Contrary to most methods, ours is resilient to long tails in the curve. We also propose an iterative refinement method to increase the resilience to long heads. All the previously mentioned methods were implemented (and are publicly available) and evaluated against eight datasets. The proposed method is a viable stable solution for knee/elbow estimation.

Estimating the knee/elbow point in error curves is a challenging task. However, most of the time these points represent ideal compromises or ideal parameters for several tasks, methods and algorithms. Our focus is determining the ideal number of clusters autonomously. In this paper, we formalize the notion of knee/elbow point, discuss known methods to determine it and propose our own method. Contrary to most methods, ours is resilient to long tails in the error curve. This behaviour is especially important when considering autonomous methods. The proposed method outperformed the competition on five datasets from UCI Machine Learning Repository.

In this paper, we propose an architecture based on NFV and SDN which allows to balance traffic analysis techniques using a Classifier. It steers flows to the appropriate Service Function Chaining (to open traffic or not) according to network requirements (such as, effectiveness, flexibility, scalability, performance, and privacy). The SSL/TLS traffic processing is carried-out by the centerpiece of this work, the SFC-enabled MITM. A Proof-of-Concept was conducted (focusing on our SFC-enabled MITM) which showed that functionalities lost due to encryption (Content Optimization, Caching, Network Anti-virus, and Content Filter) were recovered when processing opened traffic within its Service Function Chains. We also evaluated its impact on performance. The results show that cipher suite overhead plays a role but can be mitigated, the Classifier can alleviate the performance overhead of different traffic analysis techniques, network functions have lower impact to performance, and Service Function Chaining length influences page load time.

The Internet of Things (IoT) is a very promising concept that by connecting numerous devices to the internet and extracting large sums of information (BigData) can enable the realisation of various futuristic scenarios. In order to develop and assess future applications and services, it is necessary the availability of datasets that can be used to train, test and cross validate. Project SCoT (Smart Cloud of Things) has developed an M2M platform capable of collecting information from heterogeneous devices and collide that information in a large data repository. During its pilot phase, the project made the assessment of the road conditions in the region of Aveiro, Portugal. In this work we make the dataset used on the previous mentioned pilot publicly available. With this dataset our road assessment algorithm reached 80% accuracy in the task of pothole detection, other scenarios (that take into account vehicular speed, position and acceleration) can also be explored. The dataset was not pre-processed in anyway, the only transformation was made to protect the identity of the volunteers.

The technological world has grown by incorporating billions of small sensing devices, collecting and sharing huge amounts of diversified data. As the number of such devices grows, it becomes increasingly difficult to manage all these new data sources. Currently there is no uniform way to represent, share, and understand IoT data, leading to information silos that hinder the realization of complex IoT/M2M scenarios. IoT/M2M scenarios will only achieve their full potential when the devices work and learn together with minimal human intervention. In this paper we discuss the limitations of current storage and analytical solutions, point the advantages of semantic approaches for context organization and extend our unsupervised model to learn word categories automatically. Our solution was evaluated against Miller–Charles dataset and a IoT semantic dataset extracted from a popular IoT platform, achieving a correlation of 0.63.

Despite many Internet of Things (IoT) Infrastructures having been implemented in recent years, none of them is truly prepared for a global deployment, where failure tolerance and scalability are an essential requirement. This article presents an alternative concept for IoT Infrastructures, which focuses on enhancing the traditional centralized architecture, usually operated by a single entity, into a decentralized architecture featuring multiple business roles. We propose a dynamic and self-configurable infrastructure on top of a structured Peer-to-Peer network. In addition, a set of communication protocols are provided in order to support heterogeneous devices, as well as data access, streaming and persistence. It is also an important focus of our proposal to have mechanisms that guarantee the privacy and security of the information flow and storage.

PASMO is a regional project aiming to bridge the gap between research and practice in the area of urban mobility support. It covers key access roads to the seaside villages of the Aveiro/Ilhavo region, in the center of Portugal, providing vehicular communications, regular WiFi access, and a myriad of useful mobility services (parking, bikes, jams, weather) under the umbrella of a smart region infrastructure dedicated to simplify the involvement of industry in testing and developing new services. The infrastructure is currently being deployed, and the services will be operational by mid 2018.

The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area. With this in mind we propose a tailored generative stream model, with two main uses: Stream similarity and generation. Sensor data can be organized based on pattern similarity, that can be estimated using the proposed model. The proposed stream model will be used in conjunction with our context organization model, in which we aim to provide an automatic organizational model without enforcing specific representations. Moreover, the model can be used to generate streams in a controlled environment. Useful for validating, evaluating and testing any platform that deals with IoT/M2M devices.

The increasing number of small, cheap devices full of sensing capabilities lead to an untapped source of information that can be explored to improve and optimize several systems. Yet, as this number grows it becomes increasingly difficult to manage and organize all this new information. The lack of a standard context representation scheme is one of the main difficulties in this research area (Antunes et al., 2016b). With this in mind we propose a stream characterization model which aims to provide the foundations of a new stream similarity metric. Complementing previous work on context organization, we aim to provide an automatic organizational model without enforcing specific representations.

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. It is our personal belief that IoT and M2M scenarios will only achieve their full potential when all the devices will work and learn together without human interaction. In this paper we review the most relevant semantic metrics and propose a new unsupervised model that minimizes sense-conflation problem. Our solution was evaluated against Miller-Charles dataset, outperforming our previous work in every metric.

In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize context information that is independent of structure and representation. However, our previous solution suffers from semantic sensitivity. In this paper we review semantic methods that can be used to minimize this issue, and propose an unsupervised semantic similarity solution that combines distributional profiles with public web services. Our solution was evaluated against Miller-Charles dataset, achieving a correlation of 0.6.

The proliferation of user-owned connected devices has brought value to mobile application developers, which can make use of locally-available sensors and capabilities and send their information to the web, centralizing the data flows. A more distributed approach would have device capabilities offered directly on the network as services hosted by the user. These pervasive user-hosted services could be made discoverable and available over a public federated service infrastructure. The infrastructure would provide transport over an identity layer, where endpoints are addressed by their identities instead of network identifiers, and on top of which services can be exposed to be consumed by trusted friends or anonymous users, as the hosting user prefers. The work presented in this paper explores the possibility of implementing such a distributed social Service-Oriented Architecture (SOA) over Extensible Messaging and Presence Protocol (XMPP). This SOA, which would expose re-usable coarse-grained software components in a service ecosystem, differs from traditional SOA because it attempts to counter the centralization existing services, in favour of a fully-distributed service ecosystem where each peer can behave both as service consumer and provider. Finally, an analysis is done on how suitable XMPP is to serve as a base protocol for such infrastructure.

Although many environments are powered by M2M solutions, users do not have a simple way to gather their collective knowledge and program devices’ behaviour. Also, Telco providers still lack proper components for enabling integrated services over their networks. We present the final architecture of the APOLLO project, which delivers a enhanced M2M platform encompassing sensors, management and applications platform for a major Telco provider. APOLLO builds on top of ETSI M2M specifications and rich service execution environments providing easy orchestration of services to end-users.

The number of connected devices is expected to soar in the coming years, each one of them collects and distributes real-world information though various systems. As the number of such connected devices grows, it becomes increasingly difficult to store and share all these new sources of information. Several context representation schemes try to standardize this information, however none of them have been widely adopted. Publish/subscribe paradigm has proven to be an adequate abstraction for large scale information dissemination, but none of current variations is well suited for context information. In a previous publication we addressed these challenges, however our solution has some drawbacks: poor scalability and semantic extraction. The aim of this paper is twofold. First, we discuss an efficient way to deal with representation schemes diversity and propose a d-dimensional context organization model. Second, we propose a semantic-based publish/subscribe system that is well suited for M2M scenarios. Our evaluation shows that d-dimensional organization model outperforms our previous solution in both speed and space requirements.

In recent years the Internet has grown by incorporating billions of small devices, collecting real-world information and distributing it though various systems. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. Several context representation schemes have tried to standardize this information, however none of them have been widely adopted. Instead of proposing yet another context representation scheme, we discuss an efficient way to deal with this diversity of representation schemes. We define the basic requirements for context storage systems, analyse context organizations models and propose a new context storage solution. Our solution implements an organizational model that improves scalability, semantic extraction and minimizes semantic ambiguity.

As the number of environmental sensors grows, it becomes increasingly difficult to manage, store and process all these sources of information. Several context representation schemes try to standardize this information, however none of them have been widely adopted. Instead of proposing yet another context representation scheme, we discuss efficient ways to deal with this diversity of representation schemes. We defined the basic requirements for flexible context storage systems, proposed an implementation and compared our implementation against two other approaches. Our solution provides more value than the remaining solutions without suffering a significant decrease in performance. © 2014 IEEE.

Many organizations rely on a heterogeneous set of applications in virtual environment to deliver critical services to their customers. Different workloads utilize system resources at different levels. Depending on the resource utilization pattern some workloads may be better suited for hosting on a virtual platform. This paper discusses a framework for benchmarking the performance of Oracle database workloads, such as Online Analytical Processing (OLAP), Online Transaction Processing (OLTP), Web load and Email on two hypervisors, namely Xen and VMware. Further, Design of Experiments (DOE) is used to identify the significance of input parameters, and their overall effect on two hypervisors, which provides a quantitative and qualitative comparative analysis to customers with high degree of accuracy to choose the right hypervisor for their workload in datacenters.

Social networks have recently emerged to become vital tools for information and content dissemination among connections. Indeed, the immense increase of the number of users of Facebook made it rise to become the largest existing social network with more than 1.2 billion active users. However, these numbers also rose the attention of hackers and attackers who aim at propagating malware and viruses for obtaining confidential information regarding social network users. In this manner, it is crucial that each Facebook user is able to easily access, control and analyse the information shared on the corresponding profile so that profile usage deviations can be more efficiently detected. However, despite the fact that Facebook allows an analysis of all user actions through the Timeline Review, this information is not comprehensively organized and there is no statistical analysis of the user generated data. In this paper, we propose a novel framework comprising a Facebook event collector, which by being provided with an authentication token for a user profile obtained through a Facebook application developed for this purpose, collects all the corresponding posted information and stores it in a relational database for a posteriori analysis. Through the graphical interface of the developed application, users can access all stored information in a comprehensible manner, according to the type of event, thus facilitating the analysis of the user's behaviour. By storing each event with the corresponding timestamp, we are able to perform an efficient and comprehensive analysis of all posted contents and compute statistical models over the obtained data. In this manner, we can create a notion of normal usage profile and detect possible deviations which may be indicative of a compromised user account.

Smart environments are physical places that are richly and invisibly populated with sensors, actuators and computational elements. The objective of such environments is to adapt themselves to its users in order to increase their comfort and usefulness. This paper proposes a platform, named APOLLO, capable of inferring behaviour rules from a smart environment and apply them to provide an intelligent space. The APOLLO platform is built upon a Service Oriented Architecture (SOA), in which collected context information is used to infer behaviour rules though statistical and machine learning techniques. The proposed platform is to be deployed in a home automation scenario. © 2013 IEEE.

Future pervasive environments will take into consideration physical and digital social relations. Nowadays it is important use to collective intelligence, where the interpretation of context information can be harnessed as input for context-aware applications, especially for group collaboration. The use of collective intelligence represents new possibilities but also new challenges in terms of collective information for adaptability and personalization in intelligent environments. This paper presents a collaborative context-aware framework focusing on social matching capabilities for session formation in collaborative activities.

Future pervasive environments will take into consideration physical and digital social relationships. Nowadays it is important to use collective intelligence, where the interpretation of context information can be harnessed as input for context-aware applications, especially for group collaboration. For collaborative applications this represents opportunities, but also new challenges in terms of using collective information for adaptability and personalization in pervasive environments. This paper presents the challenges in design and development of a context-aware framework CSCW supporting pro-behaviour capabilities in pervasive communities. © 2013 IADIS.

Future pervasive environments will take into consideration not only individual users' interest, but also social relationships. In today's scenarios, the trend is to make use of collective intelligence, where the interpretation of context information can be harnessed as input for pervasive systems. Therefore, social CSCW applications represent new challenges and possibilities in terms of use of group context information for adaptability and personalization in pervasive computing. The objective of this paper is to present two enterprise scenarios that support collaboration and adaption capabilities through pervasive communities combined with social computing. Collaborative applications integrated with pervasive communities can increase the activity's quality of the end user in a wide variety of tasks. © 2012 IEEE.

As personal information and context sharing applications gain traction more attention is drawn to the associated privacy issues. These applications address privacy using an unsatisfactory 'whitelist' approach, similar to social networks 'friends'. Some of them also link location publishing with user interaction which is also a form of privacy control - the user has to explicitly say where he is. There are a few automatic location based-services (LBS) that track the user, but without more adequate privacy protection mechanisms they enable even bigger threats to the user. On previous work, an XMPP-based Context Distribution Architecture was defined, more suitable for the distribution of frequently changing context than other systems because it is based on the publish-subscribe pattern. In this paper the authors present an extension to this architecture that allows for the introduction of a complex degree of access control in context distribution. The devised changes enable the system to consider a number of interesting context privacy settings for context distribution control. Also, this control must be enforced in a way that it doesn't interfere with the real-time nature of the distribution process. After describing the enhancements to the architecture, a prototype of the system is presented. Finally, the delivery latency and additional processing introduced by the access control components is estimated by testing it against the existing system. © 2012 IEEE.

The utilization of Media Independent Handover (MIH) mechanisms, such as the ones provided by the IEEE802.21 standard, allow the abstraction of control and information querying primitives of different wireless access technologies, using a common interface. This not only simplifies the design of high-level entities interfacing with said technologies, but by providing a transport protocol for that interface, such primitives can be used to extend controlling mechanisms towards remote entities. However, the standard only employs such flexible and abstraction capabilities towards handover optimization and facilitation scenarios. In this paper, we take advantage of the flexible design of the MIH mechanisms, and propose evolutions over their base design, allowing their integration into the areas of Internet Multimedia Optimization and the Internet of Things. © 2012 IEEE.

Experimental testbeds for evaluating solutions in computer net- works, are today required as a complement to simulation and emu- lation. As these testbeds become larger, and accessible to a broader universe of the research community, dedicated management tools become mandatory. These tools ease the complex management of the testbed specific resources, while providing an environment for researchers to define their experiments with large flexibility. While there are currently several management tools, the research commu- nity is still lacking tools that smooth the experimentation workflow. These were key aspects that we considered when developing the management infrastructure for our wireless testbed[4] (AMazING). We developed a experimentation support framework supported by an attractive GUI, automation and scripting capabilities, as well as experiment versioning and integrated result gathering and analysis.

The Internet, and networks in general, are in many ways part of our daily life. As their usage increases, their faults and limita- tions also become more apparent, motivating network researchers to evaluate and develop new solutions anticipating future scenar- ios and overcoming the challenges identified. These challenges vary across a wide range of networking areas, from traffic opti- mization in telecom operators, to scalability in wide area networks and broadband multimedia streaming, or even to the contextualiza- tion of communications in order to optimize usage. For researchers to create reliable network solutions, able to be included in our ev- ery day life, these solutions must be carefully designed, tested, and then put into a cycle of successive refinement. Currently, there are some testbed management systems available. The core of most existent solutions can be reduced to a couple of shell scripts to operate the testbed resources, without the proper support for experiment automation. Running experiments is thus possible and flexible, but cumbersome, labor intensive, and error prone. The existent solutions lack proper graphical interfaces fa- cilitating user interaction, while centralizing the whole process of configuring experiments, reserving resources, scheduling, and col- lecting its results. Furthermore, collaboration of multiple parties is vital in the current research environment. Experiment results must be shared with a closed number of peers in order to allow a better analysis and a more solid validation inside research terms.

A brave new world made of interconnected smart devices will soon revolutionize the world. The application of the Internet of Things vision will foster the creation of new businesses across different industrial sectors. Communication Service Providers need to set the pace of this change if they want to lead this transformation. They need to both leverage and evolve their architectures in order to support a flexible creation of innovative services over distributed networks, linking heterogeneous sensors and actuators. This paper presents the roles of the telecoms on the upcoming machine-to-machine markets and devises an advanced architecture able to withstand the demands of a new plethora of evermore clever and useful services.

The wide deployment of Wireless Local Area Networks (WLAN) we are witnessing today increases connectivity opportunities for mobile terminal devices, such as smartphones. However, continuous scanning for WLAN points of attachment can be a power exhausting mechanism for such battery-powered devices. These mobile devices, besides being equipped with different wireless access interfaces, are also coupled with sensors such as accelerometer, GPS, luminance and magnetic compass. In fact, sensors are increasingly being coupled into different devices and environments and are able to convey sensing information through networks into decision entities able to optimize different processes. In this paper we propose a framework where media independent sensing information is used to enhance wireless link management towards energy-efficiency. This framework enables the dissemination of sensing information towards local and remote decision entities, enhancing other processes (e.g. mobility) with sensing information in order to provide true Ambient Intelligence scenarios. We introduce this framework into a wireless management scenario able to provide energy-efficient optimal network connectivity. © 2011 IEEE.

We describe a wireless testbed composed by 24 wireless nodes that can be used to perform a broad range of studies in the area of next generation networks. This paper addresses the difficulties and constrains faced by the authors throughout the deployment process of such testbed. Flexibility and controllability were key concerns driving the testbed design. The testbed can be remotely managed through a series of remotely accessible web services performing low-level management. Validation results are presented, showing the interference levels of the testbed as well as its maximum throughput capabilities. © Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2011.

The evolution of telecommunications has allowed the coupling of networking features to an ever-increasing plethora of devices, enabling their connectivity and berthing of an Internet of Things (IoT). However, the assorted nature of such “things” implies disparate specifications, requirements and interfacing, for interaction with high-level decision modules, services and agents involved in the most distinct kind of scenarios. The joint operation of all networkable things in a truly integrated IoT vision requires a flexible framework able to facilitate interfacing with different elements and concepts. In this paper we present a framework that builds over the concept of Media Independence, further enhancing it to encompass the abstraction of different “things”, by providing the means for accessing their different interfacing procedures. We highlight the feasibility and flexibility of this framework by introducing it into an IoT scenario, enabling a flexible way for interface retrieval and control of different entities, paving the way for a truly universal and dynamic connectivity vision.

The integration of the digital world into every aspect of our lives is becoming more and more preeminent. Nowadays, not only humans but also devices and the environments where we dwell are also able to provide their own information, through the usage of sensors. Through their interconnection over communication networks, sensors enable Ambient Intelligent scenarios achieving a true Internet of Things, and crossing the gap between computer systems and the real world. However, the different sensing devices and information generated by them, are of such disparate nature that it becomes increasingly difficult to access and make use of such heterogeneous data. In this paper, we propose a framework which not only considers a media independent information access to sensors and their supplied data, but is also flexible enough to configure and provide different means to best transport collected sensor information via wireless gateways, facilitating its deployment in different scenarios. We demonstrate its usefulness by providing a throughput efficient approach towards the collection and dissemination of sensing data, through sensing information compression and aggregation of multiple sources. © 2011 IEEE.

In recent years several authors and projects have addressed the need for an architecture for collecting and managing context information. Most of the approaches have either been purely based in middlewares unable to address the heterogeneity of components and services platforms, or have been built around Web based technologies that are unable to cope with the realtime and reactiveness requirements that many context-aware use cases have. In this paper the authors propose a new Context Management Architecture able to solve these problems. Furthermore, this architecture, exploring the XMPP protocol, can be used as the basis for new developments in this area. ©2010 IEEE.

This paper analyzes the applicability of context-based multicast content distribution (CBMCD) on the example of realistic push- and video-based mobile advertising services at a shopping centre. The technical results of the simulation of the service scenario show that CBMCD significantly reduces the number of unicast streams and the total volume of traffic in the network. The results of the financial analysis show that these technical benefits can be translated into considerable financial benefits due to costs savings. Taken together, these results suggest that CBMCD can be an efficient, cost-saving network traffic management approach and the basis for lucrative push services. © 2010 Springer-Verlag Berlin Heidelberg.

This paper presents a context-aware content distribution architecture based on both a generic context framework and a service platform composed of smart service enablers which are reused by different applications. A general description of the architecture components is presented emphasising the context framework, its main features and functionalities. To prove the proposed concepts and evaluate the architecture three applications have been developed targeting different types of content and usage scenarios in Public Smart Spaces.

The 3GPP consortium proposed in the release 7 of the IP Multimedia Subsystem (IMS) a Diameter interface for the resource admission communication process replacing the previous COPS solution. Although both academic and industry communities have deeply debate the advantages and disadvantages of each protocol, its impact in NGN may have not been thoroughly quantified. This paper compares both protocols in terms of messages exchanged between network entities, and of bandwidth requirements during the admission control process. Based on general network operator environment characteristics, we present several exploitation scenarios where it is analyzed the scalability and adequacy of each protocol. © 2009 IEEE.

In the last 3 years a medium size NGN testbed was deployed at the Instituto de Telecomunicações of Aveiro in order to support the validation of the project IST-Daidalos. This paper intends to describe not only the work done in order to setup and maintain this testbed but also to elucidate the community of some of the caveats of creating and managing such a testbed in a environment where most of the prototypes are under specification and requirements change quickly. In this paper we also present some of the methodology used in order to extract results from this testbed and an insight to what can be done in the future.

Multimedia group communication over IP is an advanced service gaining growing interest from broadcast, fixed and mobile network operators. Next Generation Networks (NGN) will require that IP based multimedia services to be efficiently delivered to groups over heterogeneous networks with QoS. Although being acknowledged as the most efficient way of delivering group communication in packet switched networks, IP multicast still faces some problems associated with QoS that have limited its success in the past. Furthermore the nonexistence of true multicast in wireless technologies has led to an inefficient mapping between the IP and wireless layers. This paper proposes a technique for efficient dynamic mapping of IP Multicast, optimizing its usage for NGN. This technique is evaluated by a prototype on a WiFi environment. © 2007 IEEE.

II NTRODUCTION Next generation wireless communication systems will handle diverse types of services, across different types of access technologies. This trend, already present in 3G networks and in the current explosion of

With the rapid advances in multimedia content delivery technologies and deployment of user-centric broadband infrastructures, there is an increasing challenge for efficient usage of the Internet resources and enhanced Quality of Service (QoS) support based on multicast. This paper discusses an architectural approach for provision of multicast services for content delivery on demand in the framework of converged fixed / mobile / broadcast infrastructures based on heterogeneous mobile IPv6 environment. Addressed issues include support of multicast transport protocols and mechanisms at access routers for different application requirements (reliable, streaming and real-time) considering asynchronous service requests and caching, QoS management of multicast transport for content delivery, seamless handover and selection of optimal access routers for multicast services. The concepts are based on the current state and possible enhancements of the mobile architecture developed in the European Community (EU) IST project DAIDALOS.

The wireless digital lifestyle comes to the expense of less privacy and security. This environment is prone to be monitored by rogue users, eager to learn from our lifestyle habits and use them for their own profit. The IP protocol provides very few mechanisms, in order to safeguard user privacy and impair efficient data-mining of user habits. This paper will address an identity architecture that makes use of both data (L2) and network (L3) layer identifiers in order to provide a pseudonimization function, based on virtual hoarding concepts. Our proposal is especially interesting when able to exploit the broadcast and promiscuous nature of wireless communications that usually is regarded as a security concern. A prototype implementation has been developed and tested. ©2006 IEEE.

Next generation wireless communication systems aim to handle diverse types of services across different types of access technologies in a seamless way. This paper proposes a next generation network architecture and evaluates possible associated signaling strategies, focusing in network-level QoS support aspects. The scenarios handled cover terminal-initiated signaling, network controlled signaling, and application-provider controlled signaling. Possible message sequence charts associated with these scenarios are presented and discussed. The paper compares the relative merits of each approach and concludes that the optimum QoS signaling solution depends on the QoS models that will be used, which are directly related to the business models chosen by the operators.

In a globally connected world filled with rich multimedia services, mobility means making the kind of services utilized independent of the access technology used. The current dependence of Technologies and Services acts as a barrier for the penetration of new markets with new products and services. Broadcast technologies such as DVB and DMB are being deployed over the world. New mobile terminals that carry two interfaces are now able to additionally connect to a broadcast network. Though these broadcast networks provide TV-like services, they lack flexibility and personalization. This paper shows how the Daidalos project has taken up these trends in terms of Mobility and Services Provisioning in a Heterogeneous Network Operator Environment towards a solution that integrates mobility and broadcast.

This paper describes the QoS architecture and the corresponding QoS signalling protocols to be developed inside the IST project Daidalos. We address the main results achieved in terms of the definition of the QoS components and its interfaces, the description of the application and network services, definition of the signalling scenarios for the integration of the QoS signalling with the application signalling and with mobility approaches, and specification of the intra- and inter- domain QoS control approaches. We also describe the QoS management of the system, through the Policy--based Management System, and a Real-time Network Monitoring system able to aid in admission control with the results of active and passive measurements. All the elements, interfaces and functionalities take into account multicast services and inherent broadcast networks.

Next generation communication systems aim to handle diverse types of services across different types of access technologies in a seamless way. These future mobile networks, usually denoted as 4G networks, will integrate also broadcast networks, such as the TV/Radio networks. This paper presents a future 4G network architecture, based on IPv6, capable of supporting a broad range of multicast services, including multimedia services, like Video Streaming over IP networks, in addition to traditional Internet and Voice services. A architectural key first companion paper presented the elements. This paper proposes signalling scenarios for the set-up of multicast and broadcast sessions with QoS support in intra- and inter-domain scenarios, also addressing the mechanisms for the integration of current Broadcast networks in the architecture, describing a strategy that can be used to support technology.

Next generation communication systems aim to handle diverse types of services across different types of access technologies in a seamless way. These future mobile networks, usually denoted as 4G networks, will integrate also broadcast networks, such as the TV/Radio networks. This paper presents a future 4G network architecture, based on IPv6, capable of supporting a broad range of multicast services, including multimedia services, like Video Streaming over IP networks, in addition to traditional Internet and Voice services. This first part presents the key architectural elements. A second companion paper presents the signalling mechanisms required for the support of inherent broadcast networks in this architecture.

This article presents results of an IP-based architecture for heterogeneous environments, covering UMTS-like TD-CDMA wireless access technology, wireless and wired LANs, that has been developed under

The increasing usage of multiple signalling mechanisms, with associated QoS extensions, creates several problems to commercial data networks. New and scalable approaches are required for the network operator to support this diversity. This paper discusses a highly flexible, scalable architecture for processing QoS Admission Control in public networks. The architecture relies on the cooperation of two different entities, an agent and a manager, with fully distributed implementation, and able to perform the required signalling, authorization, and admission control decisions. If required, the agent is capable of interfacing with different signalling mechanisms. Early implementation conclusions are also presented. This architecture is capable of operating with multiple QoS frameworks, with minimal added overhead. © Springer-Verlag 2004.

The development of robots capable of accepting instructions in terms of familiar concepts to the user is still a challenge. For these robots to emerge it?s essential the development of natural language interfaces, since this is regarded as the only interface acceptable for a machine which expected to have a high level of interactivity with Man. Our group has been involved for several years in the development of a mobile intelligent robot, named Carl, designed having in mind such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an explicit concern with the integration of the major dimensions of intelligence, namely Communication, Action, Reasoning and Learning. This paper focuses on the multi-modal human-robot language communication capabilities of Carl, since these have been significantly improved during the last year.

This paper describes the architecture and current capabilities of Carl, a prototype of an intelligent service robot, designed having in mind such tasks as serving food in a reception or acting as a host in an organization. The approach that has been followed in the design of Carl is based on an explicit concern with the integration of the major dimensions of intelligence, namely communication, action, reasoning and learning. The paper focuses on the multi-modal human-robot communication capabilities of Carl, since these have been significantly improved during the last year.