User-Centric Social Interaction for Digital Cities

Pervasive computing was envisioned by pioneers like Mark Weiser but has yet to become an everyday technology in our society. The recent advances regarding Internet of Things, social computing, and mobile access technologies converge to make pervasive computing truly ubiquitous. The key challenge is to make simple and robust solutions for normal users, which shifts the focus from complex platforms involving machine learning and artificial intelligence to more hands on construction of services that are tailored or personalized for individual users. This chapter discusses Internet of Things together with Social Computing as a basis for components that users in a “digital city” could utilize to make their daily life better, safer, etc. A novel environment for user-created services, such as social apps, is presented as a possible solution for this. The vision is that anyone could make a simple service based on Internet-enabled devices (Internet of Things) and encapsulated digital resources such as Open Data, which also can have social aspects embedded. This chapter also aims to identify trends, challenges, and recommendations in regard of Social Interaction for Digital Cities. This work will help expose future themes with high innovation and business potential based on a timeframe roughly 15 years ahead of now. The purpose is to create a common outlook on the future of Information and Communication Technologies (ICT) based on the extrapolation of current trends and ongoing research efforts.

News Recommendation for China Sina Weibo Microblog Service Based on User Social Behaviors

Sina weibo microblog is an increasingly popular social network service in China. In this work, the authors conducted a study of detecting news in Sina weibo microblog. They found the traditional definition for news can be generalized here. They first expanded the definition of news by conducting user surveys and quantitative analysis. The authors built a news recommendation system by modeling the users, classifying them into four different groups, and applying several heuristic rules, which derived from the generalized definition of news. By applying the new recommendation system, people got newsworthy information, while the funny and interesting tweets, which are popular in Sina weibo microblog, were put in the last ranking list. This study helps us achieve better understanding of heuristic rules about news. Some official organizations can also benefit from the work by supervising the most popular news around civilians.

Challenges for Personal Data Stream Management in Smart Buildings

Smart homes are equipped with multiple sensors and actuators to observe the residents and environmental phenomena, to interpret the situation out of that, and finally, to react accordingly. While the data processing for a single smart home is facile, the data processing for multiple smart homes in one smart building is more complex because there are different people (e.g., like several residents, administrators, or a property management) with different interests concerning the processed data. On that point, this chapter shows which kind of typical roles can be found in a smart building and what requirements and challenges they demand for managing and processing the data. Secondly, Data Stream Management Systems (DSMS) are introduced as an approach for processing and managing data in a smart building by presenting an appropriate architecture. Finally, the chapter discusses further concepts from DSMS and illustrates how they additionally meet and solve the requirements and the challenges.

Social Cars

Intelligent Transport Systems (ITS) encompass sensing technologies, wireless communication, and intelligent algorithms, and resemble the infrastructure for ubiquitous computing in the car. This chapter borrows from social media, locative media, mobile technologies, and urban informatics research to explore three classes of ITS applications in which human behavior plays a more pivotal role. Applications for enhancing self-awareness could positively influence driver behavior, both in real-time and over time. Additionally, tools capable of supporting our social awareness while driving could change our attitude towards others and make it easier and safer to share the road. Lastly, a better urban awareness in and outside the car improves our understanding of the road infrastructure as a whole. As a case study, the authors discuss emotion recognition (emotions such as aggressiveness and anger are a major contributing factor to car crashes) and a suitable basis and first step towards further exploring the three levels of awareness, self-, social-, and urban-awareness, in the context of driving on roads.

Towards Personal, Social, and Urban Awareness

In the past decades, numerous research efforts have been made to model and extract the contexts of users in pervasive computing environments. The recent explosion of sensor-equipped mobile phone market and the phenomenal growth of geo-tagged data (Twitter messages, Foursquare check-ins, etc.) have enabled the analysis of new dimensions of contexts that involve the social and urban context. The technology trend towards pervasive sensing and large-scale social and community computing is making “Social and Community Intelligence (SCI)” a new research area that aims at investigating individual/group behavior patterns, community and urban dynamics based on the “digital footprints.” It is believed that the SCI technology has the potential to revolutionize the field of context-aware computing. The aim of this chapter is to identify this emerging research area, present the research background, define the general system framework, characterize its unique properties, discuss the open research challenges, and present this emerging research field.

Architecture Pattern for Context-Aware Smart Environments

Recent years marked many smart environment solutions hitting the market and applying latest pervasive computing research advancements on an industrial scale. Context-aware smart environments are able to act accordingly to the immediate environment information in an intelligent, predefined, learned, or automatically inferred way, and are able to communicate to their users, thus increasing users’ comfort and awareness level. Since the beginning of the 2000s, many projects have been designing and implementing smart environment systems. When looking post-factum at the architectures of these systems, one can notice a lot of similarities among them. With the same basic structure, the biggest differences usually arise at the level of individual components, aimed to satisfy different end-level requirements. Taking many successful and undergoing projects as case-studies, this chapter looks for the common structure, the common patterns, and the “best practices” that can help future projects to reduce the efforts spent on the general system frame, and redirect those efforts to more specific requirements that are unique in every project. It introduces several architecture layers that inevitably exist in one form or another, discusses the possible layer components and the common information flows, and mentions the most notable problems, such as scalability and fault tolerance. Several case studies of successful or undergoing smart building projects show that the presented pattern can be easily mapped to their architectures.

Human Mobility Patterns

It is well accepted that the physical world itself, including communication networks, humans, and objects, is becoming a type of information system. Thus, to improve the experience of individuals, communities, organizations, and societies within such systems, a thorough comprehension of collective intelligence processes responsible for generating, handling, and controlling data is fundamental. One of the major aspects in this context and also the focus of this chapter is the development of novel methods to model human mobility patterns, which have myriad uses in crucial fields (e.g. mobile communication, urban planning, etc.). The chapter highlights the state of the art and provides a comprehensive investigation of current research efforts in this field. It classifies mobility models into synthetic, trace-based, and community-based models, and also provides insight into each category. That is, well-known approaches are presented, discussed, and qualitatively compared with each other.

Design Aspects and Context Awareness in Pervasive Games

Pervasive games are a new type of digital games that combines game and physical reality within the gameplay. This novel game type raises unprecedented research and design challenges for developers and urges the exploration of new technologies and methods to create high quality game experiences and design novel and compelling forms of content for the players. This chapter follows a systematic approach to explore the landscape of pervasive gaming. First, the authors approach pervasive games from a theoretical point of view, defining the four axes of pervasive games design, introducing the concept of game world persistency, and describing aspects of spatially/temporally/socially expanded games. Then, they present ten pervasive game projects, classified in five genres based on their playing environment and features. Following that, the authors present a comparative view of those projects with respect to several design aspects: communication and localization, context and personal awareness aspects, information model, player equipment, and game space visualization. Last, the authors highlight current trends, design principles, and future directions for pervasive games development.

Understanding Urban Dynamics from Taxi GPS Traces

The GPS traces collected from a large taxi fleet provide researchers novel opportunities to inspect the urban dynamics in a city and lead to applications that can bring great benefits to the public. In this chapter, based on a real life large-scale taxi GPS dataset, the authors reveal the unique characteristics in the four different trace stages according to the passenger status, study the urban dynamics revealed in each stage, and explain the possible applications. Specifically, from passenger vacant traces, they study the taxi service dynamics, introduce how to use them to help taxis and passengers find each other, and reveal the work shifting dynamics in a city. From passenger occupied traces, they introduce their capabilities in monitoring and predicting urban traffic and estimating travel time. From the pick-up and drop-off events, the authors show the passenger hotspots and human mobility patterns in a city. They also consider taxis as mobile GPS sensors, which probe the urban road infrastructure dynamics.

Spatial Relations in Contextual Information for Mobile Emergency Messaging

Responding to a disaster is a process that should take the least time with high-level information. It requires human decisions that could delay the whole process, thus putting more lives at stake. However, recent technological developments improve this process by facilitating decisions within the domain. Discovering the spatial relationship can help to clarify the spatial environment for the domain. In this chapter, the authors give an overview of using spatial modelling and spatial relations for context-aware messaging with emphasis on emergency situations. They utilize various existing spatial relations recognized within the field of spatial computing such as RCC8 and Egenhofer relations. The RCC8 and Egenhofer relations are examined besides a range of spatial relations using English phrases in Mona-ont emergency ontology. The Mona-ont emergency ontology is used to describe emergency scenarios. The Mona-ont emergency ontology is employed by the Mona Emergency System (MES) that generates alert messaging services to actors within a disaster area. The authors demonstrate the validity of the Mona-ont spatial relations in describing a (fictitious) flood situation in the Melbourne CBD area. They also prescribe the structure of such context-aware messages (i.e. their content and target description) for the MES system.