Quality of Information in Mobile Crowdsensing: Survey and Research Challenges

Smartphones have become the most pervasive devices in people's lives, and are clearly transforming the way we live and perceive technology. Today's smartphones benefit from almost ubiquitous Internet connectivity and come equipped with a plethora of inexpensive yet powerful embedded sensors, such as accelerometer, gyroscope, microphone, and camera. This unique combination has enabled revolutionary applications based on the mobile crowdsensing paradigm, such as real-time road traffic monitoring, air and noise pollution, crime control, and wildlife monitoring, just to name a few. Differently from prior sensing paradigms, humans are now the primary actors of the sensing process, since they become fundamental in retrieving reliable and up-to-date information about the event being monitored. As humans may behave unreliably or maliciously, assessing and guaranteeing Quality of Information (QoI) becomes more important than ever. In this paper, we provide a new framework for defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the current state-of-the-art on the topic. We also outline novel research challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN

Living Innovation Laboratory Model Design and Implementation

Living Innovation Laboratory (LIL) is an open and recyclable way for multidisciplinary researchers to remote control resources and co-develop user centered projects. In the past few years, there were several papers about LIL published and trying to discuss and define the model and architecture of LIL. People all acknowledge about the three characteristics of LIL: user centered, co-creation, and context aware, which make it distinguished from test platform and other innovation approaches. Its existing model consists of five phases: initialization, preparation, formation, development, and evaluation. Goal Net is a goal-oriented methodology to formularize a progress. In this thesis, Goal Net is adopted to subtract a detailed and systemic methodology for LIL. LIL Goal Net Model breaks the five phases of LIL into more detailed steps. Big data, crowd sourcing, crowd funding and crowd testing take place in suitable steps to realize UUI, MCC and PCA throughout the innovation process in LIL 2.0. It would become a guideline for any company or organization to develop a project in the form of an LIL 2.0 project. To prove the feasibility of LIL Goal Net Model, it was applied to two real cases. One project is a Kinect game and the other one is an Internet product. They were both transformed to LIL 2.0 successfully, based on LIL goal net based methodology. The two projects were evaluated by phenomenography, which was a qualitative research method to study human experiences and their relations in hope of finding the better way to improve human experiences. Through phenomenographic study, the positive evaluation results showed that the new generation of LIL had more advantages in terms of effectiveness and efficiency.Comment: This is a book draf

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Case Studies on the Exploitation of Crowd-Sourcing with Web 2.0 Functionalities

Crowd-sourcing appears more promising with Web 2.0 functionality and businesses have started using it for a wide range of activities, that would be better completed by a crowd rather than any specific pool of knowledge workers. However, relatively little is known about how a business can leverage on collective intelligence and capture the user- generated value for competitive advantage. This empirical study uses the principle of interpretive field research to validate the case findings with a descriptive multiple case study methodology. An extended theoretical framework to identify the important considerations at strategic and functional levels for the effective use of crowd-sourcing is proposed. The analytic framework uses five Business Strategy Components: Vision and Strategy, Human Capital, Infrastructure, Linkage and Trust, and External Environment. It also uses four Web 2.0 Functional Components: Social Networking, Interaction Orientation, Customization & Personalization, and User- added Value. By using these components as analytic lenses, the case research examines how successful e-commerce firms may deploy Web 2.0 functionalities for effective use of crowd-sourcing. Prioritization of these functional considerations might be favorable in some cases for the best fit of situations and limitations. In conclusion, it is important that the alignment between strategy and functional components is maintained

Beyond Personalization: Research Directions in Multistakeholder Recommendation

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.Comment: 64 page

Understanding human-machine networks: A cross-disciplinary survey

© 2017 ACM. In the current hyperconnected era, modern Information and Communication Technology (ICT) systems form sophisticated networks where not only do people interact with other people, but also machines take an increasingly visible and participatory role. Such Human-Machine Networks (HMNs) are embedded in the daily lives of people, both for personal and professional use. They can have a significant impact by producing synergy and innovations. The challenge in designing successful HMNs is that they cannot be developed and implemented in the same manner as networks of machines nodes alone, or following a wholly human-centric view of the network. The problem requires an interdisciplinary approach. Here, we review current research of relevance to HMNs across many disciplines. Extending the previous theoretical concepts of sociotechnical systems, actor-network theory, cyber-physical-social systems, and social machines, we concentrate on the interactions among humans and between humans and machines. We identify eight types of HMNs: public-resource computing, crowdsourcing, web search engines, crowdsensing, online markets, social media, multiplayer online games and virtual worlds, and mass collaboration. We systematically select literature on each of these types and review it with a focus on implications for designing HMNs. Moreover, we discuss risks associated with HMNs and identify emerging design and development trends

Toward a Trust Evaluation Mechanism for in the Social Internet of Things

In the blooming era of the Internet of Things (IoT), trust has been accepted as a vital factor for provisioning secure, reliable, seamless communications and services. However, a large number of challenges have been unsolved yet due to the ambiguity of the concept of trust as well as the variety of divergent trust models in different contexts. In this research, we augment the trust concept, the trust definition and provide a general conceptual model in the context of the Social IoT (SIoT) environment by breaking down all attributes influencing trust. Then, we propose a trust evaluation model called REK comprised of the triad Reputation, Experience and Knowledge trust indicators (TIs). The REK model covers multi-dimensional aspects of trust by incorporating heterogeneous information from direct observation (as Knowledge TI), personal experiences (as Experience TI) to global opinions (as Reputation TI). The associated evaluation models for the three TIs are also proposed and provisioned. We then come up with an aggregation mechanism for deriving trust values as the final outcome of the REK evaluation model. We believe this article offers better understandings on trust as well as provides several prospective approaches for the trust evaluation in the SIoT environment

Crowdsourcing Privacy Design Critique: An Empirical Evaluation of Framing Effects

When designed incorrectly, information systems can thwart people’s expectations of privacy. An emerging technique for evaluating systems during the development stage is the crowdsourcing design critique, in which design evaluations are sourced using crowdsourcing platforms. However, we know that information framing has a serious effect on decision-making and can steer design critiques in one way or another. We investigate how the framing of design cases can influence the outcomes of privacy design critiques. Specifically, we test whether -˜Personas’, a central User-Centered Design tool for describing users, can inspire empathy in users while criticizing privacy designs. In an experiment on Amazon Mechanical Turk workers (n=456), we show that describing design cases by using personas causes intrusive designs to be criticized more harshly. We discuss how our results can be used to enhance privacy-by-design processes and encourage user-centered privacy engineering