74,622 research outputs found

    Efficient Opportunistic Sensing using Mobile Collaborative Platform MOSDEN

    Get PDF
    Mobile devices are rapidly becoming the primary computing device in people's lives. Application delivery platforms like Google Play, Apple App Store have transformed mobile phones into intelligent computing devices by the means of applications that can be downloaded and installed instantly. Many of these applications take advantage of the plethora of sensors installed on the mobile device to deliver enhanced user experience. The sensors on the smartphone provide the opportunity to develop innovative mobile opportunistic sensing applications in many sectors including healthcare, environmental monitoring and transportation. In this paper, we present a collaborative mobile sensing framework namely Mobile Sensor Data EngiNe (MOSDEN) that can operate on smartphones capturing and sharing sensed data between multiple distributed applications and users. MOSDEN follows a component-based design philosophy promoting reuse for easy and quick opportunistic sensing application deployments. MOSDEN separates the application-specific processing from the sensing, storing and sharing. MOSDEN is scalable and requires minimal development effort from the application developer. We have implemented our framework on Android-based mobile platforms and evaluate its performance to validate the feasibility and efficiency of MOSDEN to operate collaboratively in mobile opportunistic sensing applications. Experimental outcomes and lessons learnt conclude the paper

    MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

    Get PDF
    Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405

    Network strategies for the new economy

    Get PDF
    In this paper we argue that the pace and scale of development in the information and communication technology industries (ICT) has had and continues to have major effects on the industry economics and competitive dynamics generally. We maintain that the size of changes in demand and supply conditions is forcing companies to make significant changes in the way they conceive and implement their strategies. We decompose the ICT industries into four levels, technology standards, supply chains, physical platforms, and consumer networks. The nature of these technologies and their cost characteristics coupled with higher degrees of knowledge specialisation is impelling companies to radical revisions of their attitudes towards cooperation and co-evolution with suppliers and customers. Where interdependencies between customers are particularly strong, we anticipate the possibility of winner-takes-all strategies. In these circumstances industry risks become very high and there will be significant consequences for competitive markets

    Genetic Programming for Smart Phone Personalisation

    Full text link
    Personalisation in smart phones requires adaptability to dynamic context based on user mobility, application usage and sensor inputs. Current personalisation approaches, which rely on static logic that is developed a priori, do not provide sufficient adaptability to dynamic and unexpected context. This paper proposes genetic programming (GP), which can evolve program logic in realtime, as an online learning method to deal with the highly dynamic context in smart phone personalisation. We introduce the concept of collaborative smart phone personalisation through the GP Island Model, in order to exploit shared context among co-located phone users and reduce convergence time. We implement these concepts on real smartphones to demonstrate the capability of personalisation through GP and to explore the benefits of the Island Model. Our empirical evaluations on two example applications confirm that the Island Model can reduce convergence time by up to two-thirds over standalone GP personalisation.Comment: 43 pages, 11 figure

    Recent mobile telecommunications alliance formation

    Get PDF
    During the year to end-January 2005, the resurgence of takeover activity in the mobile telecommunications industry 1 has attracted media attention. However, by focusing on takeovers, the willingness of companies in the sector to collaborate through alliance and joint venture formation is in danger of being overlooked. These alliances, none of which are more than two years old, can be variously interpreted. They could signify a return to expansionary behaviour by operators motivated by the desire to capture lucrative roaming traffic or retain key customers. Alternatively the alliances may be motivated by the desire to compete more effectively with Vodafone, which is arguably the only mobile operator with a global footprint. This paper is structured as follows. In the initial section, the six alliances that have been formed are described. Particular attention is paid to the membership and resulting scale of these alliances, as well as to the motives for their formation. The first sub-section focuses on those alliances that are largely scale orientated in motivation, while the second concentrates on those that are more technologically orientated. These alliances are then discussed in detail and conclusions are drawn

    Towards a cloud‑based automated surveillance system using wireless technologies

    Get PDF
    Cloud Computing can bring multiple benefits for Smart Cities. It permits the easy creation of centralized knowledge bases, thus straightforwardly enabling that multiple embedded systems (such as sensor or control devices) can have a collaborative, shared intelligence. In addition to this, thanks to its vast computing power, complex tasks can be done over low-spec devices just by offloading computation to the cloud, with the additional advantage of saving energy. In this work, cloud’s capabilities are exploited to implement and test a cloud-based surveillance system. Using a shared, 3D symbolic world model, different devices have a complete knowledge of all the elements, people and intruders in a certain open area or inside a building. The implementation of a volumetric, 3D, object-oriented, cloud-based world model (including semantic information) is novel as far as we know. Very simple devices (orange Pi) can send RGBD streams (using kinect cameras) to the cloud, where all the processing is distributed and done thanks to its inherent scalability. A proof-of-concept experiment is done in this paper in a testing lab with multiple cameras connected to the cloud with 802.11ac wireless technology. Our results show that this kind of surveillance system is possible currently, and that trends indicate that it can be improved at a short term to produce high performance vigilance system using low-speed devices. In addition, this proof-of-concept claims that many interesting opportunities and challenges arise, for example, when mobile watch robots and fixed cameras would act as a team for carrying out complex collaborative surveillance strategies.Ministerio de Economía y Competitividad TEC2016-77785-PJunta de Andalucía P12-TIC-130
    • …
    corecore