Research In Pervasive Computing Research Articles

From UbiComp to Universe—Moving Pervasive Computing Research Into Space Applications

Humanity's burgeoning crewed and uncrewed presence in space is creating increasing opportunity for ideas and approaches gestated for terrestrial use to be adapted and deployed in space applications. To illustrate this from the perspective of the Pervasive Community, this article overviews a selection of recent and ongoing space-oriented projects in the MIT Media Lab's Responsive Environments Group, and chronicles the roots that most of them had in our prior Pervasive Computing research program. These projects involve wearables, smart fabrics, sensor networks, cross-reality systems, pervasive/reactive displays, microrobots, responsive space habitat interiors, and self-assembling systems for in-space infrastructure. Many of them have been tested in zero-gravity and suborbital flights, on the International Space Station, or will be deployed during an upcoming lunar mission. Assessed together, this portfolio of work points forward to the broad role that some of the tenets of Pervasive Computing (e.g., novel sensing technologies, “smart materials”, and best-in-class modern HCI infrastructure) will play in our near-term space future. This work marks an important inflection point in the space industry, where academic research experiments are rapidly maturing—on the scale of months, not years—to influence the products, tools, and human experiences in low earth orbit and beyond.

A Retrospective and a Look Forward: Lessons Learned From Researching Emotions In-the-Wild

Emotions have a significant impact on our decision-making, learning, awareness, social interactions, and mental and physical health. Even though large efforts have been put into quantifying human emotions, their subjectivity, context-dependence, and complexity render them as being almost unpredictable. However, while different streams of research in pervasive computing and psychology have made significant progress in the quantification of emotions, the most successful research results come out of controlled laboratory studies. In this article, we present a retrospective of a series of in-the-wild studies through the lens of human emotions. We are looking at the weaknesses and strengths of traditional research methods and present lessons learned. We furthermore call for a readjustment of research rigor and describe potential new research designs specific to out-of-the-lab studies.

A Survey on Botnets: Incentives, Evolution, Detection and Current Trends

Botnets, groups of malware-infected hosts controlled by malicious actors, have gained prominence in an era of pervasive computing and the Internet of Things. Botnets have shown a capacity to perform substantial damage through distributed denial-of-service attacks, information theft, spam and malware propagation. In this paper, a systematic literature review on botnets is presented to the reader in order to obtain an understanding of the incentives, evolution, detection, mitigation and current trends within the field of botnet research in pervasive computing. The literature review focuses particularly on the topic of botnet detection and the proposed solutions to mitigate the threat of botnets in system security. Botnet detection and mitigation mechanisms are categorised and briefly described to allow for an easy overview of the many proposed solutions. The paper also summarises the findings to identify current challenges and trends within research to help identify improvements for further botnet mitigation research.

Beyond position-awareness—Extending a self-adaptive fall detection system

Ambient Assisted Living using mobile device sensors is an active area of research in pervasive computing. Multiple approaches have shown that wearable sensors perform very well and distinguish falls reliably from Activities of Daily Living. However, these systems are tested in a controlled environment and are optimized for a given set of sensor types, sensor positions, and subjects. We propose a self-adaptive pervasive fall detection approach that is robust to the heterogeneity of real life situations. Using the data of four publicly available datasets, we show that our system is not only robust regarding the different dimensions of heterogeneity, but also adapts autonomously to spontaneous changes in the sensor’s position at runtime. In this paper, we extend our self-adaptive fall detection system with (i) additional algorithms for fall detection, (ii) an approach for cross-positional sensor fusion, (iii) a fall detection approach that relies on outlier detection, and (iv) a smart fall alert. Additionally, we present implementation and evaluation of these extensions.

Spontaneous Service-Providing using WS4D in Smart Environments

Pervasive environments are characterized by a large number of embedded devices offering their services to the user. Which of the available services are of most interest to the user considerably depends on the user's current context. Spontaneous service discovery and selection is one of the most important fields of research in pervasive computing. In this paper the authors will present an enhancement of ubiquitous computing discovery mechanisms adding context handling capabilities to Web Services for Devices in Pervasive Computing using UPnP as an infrastructure to address these implicit requests. User preferences, network and location are described by a formal context model ontology that is based on two levels: a generic level and a domain specific level. As compared with previous research, the authors' method uses location aware, UPnP infrastructure, web service for devices and the notion of proactivity in pervasive computing to continuously present the Spontaneous most relevant services to the user or device in response to changes of context, services or user preferences.

Experimental Methodology in Pervasive Computing

Pervasive computing sits at the interface of computer science, social sciences, psychology, and engineering. As a consequence, consistent standards and guidelines for empirical are elusive. Thus, in most key conferences and journals in the field (including IEEE Pervasive Computing), lack of adequate evaluation is the most common reason for rejecting a submission. At the same time, the evaluation's quality is often the subject of heated discussion among reviewers and program committee members. IEEE Pervasive Computing's Experimental Methodology department will look at specific problems, practices, and recommendations related to empirical research in pervasive computing. The department is motivated by the increasing awareness that the field needs to mature, moving from visions of what could be done toward real-world systems that quantitatively prove what can be done in a reproducible, objective way.

Relational Attribute Integrated Matching Analysis (RAIMA): A Framework for the Design of Self-Adaptive Egocentric Social Networks

An emerging research in pervasive computing is the inference of social context to facilitate and mediate communications among proximate people. Understanding users' needs through information reasoning and leveraging principles of social networks play an important role in the emergence of innovative computer-mediated social networks. This paper introduces a generic social networking framework for the analysis and visualization of mobile and spontaneous social networks. The proposed framework is capable of analyzing social scores in order to provide decision support to users in the form of egocentric social graphs. As part of the framework, we introduce a matching algorithm that its efficiency is compared to commonly used “Stable Marriage Matching” algorithms in opportunistic social networks. We show the performance of the algorithm as social profile attributes increase in a network.

Pervasive Socio-Technical Fabric

In 2011, now 20 years after M. Weiser's “The Computer for the 21st Century” (1991), the vision impacting the evolution of Pervasive Computing is still the claim for an intuitive, unobtrusive and distraction free interaction with omnipresent, technology-rich environments. In an attempt of bringing interaction “back to the real world” after an era of keyboard and screen interaction (Personal computing), computers are being understood as secondary artefacts, embedded and operating in the background, whereas the set of all physical objects present in the environment are understood as the primary artefacts, the “interface”. Over it's more than two decades of evolution, the field has been undergoing three generations of research challenges fertilizing Pervasive Computing: The first generation aiming towards autonomic systems and their adaptation was driven by the availability of technology to connect literally everything to everything (Connectedness, 1991-2005). The second generation inherited from the upcoming context recognition and knowledge processing technologies (Awareness, 2000-2007), e.g. context-awareness, self-awareness, resource-awareness, etc. Finally, a third generation, building upon connectedness and awareness, attempts to exploit the (ontological) semantics of Pervasive Computing systems, services and interactions (i.e. giving meaning to situations and actions, and “intelligence” to systems) (Smartness, 2004-). While Pervasive Computing research has its success in the first, partly also in the second generation, the third generation is evolving as we speak.The FP7 FET proactive project PANORAMA (FET proactive/Goal 8.3: Pervasive Adaptation) picked up on the challenge of identifying the new trails of Pervasive Computing research, involving some 240 of the most distinguished researchers in the field in a solicitation process that lasted for about three years. The result of this process is manifested in the Pervasive Adaptation Research Agenda Book (www.perada.eu/research-agenda), which is presented in this article and the respective fett11 session.

Recognize contextual situation in pervasive environments using process mining techniques

Research in pervasive computing and ambience intelligence aims to enable users to interact with the environment in a context-aware way. To achieve this, a complex set of features describing different aspects of the environment has to be captured and processed; in other words situation-awareness is needed. This article notes uniquely three points when modelling situations. Firstly, unlike most existing approaches, context information history should be considered when modelling the situations. We argue here that the current state cannot be understood in isolation from the previous states. Secondly, in order to track user’s behaviour there is a need to consider the context information available in the different domains the user visits. Thirdly, to identify situations it can be problematic to define situation patterns and looking for an exact match as most of the approaches does. We found that the combination of the flexibility of the user behaviour and automated capture of context events provide a very effective solution for contextual situation recognition. In this article we first provide a formalization of the situation recognition problem and then we focus on the potential use of process mining techniques for measuring situation alignment, i.e., comparing the real situations of users with the expected situations. To this end, we propose two ways to create and/or maintain the fit between them: linear temporal logic (LTL) analysis and conformance testing. We evaluate the effectiveness of the framework using a third party published smart home dataset. Our experiments prove the effectiveness of applying the proposed approach to recognizing situations in the flow of context information.

On the application of epidemical spreading in collaborative context-aware computing

Pervasive Computing Research Group, Department of Informatics and Telecommunications, University of Athens, Panepistimiopolis, Ilissia, 15784, Athens, Greece. We investigate the very important concept of collaborative context awareness. Applications operating in nodes with spatial proximity and network connectivity exchange the contextual information (context) captured by their sensors (or other similar data sources). Such scheme improves the characteristics of the context aware application (e.g., robustness). The exchange of context in our model is progressed beyond a simple flooding scheme. We adopt an epidemic model, which relays information in a stochastic manner. In our model, nodes have a reasoning capability, which can further process and transform the incoming context. The derived context can be relayed throughout the network. This multi-epidemic model relies on semantic dependencies, which are modeled through a hierarchical representation scheme. We investigate the efficiency of the proposed architecture in light of different spreading parameters. Our simulations take into account the temporal validity of the exchanged context (i.e., relayed information remains valid for a certain period of time), the status of the sensing elements and the mobility of nodes. Our findings are quite promising for the emerging area of context aware application engineering.

Multi-agent smart environments

Research in pervasive computing and ambient intelligence has catapulted smart environments to the forefront of many research labs. While tremendous strides have been made toward realizing the dream of designing environments that act as intelligent agents, the size of the problem creates issues that still need to be addressed by researchers. One such issue is the role of multi-agent systems in smart environments. In this article we discuss the role of multi-agent research in the context of smart environments and survey current research in the area. In addition, we pose several challenges to be tackled in this dynamic and complex area of research.