Pervasive technology and public transport : opportunities beyond telematics

The advancements of technology in the field of public transport have been considerable. Information Technology (IT) has made the dissemination of information effortless, contributing to reduced perceived waiting time, increased sense of security, and value for money. Nevertheless, and in light of the ever more obvious widespread presence of powerful mobile devices, it seems that the use of technology may be geared towards supplementary services other than telematics. Looking at it from a passenger’s perspective, this article provides an overview of what IT-based services are currently offered in public transport and what is their assessed impact. We finalise by putting forward possible directions that future services might follow, and stress out the necessity to come up with frameworks that enable for the impact assessment on service quality and customer satisfaction.

A new algorithm for tracking people in a pervasive mining environment

This paper analyze and study a pervasive computing system in a mining environment to track people based on RFID (radio frequency identification) technology. In first instance, we explain the RFID fundamentals and the LANDMARC (location identification based on dynamic active RFID calibration) algorithm, then we present the proposed algorithm combining LANDMARC and trilateration technique to collect the coordinates of the people inside the mine, next we generalize a pervasive computing system that can be implemented in mining, and finally we show the results and conclusions.

Determining service trustworthiness in inter loud computing environments

Deployment of applications and scientific workflows that require resources from multiple distributed platforms are fuelling the federation of autonomous clouds to create cyber infrastructure environments. As the scope of federated cloud computing enlarges to ubiquitous and pervasive computing, there will be a need to assess and maintain the trustworthiness of the cloud computing entities. In this paper, we present a fully distributed framework that enable interested parties determine the trustworthiness of federated cloud computing entities.

Human-computer interaction in ubiquitous computing environments

Purpose – The purpose of this paper is to explore characteristics of human-computer interaction when the human body and its movements become input for interaction and interface control in pervasive computing settings.

Design/methodology/approach – The paper quantifies the performance of human movement based on Fitt's Law and discusses some of the human factors and technical considerations that arise in trying to use human body movements as an input medium.

Findings – The paper finds that new interaction technologies utilising human movements may provide more flexible, naturalistic interfaces and support the ubiquitous or pervasive computing paradigm.

Practical implications – In pervasive computing environments the challenge is to create intuitive and user-friendly interfaces. Application domains that may utilize human body movements as input are surveyed here and the paper addresses issues such as culture, privacy, security and ethics raised by movement of a user's body-based interaction styles.

Originality/value – The paper describes the utilization of human body movements as input for interaction and interface control in pervasive computing settings.

Establishing trust in hybrid cloud computing environments

Establishing trust for resource sharing and collaboration has become an important issue in distributed computing environment. In this paper, we investigate the problem of establishing trust in hybrid cloud computing environments. As the scope of federated cloud computing enlarges to ubiquitous and pervasive computing, there will be a need to assess and maintain the trustworthiness of the cloud computing entities. We present a fully distributed framework that enable trust-based cloud customer and cloud service provider interactions. The framework aids a service consumer in assigning an appropriate weight to the feedback of different raters regarding a prospective service provider. Based on the framework, we developed a mechanism for controlling falsified feedback ratings from iteratively exerting trust level contamination due to falsified feedback ratings. The experimental analysis shows that the proposed framework successfully dilutes the effects of falsified feedback ratings, thereby facilitating accurate and fair assessment of the service reputations.

Bayesian nonparametric extraction of hidden contexts from pervasive honest signals

Hidden patterns and contexts play an important part in intelligent pervasive systems. Most of the existing works have focused on simple forms of contexts derived directly from raw signals. High-level constructs and patterns have been largely neglected or remained under-explored in pervasive computing, mainly due to the growing complexity over time and the lack of efficient principal methods to extract them. Traditional parametric modeling approaches from machine learning find it difficult to discover new, unseen patterns and contexts arising from continuous growth of data streams due to its practice of training-then-prediction paradigm. In this work, we propose to apply Bayesian nonparametric models as a systematic and rigorous paradigm to continuously learn hidden patterns and contexts from raw social signals to provide basic building blocks for context-aware applications. Bayesian nonparametric models allow the model complexity to grow with data, fitting naturally to several problems encountered in pervasive computing. Under this framework, we use nonparametric prior distributions to model the data generative process, which helps towards learning the number of latent patterns automatically, adapting to changes in data and discovering never-seen-before patterns, contexts and activities. The proposed methods are agnostic to data types, however our work shall demonstrate to two types of signals: accelerometer activity data and Bluetooth proximal data. © 2014 IEEE.

A shadow-like task migration model based on context semantics for mobile and pervasive environments

Pervasive computing is a user-centric mobile computing paradigm, in which tasks should be migrated over different platforms in a shadow-like way when users move around. In this paper, we propose a context-sensitive task migration model that recovers program states and rebinds resources for task migrations based on context semantics through inserting resource description and state description sections in source programs. Based on our model, we design and develop a task migration framework xMozart which extends the Mozart platform in terms of context awareness. Our approach can recover task states and rebind resources in the context-aware way, as well as support multi- modality I/O interactions. The extensive experiments demonstrate that our approach can migrate tasks by resuming them from the last broken points like shadows moving along with the users.

SECC: simultaneous extraction of context and community from pervasive signals

Understanding user contexts and group structures plays a central role in pervasive computing. These contexts and community structures are complex to mine from data collected in the wild due to the unprecedented growth of data, noise, uncertainties and complexities. Typical existing approaches would first extract the latent patterns to explain the human dynamics or behaviors and then use them as the way to consistently formulate numerical representations for community detection, often via a clustering method. While being able to capture high-order and complex representations, these two steps are performed separately. More importantly, they face a fundamental difficulty in determining the correct number of latent patterns and communities. This paper presents an approach that seamlessly addresses these challenges to simultaneously discover latent patterns and communities in a unified Bayesian nonparametric framework. Our Simultaneous Extraction of Context and Community (SECC) model roots in the nested Dirichlet process theory which allows nested structure to be built to explain data at multiple levels. We demonstrate our framework on three public datasets where the advantages of the proposed approach are validated.

Pervasive multimedia guidance system for special needs passengers on public transport

[EN] This article presents a practical case of a pervasive multimedia guidance system for public transport passengers. In order to provide useful information to passengers, the system is capable of operating and adapting spontaneously to the different parts of a public transport network, using local data communication technologies. The multimedia data provided by the system are highly accessible, and adapt to the passengers' preferences, and are consequently suitable for special needs passengers. To this end, a paradigm of pervasive computing has been applied.

Semantic-based middleware solutions to support context-aware service provisioning in pervasive environments

The dynamicity and heterogeneity that characterize pervasive environments raise new challenges in the design of mobile middleware. Pervasive environments are characterized by a significant degree of heterogeneity, variability, and dynamicity that conventional middleware solutions are not able to adequately manage. Originally designed for use in a relatively static context, such middleware systems tend to hide low-level details to provide applications with a transparent view on the underlying execution platform. In mobile environments, however, the context is extremely dynamic and cannot be managed by a priori assumptions. Novel middleware should therefore support mobile computing applications in the task of adapting their behavior to frequent changes in the execution context, that is, it should become context-aware. In particular, this thesis has identified the following key requirements for novel context-aware middleware that existing solutions do not fulfil yet. (i) Middleware solutions should support interoperability between possibly unknown entities by providing expressive representation models that allow to describe interacting entities, their operating conditions and the surrounding world, i.e., their context, according to an unambiguous semantics. (ii) Middleware solutions should support distributed applications in the task of reconfiguring and adapting their behavior/results to ongoing context changes. (iii) Context-aware middleware support should be deployed on heterogeneous devices under variable operating conditions, such as different user needs, application requirements, available connectivity and device computational capabilities, as well as changing environmental conditions. Our main claim is that the adoption of semantic metadata to represent context information and context-dependent adaptation strategies allows to build context-aware middleware suitable for all dynamically available portable devices. Semantic metadata provide powerful knowledge representation means to model even complex context information, and allow to perform automated reasoning to infer additional and/or more complex knowledge from available context data. In addition, we suggest that, by adopting proper configuration and deployment strategies, semantic support features can be provided to differentiated users and devices according to their specific needs and current context. This thesis has investigated novel design guidelines and implementation options for semantic-based context-aware middleware solutions targeted to pervasive environments. These guidelines have been applied to different application areas within pervasive computing that would particularly benefit from the exploitation of context. Common to all applications is the key role of context in enabling mobile users to personalize applications based on their needs and current situation. The main contributions of this thesis are (i) the definition of a metadata model to represent and reason about context, (ii) the definition of a model for the design and development of context-aware middleware based on semantic metadata, (iii) the design of three novel middleware architectures and the development of a prototypal implementation for each of these architectures, and (iv) the proposal of a viable approach to portability issues raised by the adoption of semantic support services in pervasive applications.

Integration of Internet of Things and Cloud computing. A case study on vital signs monitoring.

Il progresso scientifico e le innovazioni tecnologiche nei campi dell'elettronica, informatica e telecomunicazioni, stanno aprendo la strada a nuove visioni e concetti. L'obiettivo della tesi è quello d'introdurre il modello del Cloud computing per rendere possibile l'attuale visione di Internet of Thing. Nel primo capitolo si introduce Ubiquitous computing come un nuovo modo di vedere i computer, cercando di fare chiarezza sulla sua definizione, la sua nascita e fornendo un breve quadro storico. Nel secondo capitolo viene presentata la visione di Internet of Thing (Internet delle “cose”) che si avvale di concetti e di problematiche in parte già considerate con Ubiquitous computing. Internet of Thing è una visione in cui la rete Internet viene estesa agli oggetti di tutti i giorni. Tracciare la posizione degli oggetti, monitorare pazienti da remoto, rilevare dati ambientali sono solo alcuni esempi. Per realizzare questo tipo di applicazioni le tecnologie wireless sono da considerare necessarie, sebbene questa visione non assuma nessuna specifica tecnologia di comunicazione. Inoltre, anche schede di sviluppo possono agevolare la prototipazione di tali applicazioni. Nel terzo capitolo si presenta Cloud computing come modello di business per utilizzare su richiesta risorse computazionali. Nel capitolo, vengono inizialmente descritte le caratteristiche principali e i vari tipi di modelli di servizio, poi viene argomentato il ruolo che i servizi di Cloud hanno per Internet of Thing. Questo modello permette di accelerare lo sviluppo e la distribuzione di applicazioni di Internet of Thing, mettendo a disposizione capacità di storage e di calcolo per l'elaborazione distribuita dell'enorme quantità di dati prodotta da sensori e dispositivi vari. Infine, nell'ultimo capitolo viene considerato, come esempio pratico, l'integrazione di tecnologie di Cloud computing in una applicazione IoT. Il caso di studio riguarda il monitoraggio remoto dei parametri vitali, considerando Raspberry Pi e la piattaforma e-Health sviluppata da Cooking Hacks per lo sviluppo di un sistema embedded, e utilizzando PubNub come servizio di Cloud per distribuire i dati ottenuti dai sensori. Il caso di studio metterà in evidenza sia i vantaggi sia le eventuali problematiche che possono scaturire utilizzando servizi di Cloud in applicazioni IoT.

Granular computing as a tool of human data interaction - a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.

Confidence: dependencies and their critical role in fostering user acceptance in pervasive applications

Pervasive computing offers new scenarios where users are surrounded by invisible and proactive technology making smart spaces. Although the utility and power of solutions developed using this computer paradigm are proved, there are unresolved problems that hinder their acceptance and inclusion in our private life. Users have problems understanding the operations of a pervasive computing solution, and therefore they should trust that the solution works properly and according to their expectations. Nevertheless, the concept of trust is already framed in a specific use within the ecosystem of applications that can populate a smart space. To take this concept of trust to the whole space, we propose to study and define the concept of confidence. In contrast to the concept of trust, confidence has deeper psychological implications.

Granular computing as a basis of human–data interaction: a cognitive cities use case

The article proposes granular computing as a theoretical, formal and methodological basis for the newly emerging research field of human–data interaction (HDI). We argue that the ability to represent and reason with information granules is a prerequisite for data legibility. As such, it allows for extending the research agenda of HDI to encompass the topic of collective intelligence amplification, which is seen as an opportunity of today’s increasingly pervasive computing environments. As an example of collective intelligence amplification in HDI, we introduce a collaborative urban planning use case in a cognitive city environment and show how an iterative process of user input and human-oriented automated data processing can support collective decision making. As a basis for automated human-oriented data processing, we use the spatial granular calculus of granular geometry.

Vertical handover based adaptions for multimedia applications in pervasive systems

This paper proposes an architecture for pervasive computing which utilizes context information to provide adaptations based on vertical handovers (handovers between heterogeneous networks) while supporting application Quality of Service (QoS). The future of mobile computing will see an increase in ubiquitous network connectivity which allows users to roam freely between heterogeneous networks. One of the requirements for pervasive computing is to adapt computing applications or their environment if current applications can no longer be provided with the requested QoS. One of possible adaptations is a vertical handover to a different network. Vertical handover operations include changing network interfaces on a single device or changes between different devices. Such handovers should be performed with minimal user distraction and minimal violation of communication QoS for user applications. The solution utilises context information regarding user devices, user location, application requirements, and network environment. The paper shows how vertical handover adaptations are incorporated into the whole infrastructure of a pervasive system