Aplikacije utemeljene na mnoštvu i društveni izazovi

The spread of mobile technology and ubiquitous connectivity have opened great possibilities for the implementation of applications that leverage data generated by normal users’ interactions on the web. As a consequence, there is a growing interest in crowd-based applications, namely those programs that involve people in a participatory or opportunistic way. In many cases, data can be gathered automatically without user intervention and, in some cases, even without their explicit knowledge. The possibility to elude a users’ awareness fosters concerns regarding the potential risks hidden inside crowd-based applications. These applications might compromise the privacy of citizens, whilst data collected by them might be used to manipulate people’s opinions. The governance of technology is a controversial area, and there is a wide array of different positions on the matter. There are those who dogmatically argue the positive value of technology, while others interpret the ongoing digital advancements as a dystopian menace. This article focuses on crowd-based applications, highlighting some societal challenges and risks that they may present. Technology runs so fast that it is challenging to keep pace with the changes brought by the digital revolution. However, an effort is required to extend the depth of digital knowledge of citizens and involve them in the use of the new technologies, and in this endeavor, greater knowledge is an essential step in any critical process.Širenje mobilne tehnologije i sveprisutna povezanost otvorili su velike mogućnosti za upotrebu aplikacija koje iskorištavaju podatke generirane normalnim interakcijama korisnika na webu. Kao posljedica toga, sve je veći interes za aplikacije utemeljene na mnoštvu (engl. crowd-based applications), za one programe koji uključuju ljude na participativni ili oportunistički način. U mnogim se slučajevima podaci mogu prikupljati automatski, bez djelovanja korisnika, a u nekim slučajevima čak i bez njihova izričitog znanja. Mogućnost izbjegavanja svjesnosti korisnika potiče zabrinutosti u vezi s potencijalnim rizicima koji su skriveni u aplikacijama utemeljenim na mnoštvu. Te aplikacije mogu ugroziti privatnost građana, dok bi se prikupljeni podaci mogli koristiti za manipuliranje stavovima ljudi. Upravljanje tehnologijom kontroverzno je područje i o tom pitanju postoji mnoštvo različitih stajališta. Neki dogmatski zastupaju pozitivne vrijednosti tehnologije, dok drugi digitalni napredak tumače kao distopijsku prijetnju. Rad se usredotočuje na aplikacije utemeljene na mnoštvu, ističući neke društvene izazove i rizike koje mogu predstavljati. Tehnologija napreduje tako brzo da je izazovno biti u tijeku s promjenama koje je donijela digitalna revolucija. No, potrebno je pokušati produbiti digitalno znanje građana i uključiti ih u upotrebu novih tehnologija, a u tom je poduhvatu veće znanje temeljni korak u svakom kritičnom procesu

Co-creating the cities of the future

In recent years, the evolution of urban environments, jointly with the progress of the Information and Communication sector, have enabled the rapid adoption of new solutions that contribute to the growth in popularity of Smart Cities. Currently, the majority of the world population lives in cities encouraging different stakeholders within these innovative ecosystems to seek new solutions guaranteeing the sustainability and efficiency of such complex environments. In this work, it is discussed how the experimentation with IoT technologies and other data sources form the cities can be utilized to co-create in the OrganiCity project, where key actors like citizens, researchers and other stakeholders shape smart city services and applications in a collaborative fashion. Furthermore, a novel architecture is proposed that enables this organic growth of the future cities, facilitating the experimentation that tailors the adoption of new technologies and services for a better quality of life, as well as agile and dynamic mechanisms for managing cities. In this work, the different components and enablers of the OrganiCity platform are presented and discussed in detail and include, among others, a portal to manage the experiment life cycle, an Urban Data Observatory to explore data assets, and an annotations component to indicate quality of data, with a particular focus on the city-scale opportunistic data collection service operating as an alternative to traditional communications.This work has been partially funded by the research project OrganiCity, under the grant agreement No. 645198 of the European Union’s Horizon 2020 research and innovation program

Citizens AND HYdrology (CANDHY): conceptualizing a transdisciplinary framework for citizen science addressing hydrological challenges

Widely available digital technologies are empowering citizens who are increasingly well informed and involved in numerous water, climate, and environmental challenges. Citizen science can serve many different purposes, from the "pleasure of doing science" to complementing observations, increasing scientific literacy, and supporting collaborative behaviour to solve specific water management problems. Still, procedures on how to incorporate citizens' knowledge effectively to inform policy and decision-making are lagging behind. Moreover, general conceptual frameworks are unavailable, preventing the widespread uptake of citizen science approaches for more participatory cross-sectorial water governance. In this work, we identify the shared constituents, interfaces, and interlinkages between hydrological sciences and other academic and non-academic disciplines in addressing water issues. Our goal is to conceptualize a transdisciplinary framework for valuing citizen science and advancing the hydrological sciences. Joint efforts between hydrological, computer, and social sciences are envisaged for integrating human sensing and behavioural mechanisms into the framework. Expanding opportunities of online communities complement the fundamental value of on-site surveying and indigenous knowledge. This work is promoted by the Citizens AND HYdrology (CANDHY) Working Group established by the International Association of Hydrological Sciences (IAHS)

Vehicle as a Service (VaaS): Leverage Vehicles to Build Service Networks and Capabilities for Smart Cities

Smart cities demand resources for rich immersive sensing, ubiquitous communications, powerful computing, large storage, and high intelligence (SCCSI) to support various kinds of applications, such as public safety, connected and autonomous driving, smart and connected health, and smart living. At the same time, it is widely recognized that vehicles such as autonomous cars, equipped with significantly powerful SCCSI capabilities, will become ubiquitous in future smart cities. By observing the convergence of these two trends, this article advocates the use of vehicles to build a cost-effective service network, called the Vehicle as a Service (VaaS) paradigm, where vehicles empowered with SCCSI capability form a web of mobile servers and communicators to provide SCCSI services in smart cities. Towards this direction, we first examine the potential use cases in smart cities and possible upgrades required for the transition from traditional vehicular ad hoc networks (VANETs) to VaaS. Then, we will introduce the system architecture of the VaaS paradigm and discuss how it can provide SCCSI services in future smart cities, respectively. At last, we identify the open problems of this paradigm and future research directions, including architectural design, service provisioning, incentive design, and security & privacy. We expect that this paper paves the way towards developing a cost-effective and sustainable approach for building smart cities.Comment: 32 pages, 11 figure

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities

Mobile crowdsensing (MCS) has gained significant attention in recent years and has become an appealing paradigm for urban sensing. For data collection, MCS systems rely on contribution from mobile devices of a large number of participants or a crowd. Smartphones, tablets, and wearable devices are deployed widely and already equipped with a rich set of sensors, making them an excellent source of information. Mobility and intelligence of humans guarantee higher coverage and better context awareness if compared to traditional sensor networks. At the same time, individuals may be reluctant to share data for privacy concerns. For this reason, MCS frameworks are specifically designed to include incentive mechanisms and address privacy concerns. Despite the growing interest in the research community, MCS solutions need a deeper investigation and categorization on many aspects that span from sensing and communication to system management and data storage. In this paper, we take the research on MCS a step further by presenting a survey on existing works in the domain and propose a detailed taxonomy to shed light on the current landscape and classify applications, methodologies, and architectures. Our objective is not only to analyze and consolidate past research but also to outline potential future research directions and synergies with other research areas

Game Theory Based Privacy Protection for Context-Aware Services

In the era of context-aware services, users are enjoying remarkable services based on data collected from a multitude of users. To receive services, they are at risk of leaking private information from adversaries possibly eavesdropping on the data and/or the un--trusted service platform selling off its data. Malicious adversaries may use leaked information to violate users\u27 privacy in unpredictable ways. To protect users\u27 privacy, many algorithms are proposed to protect users\u27 sensitive information by adding noise, thus causing context-aware service quality loss. Game theory has been utilized as a powerful tool to balance the tradeoff between privacy protection level and service quality. However, most of the existing schemes fail to depict the mutual relationship between any two parties involved: user, platform, and adversary. There is also an oversight to formulate the interaction occurring between multiple users, as well as the interaction between any two attributes. To solve these issues, this dissertation firstly proposes a three-party game framework to formulate the mutual interaction between three parties and study the optimal privacy protection level for context-aware services, thus optimize the service quality. Next, this dissertation extends the framework to a multi-user scenario and proposes a two-layer three-party game framework. This makes the proposed framework more realistic by further exploring the interaction, not only between different parties, but also between users. Finally, we focus on analyzing the impact of long-term time-serial data and the active actions of the platform and adversary. To achieve this objective, we design a three-party Stackelberg game model to help the user to decide whether to update information and the granularity of updated information

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