In this paper, we report on findings from an ethnographic study of how people use their calendars for personal information management (PIM). Our participants were faculty, staff and students who were not required to use or contribute to any specific calendaring solution, but chose to do so anyway. The study was conducted in three parts: first, an initial survey provided broad insights into how calendars were used; second, this was followed up with personal interviews of a few participants which were transcribed and content-analyzed; and third, examples of calendar artifacts were collected to inform our analysis. Findings from our study include the use of multiple reminder alarms, the reliance on paper calendars even among regular users of electronic calendars, and wide use of calendars for reporting and life-archival purposes. We conclude the paper with a discussion of what these imply for designers of interactive calendar systems and future work in PIM research.
With the increased use of mobile devices, more and more calendaring tasks are performed off the desktop computer. @cite_8 report on issues faced by mobile workers, their need for access to people and information located remotely, and the planful opportunism they engage in when utilizing their for tasks.
@cite_8
2107163126
The rapid and accelerating move towards use of mobile technologies has increasingly provided people and organizations with the ability to work away from the office and on the move. The new ways of working afforded by these technologies are often characterized in terms of access to information and people anytime, anywhere. This article presents a study of mobile workers that highlights different facets of access to remote people and information, and different facets of anytime, anywhere. Four key factors in mobile work are identified: the role of planning, working in "dead time," accessing remote technological and informational resources, and monitoring the activities of remote colleagues. By reflecting on these issues, we can better understand the role of technology and artifacts in mobile work and identify the opportunities for the development of appropriate technological solutions to support mobile workers. Advancement in mobile broadband communication enables to effectively communicate anywhere at any time. More specifically, for people who live in rural areas of developing countries it is the best means to communicate with the rest of the world. This has prodigious potential of enabling new opportunities in their live. The appearance of cloud computing, the recent improvements in communication networks, and the mobile phone market penetration allow a large number of users to access remote computing systems using their mobile devices. Mobile crowdsourcing (MC) is a transformative paradigm that engages a crowd of mobile users (i.e., workers) in the act of collecting, analyzing, and disseminating information or sharing their resources. To ensure quality of service, MC platforms tend to recommend MC tasks to workers based on their context information extracted from their interactions and smartphone sensors. This raises privacy concerns hard to address due to the constrained resources on mobile devices. In this paper, we identify fundamental tradeoffs among three metrics—utility, privacy, and efficiency—in an MC system and propose a flexible optimization framework that can be adjusted to any desired tradeoff point with joint efforts of MC platform and workers. Since the underlying optimization problems are NP-hard, we present efficient approximation algorithms to solve them. Since worker statistics are needed when tuning the optimization models, we use an efficient aggregation approach to collecting worker feedbacks while providing differential privacy guarantees. Both numerical evaluations and performance analysis are conducted to demonstrate the effectiveness and efficiency of the proposed framework. Spatial Crowdsourcing (SC) is a transformative platform that engages individuals, groups and communities in the act of collecting, analyzing, and disseminating environmental, social and other spatio-temporal information. The objective of SC is to outsource a set of spatio-temporal tasks to a set of workers, i.e., individuals with mobile devices that perform the tasks by physically traveling to specified locations of interest. However, current solutions require the workers, who in many cases are simply volunteering for a cause, to disclose their locations to untrustworthy entities. In this paper, we introduce a framework for protecting location privacy of workers participating in SC tasks. We argue that existing location privacy techniques are not sufficient for SC, and we propose a mechanism based on differential privacy and geocasting that achieves effective SC services while offering privacy guarantees to workers. We investigate analytical models and task assignment strategies that balance multiple crucial aspects of SC functionality, such as task completion rate, worker travel distance and system overhead. Extensive experimental results on real-world datasets show that the proposed technique protects workers' location privacy without incurring significant performance metrics penalties. With smart devices, particular smartphones, becoming our everyday companions, the ubiquitous mobile Internet and computing applications pervade people daily lives. With the surge demand on high-quality mobile services at anywhere, how to address the ubiquitous user demand and accommodate the explosive growth of mobile traffics is the key issue of the next generation mobile networks. The Fog computing is a promising solution towards this goal. Fog computing extends cloud computing by providing virtualized resources and engaged location-based services to the edge of the mobile networks so as to better serve mobile traffics. Therefore, Fog computing is a lubricant of the combination of cloud computing and mobile applications. In this article, we outline the main features of Fog computing and describe its concept, architecture and design goals. Lastly, we discuss some of the future research issues from the networking perspective. Blind people face a range of accessibility challenges in their everyday lives, from reading the text on a package of food to traveling independently in a new place. Answering general questions about one's visual surroundings remains well beyond the capabilities of fully automated systems, but recent systems are showing the potential of engaging on-demand human workers (the crowd) to answer visual questions. The input to such systems has generally been a single image, which can limit the interaction with a worker to one question; or video streams where systems have paired the end user with a single worker, limiting the benefits of the crowd. In this paper, we introduce Chorus:View, a system that assists users over the course of longer interactions by engaging workers in a continuous conversation with the user about a video stream from the user's mobile device. We demonstrate the benefit of using multiple crowd workers instead of just one in terms of both latency and accuracy, then conduct a study with 10 blind users that shows Chorus:View answers common visual questions more quickly and accurately than existing approaches. We conclude with a discussion of users' feedback and potential future work on interactive crowd support of blind users. In this paper we describe a prototype spatial audio user interface for a Global Positioning System (GPS). The interface is designed to allow mobile users to carry out location tasks while their eyes, hands or attention are otherwise engaged. Audio user interfaces for GPS have typically been designed to meet the needs of visually impaired users, and generally, though not exclusively, employ speech-audio. In contrast, our prototype system uses a simple form of non-speech, spatial audio. This paper analyses various candidate audio mappings for location and distance information. A variety of tasks, design considerations, design trade-offs and opportunities are considered. The findings from pilot empirical testing are reported. Finally, opportunities for improvements to the system and for future evaluation are explored. Location sharing services (LSS) like Foursquare, Gowalla, and Facebook Places support hundreds of millions of user-driven footprints (i.e., "checkins"). Those global-scale footprints provide a unique opportunity to study the social and temporal characteristics of how people use these services and to model patterns of human mobility, which are significant factors for the design of future mobile+location-based services, traffic forecasting, urban planning, as well as epidemiological models of disease spread. In this paper, we investigate 22 million checkins across 220,000 users and report a quantitative assessment of human mobility patterns by analyzing the spatial, temporal, social, and textual aspects associated with these footprints. We find that: (i) LSS users follow the “Levy Flight” mobility pattern and adopt periodic behaviors; (ii) While geographic and economic constraints affect mobility patterns, so does individual social status; and (iii) Content and sentiment-based analysis of posts associated with checkins can provide a rich source of context for better understanding how users engage with these services. Mobile devices are increasingly being relied on for services that go beyond simple connectivity and require more complex processing. Fortunately, a mobile device encounters, possibly intermittently, many entities capable of lending it computational resources. At one extreme is the traditional cloud-computing context where a mobile device is connected to remote cloud resources maintained by a service provider with which it has an established relationship. In this paper we consider the other extreme, where a mobile device's contacts are only with other mobile devices, where both the computation initiator and the remote computational resources are mobile, and where intermittent connectivity among these entities is the norm. We present the design and implementation of a system, Serendipity, that enables a mobile computation initiator to use remote computational resources available in other mobile systems in its environment to speedup computing and conserve energy. We propose a simple but powerful job structure that is suitable for such a system. Serendipity relies on the collaboration among mobile devices for task allocation and task progress monitoring functions. We develop algorithms that are designed to disseminate tasks among mobile devices by accounting for the specific properties of the available connectivity. We also undertake an extensive evaluation of our system, including experience with a prototype, that demonstrates Serendipity's performance. Personal mobile devices are increasingly equipped with the capability to sense the physical world (through cameras, microphones, and accelerometers, for example) and the, network world (with Wi-Fi and Bluetooth interfaces). Such devices offer many new opportunities for cooperative sensing applications. For example, users' mobile phones may contribute data to community-oriented information services, from city-wide pollution monitoring to enterprise-wide detection of unauthorized Wi-Fi access points. This people-centric mobile-sensing model introduces a new security challenge in the design of mobile systems: protecting the privacy of participants while allowing their devices to reliably contribute high-quality data to these large-scale applications. We describe AnonySense, a privacy-aware architecture for realizing pervasive applications based on collaborative, opportunistic sensing by personal mobile devices. AnonySense allows applications to submit sensing tasks that will be distributed across anonymous participating mobile devices, later receiving verified, yet anonymized, sensor data reports back from the field, thus providing the first secure implementation of this participatory sensing model. We describe our trust model, and the security properties that drove the design of the AnonySense system. We evaluate our prototype implementation through experiments that indicate the feasibility of this approach, and through two applications: a Wi-Fi rogue access point detector and a lost-object finder. Visual markers are graphic symbols designed to be easily recognised by machines. They are traditionally used to track goods, but there is increasing interest in their application to mobile HCI. By scanning a visual marker through a camera phone users can retrieve localised information and access mobile services. One missed opportunity in current visual marker systems is that the markers themselves cannot be visually designed, they are not expressive to humans, and thus fail to convey information before being scanned. This paper provides an overview of d-touch, an open source system that allows users to create their own markers, controlling their aesthetic qualities. The system runs in real-time on mobile phones and desktop computers. To increase computational efficiency d-touch imposes constraints on the design of the markers in terms of the relationship of dark and light regions in the symbols. We report a user study in which pairs of novice users generated between 3 and 27 valid and expressive markers within one hour of being introduced to the system, demonstrating its flexibility and ease of use. A mobile device like a smart phone is becoming one of main information processing devices for users these days. Using it, a user not only receives and makes calls, but also performs information tasks. However, a mobile device is still resource constrained, and some applications, especially work related ones, usually demand more resources than a mobile device can afford. To alleviate this, a mobile device should get resources from an external source. One of such sources is cloud computing platforms. Nevertheless an access to these platforms is not always guaranteed to be available and or is too expensive to access them. We envision a way to overcome this issue by creating a virtual cloud computing platform using mobile phones. We argue that due to the pervasiveness of mobile phones and the enhancement in their capabilities this idea is feasible. We show prior evaluation results to support our concept and discuss future developments. In the inaugural issue of MC2R in April 1997 [24], I highlighted the seminal influence of mobility in computing. At that time, the goal of "information at your fingertips anywhere, anytime" was only a dream. Today, through relentless pursuit of innovations in wireless technology, energy-efficient portable hardware and adaptive software, we have largely attained this goal. Ubiquitous email and Web access is a reality that is experienced by millions of users worldwide through their Blackberries, iPhones, iPads, Windows Phone devices, and Android-based devices. Mobile Web-based services and location-aware advertising opportunities have emerged, triggering large commercial investments. Mobile computing has arrived as a lucrative business proposition. Looking ahead, what are the dreams that will inspire our future efforts in mobile computing? We begin this paper by considering some imaginary mobile computing scenarios from the future. We then extract the deep assumptions implicit in these scenarios, and use them to speculate on the future trajectory of mobile computing. Despite the increased capabilities of mobile devices, mobile application resource requirements can often transcend what can be accomplished on a single device. This has been addressed through several proposals for efficient computation offloading from mobile devices to remote cloud resources or closely located computing resources known as cloudlets. In this paper we consider an environment in which computational offloading is performed among a set of mobile devices. We call this environment a Mobile Device Cloud (MDC). We are interested in MDCs where nodes are highly collaborative . We develop computational offloading schemes that maximize the lifetime of the ensemble of mobile devices where we consider the network to be alive as long as no device has depleted its battery. As a secondary contribution in this work, we develop and use an experimentation platform that allows us to evaluate a range of computational models and profiles derived from a realistic testbed. We use this platform as a first step in an evaluation exercise that demonstrates the effectiveness of our computation offloading algorithms in extending the lifetime of an MDC. Twenty-three professional persons were interviewed extensively to find out how they keep their appointment calendars and to extract from that information suggestions about how calendars could be computerized. For the majority of the persons interviewed calendars are indispensable to the conduct of their business, and, in some cases, their personal lives. At the same time the data show an unexpectedly large amount of diversity in the kinds of calendars people use and in the ways they use them. Substantially more than half of the respondents have more than one calendar, with two persons using as many as six calendars at once. Portability and access from diverse locations are important for many persons. Concerns about privacy vary widely: some persons keep their calendars closely guarded, others allow free access to them. Relevant time spans covered by calendars are enormous. Some few people are concerned only with the current day and the day following, others may plan appointments a year or more in advance. A substantial number of appointments are changed after they have been made and, once again, the range is large, from about 2 per cent for some persons to about 80 per cent for others. Archiving, query patterns, and the insertion of correlated information into calendars also vary greatly among various users. Due to its popularity and open-source nature, Android is the mobile platform that has been targeted the most by malware that aim to steal personal information or to control the users’ devices. More specifically, mobile botnets are malware that allow an attacker to remotely control the victims’ devices through different channels like HTTP, thus creating malicious networks of bots. In this paper, we show how it is possible to effectively group mobile botnets families by analyzing the HTTP traffic they generate. To do so, we create malware clusters by looking at specific statistical information that are related to the HTTP traffic. This approach also allows us to extract signatures with which it is possible to precisely detect new malware that belong to the clustered families. Contrarily to x86 malware, we show that using fine-grained HTTP structural features do not increase detection performances. Finally, we point out how the HTTP information flow among mobile bots contains more information when compared to the one generated by desktop ones, allowing for a more precise detection of mobile threats. Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of data on urban systems. The locations, communications, and activities of millions of people are recorded and stored by new information technologies. This work utilizes novel dynamic data, generated by mobile phone users, to measure spatiotemporal changes in population. In the process, we identify the relationship between land use and dynamic population over the course of a typical week. A machine learning classification algorithm is used to identify clusters of locations with similar zoned uses and mobile phone activity patterns. It is shown that the mobile phone data is capable of delivering useful information on actual land use that supplements zoning regulations. Although smartphones are increasingly becoming more and more powerful, enabling pervasiveness is severely hindered by the resource limitations of mobile devices. The combination of social interactions and mobile devices in the form of ‘crowd computing’ has the potential to surpass these limitations. In this paper, we introduce Honeybee; a crowd computing framework for mobile devices. Honeybee enables mobile devices to share work, utilize local resources and human collaboration in the mobile context. It employs ‘work stealing’ to effectively load balance tasks across nodes that are a priori unknown. We describe the design of Honeybee, and report initial experimental data from applications implemented using Honeybee. One of fundamental issues for service robots is human-robot interaction. In order to perform such a task and provide the desired services, these robots need to detect and track people in the surroundings. In this paper, we propose a solution for human tracking with a mobile robot that implements multisensor data fusion techniques. The system utilizes a new algorithm for laser-based leg detection using the onboard laser range finder (LRF). The approach is based on the recognition of typical leg patterns extracted from laser scans, which are shown to also be very discriminative in cluttered environments. These patterns can be used to localize both static and walking persons, even when the robot moves. Furthermore, faces are detected using the robot's camera, and the information is fused to the legs' position using a sequential implementation of unscented Kalman filter. The proposed solution is feasible for service robots with a similar device configuration and has been successfully implemented on two different mobile platforms. Several experiments illustrate the effectiveness of our approach, showing that robust human tracking can be performed within complex indoor environments. Despite increasing usage of mobile computing, exploiting its full potential is difficult due to its inherent problems such as resource scarcity, frequent disconnections, and mobility. Mobile cloud computing can address these problems by executing mobile applications on resource providers external to the mobile device. In this paper, we provide an extensive survey of mobile cloud computing research, while highlighting the specific concerns in mobile cloud computing. We present a taxonomy based on the key issues in this area, and discuss the different approaches taken to tackle these issues. We conclude the paper with a critical analysis of challenges that have not yet been fully met, and highlight directions for future work. Highlights? Problems in mobile computing can be solved when combined with cloud computing. ? Three types of mobile clouds are; offloading to a remote server, to a local cloudlet, and sharing work in a mobile p2p network. ? Existing research on mobile cloud computing is presented as a taxonomy of issues. ? We identify issues in operational, end user, and service levels, and in security and context awareness.