4,356 research outputs found

    CAST: Proximity broadcasting as a mode of news distribution in rural Armenia

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    CAST (DisCovery Amplification Sustainability and InTeractions) has trialled a new community communication network in rural Armenia developing models to emerge alternative news media in a highly politically pressured national state. The project was a collaboration between the Media Innovation Studio, UK, Impact Hub Yerevan and SMART Edge Platform provider WICASTR™, Yerevan. The project also ran with the support of the United Nations Development Programme, award-winning investigative journalism outlet Hetq, and Civilnet from the Civilitas Foundation. It was a three-phase year-long pilot that ran in 2016 and 2017, funded by the UK Higher Education Innovation Fund. The aim was to: Build a lightweight community connectivity system for content distribution Generate proximity insights: new data analytics that allow publishers to pinpoint what content is consumed where Facilitate novel approaches to digital literacy by creating engaged digital communities New knowledge and impact have been created around: How to build hyperlocal proximity networks using online to offline wifi technology Future scoping information systems for remote communities New hyperlocal news data analytics for publishers Novel methods to add to media plurality in a highly politically pressured environment Strategies to improve digital literacy and community communication that can challenge a digital divid

    Mapping Participatory Sensing and Community-led Environmental Monitoring Initiatives: Making Sense H2020 CAPS Project

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    This report presents a summary of the state of the art in urban participatory sensing and community-led environmental monitoring, the types of engagement approaches typically followed, contextual examples of current developments in this field, and current challenges and opportunities for successful interventions. The goal is to better understand the field and possible options for reflection and action around it, in order to better inform future conceptual and practical developments inside and outside the Making Sense project.JRC.I.2-Foresight, Behavioural Insights and Design for Polic

    Cloud-assisted body area networks: state-of-the-art and future challenges

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    Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed

    From MANET to people-centric networking: Milestones and open research challenges

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    In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

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

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    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

    Proactive seeding for information cascades in cellular networks

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    Abstract—Online social networks (OSNs) play an increasingly important role today in informing users about content. At the same time, mobile devices provide ubiquitous access to this content through the cellular infrastructure. In this paper, we exploit the fact that the interest in content spreads over OSNs, which makes it, to a certain extent, predictable. We propose Proactive Seeding– a technique for minimizing the peak load of cellular networks, by proactively pushing (“seeding”) content to selected users before they actually request it. We develop a family of algorithms that take as input information primarily about (i) cascades on the OSN and possibly about (ii) the background traffic load in the cellular network and (iii) the local connectivity among mobiles; the algorithms then select which nodes to seed and when. We prove that Proactive Seeding is optimal when the prediction of information cascades is perfect. In realistic simulations, driven by traces from Twitter and cellular networks, we find that Proactive Seeding reduces the peak cellular load by 20%-50%. Finally, we combine Proactive Seeding with techniques that exploit local mobile-to-mobile connections to further reduce the peak load. I
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