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Substantial ongoing research now uses smartphones as a research platform for various studies and interventions. With the ageing population becoming a frequent focus of needed research, an increasing number of studies and projects attempt to develop technological interventions for the elderly population. Yet, it is not clear exactly how widespread is the adoption and use of smartphones amongst seniors. Many studies acknowledge that today's elders are not particularly keen on using smartphones, but in the near future we can expect this trend to change. In this paper we present an in-depth survey of statistics on smartphone adoption within the elder population, and describe both the popularity and type of use that smartphones enjoy amongst elders. We show that far from being ubiquitous, smartphones are still overshadowed by traditional feature phones today, and substantial geographical differences also do exist between countries. Furthermore, those seniors who do adopt smartphones tend to use them as feature phones, and do not adopt services that are popular amongst younger users. Our survey provides an assessment on the ubiquity of smartphones amongst seniors, that can be used to inform the assumptions of our research community.

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A B S T R A C TWaste Management (WM) represents an important part of Smart Cities (SCs) with significant impact on modern societies. WM involves a set of processes ranging from waste collection to the recycling of the collected materials. The proliferation of sensors and actuators enable the new era of Internet of Things (IoT) that can be adopted in SCs and help in WM. Novel approaches that involve dynamic routing models combined with the IoT capabilities could provide solutions that outperform existing models. In this paper, we focus on a SC where a number of collection bins are located in different areas with sensors attached to them. We study a dynamic waste collection architecture, which is based on data retrieved by sensors. We pay special attention to the possibility of immediate WM service in high priority areas, e.g., schools or hospitals where, possibly, the presence of dangerous waste or the negative effects on human quality of living impose the need for immediate collection. This is very crucial when we focus on sensitive groups of citizens like pupils, elderly or people living close to areas where dangerous waste is rejected. We propose novel algorithms aiming at providing efficient and scalable solutions to the dynamic waste collection problem through the management of the trade-off between the immediate collection and its cost. We describe how the proposed system effectively responds to the demand as realized by sensor observations and alerts originated in high priority areas. Our aim is to minimize the time required for serving high priority areas while keeping the average expected performance at high level. Comprehensive simulations on top of the data retrieved by a SC validate the proposed algorithms on both quantitative and qualitative criteria which are adopted to analyze their strengths and weaknesses. We claim that, local authorities could choose the model that best matches their needs and resources of each city.

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While Decision Support Systems (DSS) have a long history, their usefulness for non-experts outside specific organisations has not lived up to the promise. A key reason for this is the high cost associated with populating the underlying knowledge bases. In this article, we describe how DSSs can leverage crowds and their wisdom in constructing knowledge bases to overcome this challenge. We also demonstrate how to construct DSSs on-the-fly using the collected data. Our user-driven laboratory studies focus on user perceptions of the concept itself, and motives for contributing and using such a DSS. To the best of our knowledge, our approach and implementation are the first to demonstrate such use of crowdsourcing in building DSSs. Decision Support Systems, evaluation, crowdsourcing, wisdom of the crowd

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