The Internet of Everything (IoE) is the paradigm of intelligent services that supports a ubiquitous and always-connected world of smart sensors and actuators, human and non-human in a cyber-physical lifestyle. Recent years have witnessed the fast proliferation of smart devices. In addition, these functional smart sensors possess powerful computation and communication capabilities and contribute to intelligent services outcomes of IoE-based applications. Then by adopting a knowledge-based approach on sensors smartness, we make a comparative assessment and classify smart sensors (image sensors, temperature sensors, proximity sensors, speed sensors, and sound sensors) regarding intelligence levels. We extracted valuable conclusions and inferences from this classification, providing insights and future directions towards promised benefits offered by distilled intelligence in smart sensors applications. (Abstract)

With a growing demand for new technologies, concepts such as the Internet of Everything (IoE) – in which smart sensors (humans and machines) connect, communicate, and share information from the surrounding environment – are gaining notoriety. Thus, this study focuses on developing a knowledge flow model in the IoE scenario for Smart Cities, including how knowledge is disseminated in these intelligent environments in human-to-machine interactions. The study considers a knowledge-based taxonomy defined for IoE environments and supports the full realization of IoE applications through a knowledge management strategy. The proposed IoE Knowledge Flow Model contributes to identification of knowledge flows related to IoE architecture layers in smart city applications.

The paradigm of the Internet of Everything (IoE) adds value to Internet of Things (IoT) in connections among people, processes, data, and things. However, knowledge creation process in IoE context still requires concerns to what extent connected sensors and actuators collaborates in value created and outcomes of IoE applications. This study focuses on the requirement and design approach for identification of critical knowledge within IoE domain though a knowledge ranking approach. Existing solutions for semantic definitions and taxonomies in IoE are reviewed for this purpose. In addition, this work proposes the use of a knowledge-based taxonomy as the main driver for ranking knowledge in smart sensors as IoE enablers with a qualitative approach of its dimensions. To the best of our knowledge, this is the first work that comprehensively integrate a taxonomy concerning ranking knowledge in IoE applications.

Over the past decade, online crowdsourcing has established itself as an emerging paradigm that industry and government have been using to harness the cognitive abilities of a multitude of users distributed around the world. In this context, microtask crowdsourcing has become the method of choice for addressing a wide range of diverse problems. Microtasks typically require a minimum of time and cognitive effort, but combined individual efforts have made it possible to accomplish great achievements. The goal of this paper is to contribute to the ongoing effort of understanding whether the same success that microtask crowdsourcing has achieved in other domains can be obtained in the field of social news curation. In particular, we ask whether it is possible to turn online news curation, typically a social and collaborative activity on the Web, into a model in which curatorial activities are mapped into microtasks to be performed by a crowd of online users.

In the last decade, games with a purpose (GWAP) have emerged as an effective way to recruit and organize a large number of volunteers to help address difficult challenges. In previous work, a framework for crowdsourced collection, cleaning, and measurement of digital content was presented and used in the design of Cassino Musical, a game for procuring and measuring the musical talent of unknown artists. Preliminary results from an online experiment with the game provide basic proof that GWAP can be successfully applied to the task of measuring musical talent, and are described in this paper.

Worldwide, the electricity sector has undergone radical changes with the emergence of new technologies that expand possibilities. In such a dynamic environment, the sector's regulatory and supervisory activities became major challenges. In this article, we present a case study on the collaborative design of a digital platform for learning, accessing information and fostering the collaboration between distributors, the regulatory agency and electricity consumer councils in Brazil. A series of national surveys were conducted with industry authorities, experts, representatives of electricity distributors and representatives of final consumers. Finally, we present the results of these surveys, which allowed the derivation of primary requirements for the design of the collaboration platform that is under development.

In recent years, social curation technology has been successfully promoted as a means of addressing the problem of involving citizens with news content. Sites like Facebook., Reddit and Storify., just to name a few, allow their users to discuss, comment., share, identify and review news content from various sources. A key goal of this paper is to analyze the process of social validation in the context of Acropolis, a social computing platform that allows citizens to build and share their own narratives about complex or long-term news stories. The sense of social validation determines an increase in self-esteem, self-confidence, and instant happiness, when conveniently engendered in virtual environments.

The ubiquity of smartphones creates great opportunities for participatory sensing, where people can implicitly contribute observations about their local environments through sensors such as cameras and accelerometers. The collected data can then be aggregated and used to benefit the crowd in some way. In this paper, we report on the current development of Sherlock, a device capable of automatically detecting the presence of people in localities through evidences left by smartphones called probe requests, without any user intervention. To validate the proposed mechanism implemented in the device, we performed an experiment with ten participants in six rounds where it was possible to automatically detect 41 presence events, of which 66% could be detected within less than 30s. The implicit crowdsourcing mechanism behind this approach may allow real-time monitoring of people flows in public environments, which can enable, among other things, energy systems to be automatically orchestrated according to demand, reducing associated costs.

Acropolis is a social computing platform especially designed to engage citizens in building and sharing their own narratives about complex or long-term news stories. The first two design cycles of the platform have shown, by positive evidence, that this goal has been achieved, but in the previous papers no analysis was conducted regarding the social behavior of the participants in relation to consuming or being exposed to different viewpoints of their own. Several studies suggest that people crave more for reinforcement of opinion than they reject exposure from different points of view, and that from the standpoint of strengthening democratic discourse, hearing the other side is crucial. A key goal of this paper is to evaluate the extent to which the participants of the latest experiment in the aforementioned platform followed this behavior. Besides that, we aim to investigate whether the design of the platform favored the goal of encouraging the consumption of diverse sources and opinions, with an eye towards the next design cycles.

This paper presents an observatory for registering applications that use participatory sensing to collect data. Cataloging these applications will aid the scientific community to exchange more information, facilitating the comparison between different initiatives. Through an initial research, the applications are categorized in areas usually considered in the literature. We propose a survey to validate the platform and also discuss the taxonomies created as a result of this survey. The main contributions of this paper include the classification of crowdsensing applications in different ontological categories, as well as the proposal of a technology platform that enables the distributed and collaborative cataloging of crowdsensing initiatives.

It is understood that large quantities and diversity among users of a crowdsourcing system is something to be explored. So that the characterization of these users can aid in the decision-making on aspects related to crowdsourcing systems design, such as user interface design, recruitment strategies, motivating participants, among other specific challenges of crowdsourcing systems. Considering the characterization of the users' communities within the context of crowdsourcing systems, it was noted the need for a framework that helps in the identification of user-model classes more adherent to certain types of crowdsourcing systems. Thus, in this paper we present the proposal of a high-level framework that associates a set of relevant user models classes in relation to types of crowdsourcing systems. This paper also discusses the results of a preliminary study, presenting the analysis of the framework, which was capable of helping a crowdsourcing researchers' group choosing the user-model class that best fits to their business models.

Waiting for service is a problem faced by people from different countries and cultures. The problem of waiting in a queue can bring losses of quality of life and even economic problems. In this paper, we propose a mechanism for automatic detection of wait events via smartphones. This detection is done cooperatively by observing data collected from the user's smartphone's wifi and accelerometer sensors, and such information is compared to contributions made by other users. Experiments have proven the efficacy of this mechanism to be about 99% accurate.

The waiting time for customer services is a problem that affects most cultures and has been studied by the academic community for several decades from the standpoint of psychology, sociology, marketing and computer science. In this paper we present a review of the literature on the subject, and also propose a classification of approaches that deal with this problem. We also report our participatory effort in articulating requirements and functionalities of a design solution to address this problem. A series of interviews were conducted with stakeholders and other people involved in the problem of waiting time delays in customer services, allowing requirements to be posed and validated through a survey. Finally, we present the latest details of an application being developed to address this problem and the final conclusions.

This work in progress proposes the use of vivid impressions of users to monitor the waiting and service time in the provision of public and private services. The solution consists of an environment in which users can report the time spent waiting for the provision of a particular service. For the strategy to be successful, this account should be done through a simplified interface, preferably mobile, that takes advantage of the user's location knowledge. This approach uses the crowd collaboration to achieve some goal, and has been used in applications such as Waze.

A basic property of wireless networks is the half duplex nature of their links. However, previous works have already shown, with real prototypes of full duplex radios, to be possible for a node to transmit and receive packets at the same time. Nevertheless, the gains that result from such techniques are not straightforward. In this work we analyze how frequently full duplex transmissions can occur in typical wireless network scenarios. Simulations were conducted in both infra-structured and multihop networks with several traffic patterns, and results show potential gains up to 99.96% and 36.93% in the infra-structured and multihop cases, respectively.