On-line social networking and communication services are increasingly popular methods to communicate with friends, family and communities. Statistics shows that users of services like Facebook and Twitter stretches across geographical locations, professions, age groups and habits. Smart mobile devices with Internet connectivity simplifies access to these services at anytime and from almost anywhere. However, the huge amount of user-generated content makes it difficult to identify useful information. A challenge is to create micro-communities where users may join in from heterogeneous social networks using proper user and identity management. The increasing number of social networks and communication services are also creating new challenges in social media content filtering, micro-community discovery, automatic group communication initialization.This licentiate thesis proposes to utilize social graphs for improving group communication. It therefore presents a framework that manages information harnessed from social-networking services and personal devices such as mobile phones and laptops. The framework can identify individual communication patterns and use these to calculate a social strength between users expressed as a weighted social graph.The central component of the framework is a social recommendation engine for social content filtering, group management and communication pattern discovery. The engine harness personalized social data (both content and contact) from the social-networking services and personal devices. The framework also contains methods for social strength calculation based on a unified interaction model that supports communication pattern discovery. A comparison study is presented together with the framework, which evaluates different social strength computation methods based on a simulated interaction dataset. The feasibility of social data collection from social networks and communication services are also discussed to illuminate potential benefits of the framework for the next generation of communication tools (such as mobile video conferencing).Evaluation of the framework is initially done by proof-of-concept prototypes that illustrate functional feasibility. Two prototypes are presented in this thesis, a presence information viewer that filters and prioritizes contacts and a real-time photo sharing application utilizing calendar data for initiation of group communication. In conclusion, improving group communication by offering services for micro-communities, based on our communication habits, personal interests and context (such as activity and location) is technically realistic and feasible.

The paradigm of pervasive computing aims to integrate the computing technologies in a graceful and transparent manner, and make computing solutions available anywhere and at any time. Different aspects of pervasive computing, like smart homes, smart offices, social networks, micromarketing applications, PDAs, etc. are becoming a part of everyday life. Context of pervasive computing system is any piece of information that can be of possible interest to the system. Context often includes location, time, activity, surroundings, etc. One of the core features of pervasive computing systems is context awareness – the ability to use context information to the benefit of the system. The thesis proposes a set of context prediction and situation prediction methods on top of enhanced situation awareness mechanisms. Being aware of the future context enables a pervasive computing system to choose the most efficient strategies to achieve its stated objectives and therefore a timely response to the upcoming situation can be provided. This thesis focuses on the challenges of context prediction, but in order to become really efficient and useful, context prediction approaches need to be gracefully integrated with different other aspects of reasoning about the context. This thesis proposes a novel integrated approach for proactively working with context information. In order to become efficient, context prediction should be complemented with proper acting on predicted context, i.e. proactive adaptation. The majority of current approaches to proactive adaptation solves context prediction and proactive adaptation problems in sequence. This thesis identifies the shortcomings of that approach, and proposes an alternative solution based on reinforcement learning techniques. The concept of situation provides useful generalization of context data and allows eliciting the most important information from the context. The thesis proposes, justifies and evaluates improved situation modeling methods that allow covering broader range of real-life situations of interest and efficiently reason about situation relationships. The context model defines the pervasive computing system’s understanding of its internal and external environments, and determines the input for context prediction solutions. This thesis proposes novel methods for formal verification of context and situation models that can help to build more reliable and dependable pervasive computing systems and avoid the inconsistent context awareness, situation awareness and context prediction results. The architecture of pervasive computing system integrates all the aspects of context reasoning and governs the interaction and collaboration between different context processing mechanisms. This thesis proposes, justifies and evaluates the architectural support for context prediction methods. The novel architectural solutions allow encapsulating various practical issues and challenges of pervasive computing systems and handling them on low levels of context processing, therefore, supporting the efforts for efficient context prediction and proactive adaptation.

Mobility support for users connecting to the Internet is an increasing trend. Different types of access networks like WiFi, CDMA, and UMTS are available, creating a heterogeneous access network environment. In the Internet today, there are a number of providers of various sizes supporting different technologies. Moving between such operators different types of authentication methods are often used interrupting ongoing services. This, in combination with lacking roaming agreements makes mobility among them with maintained connectivity and uninterrupted services difficult or even impossible.This thesis proposes an extended functionality to the Authentication, Authorization and Accounting, (AAA) protocol that enables a single AAA infrastructure to exist in a heterogeneous network environment and that enables interconnection between different operators in a tree-like structure of AAA servers. Mobile devices will maintain their IP address while connected to a network different from the home network independent of the network access technology. Furthermore, a scalability study is carried out in order to determine what is required from an AAA system in order for it to perform when dealing with larger numbers of users, service providers as well as supporting new technologies. A method for providing information for handover decisions for intra- and inter-operator mobility is also proposed. The proposed method selects the access network that according to a metric based on jitter and delay shows best performance.Evaluations show that authentication and IP address assignment can be supported in an efficient way in comparison with state of the art for both Ethernet and PPP based access networks using a common AAA infrastructure. CPU, memory and network link capacity in the home AAA server are identified as the primary bottlenecks when discussing scalability in RADIUS based AAA infrastructures and guidelines are proposed to address scalability issues during system design. The metric proposed to support in handover decisions shows that bandwidth can be estimated with more than 90% accuracy for WiFi, CDMA, and UMTS access networks.

This licentiate thesis discusses how lifelogging technologies can be used to build digital reminiscence systems. Lifelogging is a recent pervasive computing trend where different aspects of someone’s life are captured digitally. The aim of the proposed system is to create digital lifestories that can visualize the life of a person and provide a means for retrieving life experiences. The target users are people with mild dementia who have problems in navigating their daily life and in recalling previous events. The claim is that digital lifestories can be utilized for memory and reminiscence support as well as strengthen the bond between a person with mild dementia and his family. The main focus of the research study is about designing and developing digital reminiscence systems that can be used by people with mild dementia as aiding memory tools. Creating digital lifestories requires capturing of context data, such as places and people, and content data, such as sound and images, using pervasive lifelogging tools. The passive and continues capture of data results in the occurrence of false data and noise. For that, the system should reduce the collected data to not overload the user when reviewing the lifelogs. Another problem is that the life should be segmented in the form of activities that are searchable and accessible. Thus the collected lifelog data should be aggregated and structured into semantic activities and then represented as digital lifestories where context data can be retrieved together with related content. This licentiate thesis proposes solutions for filtering collected data to reduce the user’s efforts when reminiscing. The thesis also presents a method that uses prior knowledge of context data to improve the recognition of activities when creating the digital lifestories. In addition, locations where the user spends significant time can help in determining context parameters such as activities. This licentiate thesis proposes a novel approach that collects and clusters logged locations of the user to improve the activity recognition task. The presented approach defines possible places first, and it then identifies activities based on those places. Images, as content data, are then associated with the activities based on their timeframes so the user can review and adjust the data before saving it to his lifestory. The presented digital reminiscence system was evaluated through a field-test involving 10 people with mild dementia together with their caregivers. Healthcare professionals were also involved in the design and the evaluation of the system to improve the outcome of the study. The preliminary results indicate that the system indeed improves the quality of life for people with mild dementia, as their reminiscence processes are encouraged and that the communication with their surroundings increases in both volume and quality. The thesis shows that digital reminiscence systems, which describe life through activities, can increase the perceived quality of life for people with mild dementia. It also shows that activity recognition can be improved by using prior knowledge of context data and by automatic location clustering.

There is an increasing need for personalised and context-aware services in our everyday lives and we rely on mobile and wearable devices to provide such services. Data collected from these devices includes important information about users’ movements, locations, physiological status, and environment. This data can be analysed in order to recognise users’ activities and thus provide contextual information for services. Such activity recognition is an important tool for personalising and adapting assistive services and thereby increasing the usefulness of them.This licentiate thesis focuses on three important aspects for activity recognition usingwearable, resource constrained, devices in pervasive services. Firstly, it is investigated how to perform activity recognition unobtrusively by using a single tri-axial accelerometer. This involves finding the best combination of sensor placement and machine learning algorithm for the activities to be recognized. The best overall placement was found to be on the wrist using the random forest algorithm for detecting Strong-Light, Free-Bound and Sudden-Sustained movement activities belonging to the Laban Effort Framework.Secondly, this thesis proposes a novel machine learning algorithm suitable for resource-constrained devices commonly found in wearable and pervasive systems. The proposed algorithm is computationally inexpensive, parallelizable, has a small memory footprint, and is suitable for implementation in hardware. Due to this, it can reduce battery usage, increase responsiveness, and also make it possible to distribute the machine learning task, which enables balancing computational costs against data traffic costs. The proposed algorithm is shown to have a comparable accuracy to that of more advanced machine learning algorithms mainly for datasets with two classes.Thirdly, activity recognition is applied in a personalised and pervasive service for im-proving health and wellbeing. Two monitoring prototypes and one coaching prototype were proposed for achieving positive behaviour change. The three prototypes were evaluated in a user workshop with 12 users aging between 20 and 60. Participants of the workshop believed that the proposed health and wellbeing app is something people are likely to use on a permanent basis.By applying results from this thesis, systems can be made more energy efficient andless obtrusive while still maintaining a high activity recognition accuracy. It also shows that pervasive and wearable systems using activity recognition have the potential of relieving some problems in health and wellbeing that society face today.

Background: Digital education provides lifelong learning opportunities and acquisition of new skills and the importance of developing flexible e-learning platforms, taking into account the end- users’ needs and experiences, is high. People with intellectual disabilities are the most likely group to encounter challenges related to school while they are underrepresented in studies in web accessibility and digital education. DigiJag project in Sweden aims to the development of an e-learning and social platform accessible to users with moderate intellectual disabilities. Purpose: The purpose of this exploratory research study was to define the user’s needs and identify the key features used in the design of an e-learning and social platform accessible to users with moderate ID. Methods: Participatory methods were used to provide all stakeholders an influence on the final design. Qualitative data was collected from two focus groups with professional experts. Qualitative data was also collected from a series of processes together with students with ID including three voting sessions, individual observation barrier walkthroughs and two cognitive walkthroughs for heuristic evaluation of DigiJag platform’s Hi-Fi interactive prototype. Qualitative content theme analysis was used, and an iterative prototype design process was applied. Results: The study revealed themes related to direct and indirect stakeholders, values that reflect user needs, key features to be supported by the platform and suggestions for data collection methods from students with intellectual disabilities using participatory design processes. Additionally, aesthetic elements, social themes for the platform, information regarding the user experience of students with intellectual disabilities with existing interactive digital tools as well as results from heuristic evaluation of an interactive Hi-Fi prototype were also part of the study’s findings. Discussion: By involving people with intellectual disabilities in different stages of this study, we managed to give them voice in the design process and also to distribute power from designers and experts to users with intellectual disabilities. / Bakgrund: Digital utbildning ger möjligheter till livslångt lärande och nya färdigheter därför är vikten av att utveckla flexibla plattformar för e-learning, med hänsyn till slutanvändarnas behov och erfarenheter, hög. Personer med intellektuella funktionsnedsättningar är den grupp som möter störst utmaningar relaterade till skolan samtidigt som de är underrepresenterade i studier inom webbtillgänglighet och digital utbildning. DigiJag-projektet syfte är att utveckla en elearning och social plattform som är tillgänglig för användare med måttliga intellektuella funktionsnedsättning (IF). Syfte: Syftet med den här undersökande forskningsstudien var att definiera användarens behov och identifiera nyckelfunktioner som används vid utformningen av en e-learning och social plattform som är tillgänglig för användare med måttligt IF. Metoder: Deltagande metoder användes för att ge alla intressenter ett inflytande på den slutliga designen. Kvalitativa data samlades in från två fokusgrupper med professionella experter. Kvalitativa data samlades också in från en serie processer tillsammans med studenter med IF inklusive tre omröstningssessioner, individuella genomgångar för observationsbarriär och två kognitiva genomgångar för heuristisk utvärdering av DigiJag-plattformens interaktiva prototyp Hi-Fi. Kvalitativt innehållsanalys användes och en iterativ prototypdesignprocess tillämpades. Resultat: Studien visade teman relaterade till direkta och indirekta intressenter, värden som återspeglar användarnas behov, nyckelfunktioner som stöds av plattformen och förslag till datainsamlingsmetoder från studenter med intellektuella funktionsnedsättningar med deltagande designprocesser. Dessutom var estetiska element, sociala teman för plattformen, information om användarupplevelsen för studenter med intellektuella funktionsnedsättningar med befintliga interaktiva digitala verktyg samt resultat från heuristisk utvärdering av en interaktiv Hi-Fi-prototyp också en del av studiens resultat. Diskussion: Genom att involvera personer med intellektuella funktionsnedsättningar i olika stadier av denna studie lyckades vi ge dem röst i designprocessen och också att omfördela makt från designers och experter till användare med intellektuella funktionsnedsättningar.

This paper investigates user engagement in online communities using design provocations. These provocations aimed to stimulate feedback and gather discussion from members of the online community, Upbeater society. The design process consisted of preliminary interviews which gathered user insights, after which five design provocations following a Research through Design methodology were created using the insights and related work, and these provocations were used to provoke discussions and insights from the members. The discussions from the study indicated the possibility to move beyond the online space of the community, a need to examine power dynamics existing in the communities and the potential of the provocations to study attributes that could act as facilitators or constraints of the online communities. This study also suggested that design fictions could be a useful tool for researchers analysing engagement in online communities. / Denna uppsats undersöker användarengagemang i online communities, eller på svenska online-gemenskaper, med hjälp av designprovokationer. Syftet med provokationerna var att uppmuntra feedback och samla diskussionspunkter från medlemmar i online-gemenskapen Upbeater Society. Designprocessen bestod av preliminära intervjuer för att samla insikter om användarna, varefter fem designprovokationer influerade av insikterna samt tidigare studier skapades med hjälp av en Research through Design-metodik. Dessa provokationer användes sedan för att uppmana diskussion och nya insikter från medlemmarna. Diskussionerna från studien pekar på möjligheten att gå bortom gemenskapens online-miljö, behovet av att undersöka maktdynamik som finns i online communities samt potentialen hos designprovokationer för att studera attribut som antingen kan underlätta eller begränsa för online-gemenskaper. Studien föreslår också att designfiktioner kan vara ett användbart verktyg för att analysera användareengagemang i online-communities.

This research article explores the potential of AI as a collaborative tool in graphic design, investigating designers’ perceptions and concerns regarding its integration. A preliminary study identifies current challenges faced by designers, leading to the development of three scenarios envisioning the future of AI-powered tools. A prototype tool called “Desain” with advanced AI features is created. User study and interviews uncover designers’ perspectives and ethical concerns. AI is valued as a collaborative tool, but its limitations in capturing human creativity are emphasized. Ethical concerns include lazy design, accountability, and data privacy. The study emphasizes interdisciplinary collaboration, ethical guidelines, and responsible decision-making for the future of AI in graphic design.

In an increasingly applied domain of pervasive computing, sensing devices are being deployed progressively for data acquisition from various systems through the use of technologies such as wireless sensor networks. Data obtained from such systems are used analytically to advance or improve system performance or efficiency. The possibility to acquire an enormous amount of data from any target system has made machine learning a useful approach for several large-scale analytical solutions. Machine learning has proved viable in the area of the energy sector, where the global demand for energy and the increasingly accepted need for green energy is gradually challenging energy supplies and the efficiency in its consumption. This research, carried out within the area of pervasive computing, aims to explore the application of machine learning and its effectiveness in the energy sector with dependency on sensing devices. The target application area readily falls under a multi-domain energy grid which provides a system across two energy utility grids as a combined heat and power system. The multi-domain aspect of the target system links to a district heating system network and electrical power from a combined heat and power plant. This thesis, however, focuses on the district heating system as the application area of interest while contributing towards a future goal of a multi-domain energy grid, where improved efficiency level, reduction of overall carbon dioxide footprint and enhanced interaction and synergy between the electricity and thermal grid are vital goals. This thesis explores research issues relating to the effectiveness of machine learning in forecasting heat demands at district heating substations, and the key factors affecting domestic heat load patterns in buildings. The key contribution of this thesis is the application of machine learning techniques in forecasting heat energy consumption in buildings, and our research outcome shows that supervised machine learning methods are suitable for domestic thermal load forecast. Among the examined machine learning methods which include multiple linear regression, support vector machine,  feed forward neural network, and regression tree, the support vector machine performed best with a normalized root mean square error of 0.07 for a 24-hour forecast horizon. In addition, weather and time information are observed to be the most influencing factors when forecasting heat load at heating network substations. Investigation on the effect of using substation's operational attributes, such as the supply and return temperatures, as additional input parameters when forecasting heat load shows that the use of substation's internal operational attributes has less impact.

<p>Examensarbetet är utfört vid Institutionen för teknik och naturvetenskap (ITN) vid Tekniska fakulteten, Linköpings universitet</p>