Understanding social behaviors in the indoor environment: a complex network approach

Being able to monitor and analyze human interactions in the indoor environment over time has many architectural applications from spatial planning to post occupancy evaluation. In this paper, we present our interdisciplinary approach that interprets human-spatial interactions as a complex network. We combine methods and techniques from sensor networks, signal processing, data mining, network theory, and information visualization to form a novel framework that facilitates versatile investigations. We will demonstrate the framework with a real-world case study: we have collected and analyzed human-spatial interaction data from a workshop scenario where multiple design projects were conducted within a shared studio space

Interval Tree-Based Task Scheduling Method for Mobile Crowd Sensing Systems

Nowadays there is an increasing demand to provide a real-time environmental information. So, the growing number of mobile devices carried by users establish a new and fastgrowing sensing paradigm to satisfy this need, which is called Mobile Crowd Sensing (MCS). The MCS uses different sensing abilities to acquire local knowledge through enhanced mobile devices. In MCS, it is very important to collect high-quality sensory data that satisfies the needs of all assigned tasks and the task organizers with a minimum cost for the participants. One of the most important factors which affect the MCS cost is how to schedule different sensing tasks which must be assigned to a smartphone with the objective of minimizing sensing energy consumption while ensuring high-quality sensory data. In this paper, the problem of task scheduling the which have mutual sensor is formulated and a scheduling method to minimize the energy consumption by reducing the sensor utilization is proposed. The proposed method will incentive the users to participate in multiple tasks at the same time, which minimizes the total cost of the performed tasks and increases his rewards. The experimental results by using synthetic and real data show that the proposed scheduling method can minimize the energy consumption and preserve the task requirements compared to existing algorithms

Design criteria for Indoor Positioning Systems in hospitals using technological, organizational and individual perspectives

This dissertation considers three different studies that handle Indoor Positioning Systems (IPS) in hospitals. Study 1 uses the Reasoned Action Approach by questioning hospital visitors and employees about their intention to use IPS in hospitals. Study 2 reviews IPS in hospitals. Study 3 is based on the results of the first two studies. It handles expert interviews that were conducted with different hospitals and IPS developers to evaluate the determined propositions. Then, the insights were used to conduct and evaluate experiments by testing an ultrasound-based IPS for hospitals

Do Monetary Incentives Influence Users’ Behavior in Participatory Sensing?

Participatory sensing combines the powerful sensing capabilities of current mobile devices with the mobility and intelligence of human beings, and as such has to potential to collect various types of information at a high spatial and temporal resolution. Success, however, entirely relies on the willingness and motivation of the users to carry out sensing tasks, and thus it is essential to incentivize the users’ active participation. In this article, we first present an open, generic participatory sensing framework (Citizense) which aims to make participatory sensing more accessible, flexible and transparent. Within the context of this framework we adopt three monetary incentive mechanisms which prioritize the fairness for the users while maintaining their simplicity and portability: fixed micro-payment, variable micro-payment and lottery. This incentive-enabled framework is then deployed on a large scale, real-world case study, where 230 participants were exposed to 44 different sensing campaigns. By randomly distributing incentive mechanisms among participants and a subset of campaigns, we study the behaviors of the overall population as well as the behaviors of different subgroups divided by demographic information with respect to the various incentive mechanisms. As a result of our study, we can conclude that (1) in general, monetary incentives work to improve participation rate; (2) for the overall population, a general descending order in terms of effectiveness of the incentive mechanisms can be established: fixed micro-payment first, then lottery-style payout and finally variable micro-payment. These two conclusions hold for all the demographic subgroups, even though different different internal distances between the incentive mechanisms are observed for different subgroups. Finally, a negative correlation between age and participation rate was found: older participants contribute less compared to their younger peers

From data acquisition to data fusion : a comprehensive review and a roadmap for the identification of activities of daily living using mobile devices

This paper focuses on the research on the state of the art for sensor fusion techniques, applied to the sensors embedded in mobile devices, as a means to help identify the mobile device user’s daily activities. Sensor data fusion techniques are used to consolidate the data collected from several sensors, increasing the reliability of the algorithms for the identification of the different activities. However, mobile devices have several constraints, e.g., low memory, low battery life and low processing power, and some data fusion techniques are not suited to this scenario. The main purpose of this paper is to present an overview of the state of the art to identify examples of sensor data fusion techniques that can be applied to the sensors available in mobile devices aiming to identify activities of daily living (ADLs)

A Survey on Smartphone-Based Crowdsensing Solutions

© 2016 Willian Zamora et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.[EN] In recent years, the widespread adoption of mobile phones, combined with the ever-increasing number of sensors that smartphones are equipped with, greatly simplified the generalized adoption of crowdsensing solutions by reducing hardware requirements and costs to a minimum. These factors have led to an outstanding growth of crowdsensing proposals from both academia and industry. In this paper, we provide a survey of smartphone-based crowdsensing solutions that have emerged in the past few years, focusing on 64 works published in top-ranked journals and conferences. To properly analyze these previous works, we first define a reference framework based on how we classify the different proposals under study. The results of our survey evidence that there is still much heterogeneity in terms of technologies adopted and deployment approaches, although modular designs at both client and server elements seem to be dominant. Also, the preferred client platform is Android, while server platforms are typically web-based, and client-server communications mostly rely on XML or JSON over HTTP. The main detected pitfall concerns the performance evaluation of the different proposals, which typically fail to make a scalability analysis despite being critical issue when targeting very large communities of users.This work was partially supported by the Ministerio de Economia y Competitividad, Programa Estatal de Investigacion, Desarrollo e Innovacion Orientada a los Retos de la Sociedad, Proyectos I+D+I 2014, Spain, under Grant TEC2014-52690-R, the "Universidad Laica Eloy Alfaro de Manabi-ULEAM," and the "Programa de Becas SENESCYT de la Republica del Ecuador."Zamora-Mero, WJ.; Tavares De Araujo Cesariny Calafate, CM.; Cano Escribá, JC.; Manzoni, P. (2016). A Survey on Smartphone-Based Crowdsensing Solutions. Mobile Information Systems. 2016:1-26. https://doi.org/10.1155/2016/9681842S126201

How data will transform industrial processes: crowdsensing, crowdsourcing and big data as pillars of industry 4.0

We are living in the era of the fourth industrial revolution, namely Industry 4.0. This paper presents themain aspects related to Industry 4.0, the technologies thatwill enable this revolution, and the main application domains thatwill be affected by it. The effects that the introduction of Internet of Things (IoT), Cyber-Physical Systems (CPS), crowdsensing, crowdsourcing, cloud computing and big data will have on industrial processeswill be discussed. Themain objectiveswill be represented by improvements in: production efficiency, quality and cost-effectiveness; workplace health and safety, as well as quality of working conditions; products' quality and availability, according to mass customisation requirements. The paper will further discuss the common denominator of these enhancements, i.e., data collection and analysis. As data and information will be crucial for Industry 4.0, crowdsensing and crowdsourcing will introduce new advantages and challenges, which will make most of the industrial processes easier with respect to traditional technologies

A Framework and Classification for Fault Detection Approaches in Wireless Sensor Networks with an Energy Efficiency Perspective

Wireless Sensor Networks (WSNs) are more and more considered a key enabling technology for the realisation of the Internet of Things (IoT) vision. With the long term goal of designing fault-tolerant IoT systems, this paper proposes a fault detection framework for WSNs with the perspective of energy efficiency to facilitate the design of fault detection methods and the evaluation of their energy efficiency. Following the same design principle of the fault detection framework, the paper proposes a classification for fault detection approaches. The classification is applied to a number of fault detection approaches for the comparison of several characteristics, namely, energy efficiency, correlation model, evaluation method, and detection accuracy. The design guidelines given in this paper aim at providing an insight into better design of energy-efficient detection approaches in resource-constraint WSNs

Public crowdsensing of heat waves by social media data

Abstract. Investigating on society-related heat wave hazards is a global issue concerning the people health. In the last two decades, Europe experienced several severe heat wave episodes with catastrophic effects in term of human mortality (2003, 2010 and 2015). Recent climate investigations confirm that this threat will represent a key issue for the resiliency of urban communities in next decades. Several important mitigation actions (Heat-Health Action Plans) against heat hazards have been already implemented in some WHO (World Health Organization) European region member states to encourage preparedness and response to extreme heat events. Nowadays, social media (SM) offer new opportunities to indirectly measure the impact of heat waves on society. Using the crowdsensing concept, a micro-blogging platform like Twitter may be used as a distributed network of mobile sensors that react to external events by exchanging messages (tweets). This work presents a preliminary analysis of tweets related to heat waves that occurred in Italy in summer 2015. Using TwitterVigilance dashboard, developed by the University of Florence, a sample of tweets related to heat conditions was retrieved, stored and analyzed for main features. Significant associations between the daily increase in tweets and extreme temperatures were presented. The daily volume of Twitter users and messages revealed to be a valuable indicator of heat wave impact at the local level, in urban areas. Furthermore, with the help of Generalized Additive Model (GAM), the volume of tweets in certain locations has been used to estimate thresholds of local discomfort conditions. These city-specific thresholds are the result of dissimilar climatic conditions and risk cultures